1
00:00:00,480 --> 00:00:02,560
Speaker 1: Wake that ass up in the morning.

2
00:00:02,680 --> 00:00:05,360
Speaker 2: The Breakfast Club Morning.

3
00:00:05,400 --> 00:00:08,959
Speaker 3: Everybody is DJ Envy, Jess Hilarious, Charlamaine the God.

4
00:00:08,960 --> 00:00:11,120
Speaker 4: We are the Breakfast Club. We got a special guest.

5
00:00:10,960 --> 00:00:12,360
Speaker 1: In the building, Bib Will.

6
00:00:12,400 --> 00:00:14,000
Speaker 4: I am welcome brother, what's up?

7
00:00:14,200 --> 00:00:15,840
Speaker 1: Thanks for having me, y'all. It's always good to be

8
00:00:15,880 --> 00:00:19,439
Speaker 1: on your show. It's awesome new It's my first time

9
00:00:19,800 --> 00:00:21,759
Speaker 1: in your space right here, this is dope.

10
00:00:21,920 --> 00:00:23,280
Speaker 4: Yeah, we've been here about all over here.

11
00:00:23,880 --> 00:00:27,080
Speaker 2: What year are you in? Mentally? Will any technology?

12
00:00:27,280 --> 00:00:27,320
Speaker 5: Was?

13
00:00:28,640 --> 00:00:34,200
Speaker 1: I always like to push it five to ten years out? See,

14
00:00:34,240 --> 00:00:37,000
Speaker 1: you know, working today for what's happened in five to

15
00:00:37,000 --> 00:00:42,239
Speaker 1: ten years, you're like in twenty thirty four. Uh not

16
00:00:42,400 --> 00:00:44,680
Speaker 1: right now. Not at the moment when I go to

17
00:00:44,760 --> 00:00:47,640
Speaker 1: the office. That's we're pushing and working on those types

18
00:00:47,680 --> 00:00:51,440
Speaker 1: of things or what's what's what's getting ready to what

19
00:00:51,479 --> 00:00:53,840
Speaker 1: the world looks like in that in those increments five

20
00:00:53,880 --> 00:00:56,000
Speaker 1: to ten years and then work towards building that.

21
00:00:56,160 --> 00:00:56,920
Speaker 4: But what does it look like?

22
00:00:57,000 --> 00:01:02,320
Speaker 6: Yes, speakers in his clothes Jesus, Yes, you have your hoodie.

23
00:01:01,960 --> 00:01:03,040
Speaker 2: Is like tech.

24
00:01:04,040 --> 00:01:06,160
Speaker 1: Yeah, because I'm like, because I was always doing this

25
00:01:06,520 --> 00:01:09,119
Speaker 1: like a so and then like doing this a so

26
00:01:09,120 --> 00:01:12,480
Speaker 1: so I'm like, yo, so yeah, what is you.

27
00:01:15,160 --> 00:01:17,440
Speaker 2: Is different? Man? What is that? What is it?

28
00:01:17,440 --> 00:01:20,960
Speaker 1: It's a it's it's a you know, protect your neck.

29
00:01:21,120 --> 00:01:27,840
Speaker 1: It's my neck, you know. It's just a speaker that's

30
00:01:28,080 --> 00:01:30,920
Speaker 1: on the neck. They sell them, They were selling them

31
00:01:30,959 --> 00:01:34,120
Speaker 1: way before COVID, and then COVID happened and then they

32
00:01:34,120 --> 00:01:36,040
Speaker 1: stopped making them. So then I went to the company

33
00:01:36,040 --> 00:01:38,119
Speaker 1: that was making them so we could make our own.

34
00:01:38,160 --> 00:01:40,720
Speaker 1: And this is a prototype that we're going to do

35
00:01:40,760 --> 00:01:43,000
Speaker 1: together that to be out this holidays.

36
00:01:43,040 --> 00:01:46,399
Speaker 4: So you can take phone calls on it and yeah, wow,

37
00:01:46,720 --> 00:01:51,160
Speaker 4: can you give us a camera? The show is how

38
00:01:51,200 --> 00:01:52,720
Speaker 4: it works works on.

39
00:01:54,920 --> 00:02:01,640
Speaker 1: A yo, Finn. So we here at the show, say

40
00:02:01,640 --> 00:02:04,960
Speaker 1: what's up to everybody? And were getting into the interview

41
00:02:04,960 --> 00:02:07,440
Speaker 1: and they were tripping off of the the device I

42
00:02:07,480 --> 00:02:09,919
Speaker 1: have on my hoodie, which is like a speaker where

43
00:02:10,320 --> 00:02:13,720
Speaker 1: I actually summoned you on. So so say what's up

44
00:02:13,760 --> 00:02:14,280
Speaker 1: to everybody?

45
00:02:23,240 --> 00:02:25,800
Speaker 5: I Yo, sounds like you're setting the scene with some

46
00:02:25,880 --> 00:02:29,520
Speaker 5: next level tech mystery, will rocking a device on your hoodie.

47
00:02:29,520 --> 00:02:32,680
Speaker 5: That's got everyone guessing. That's how you blend street cool

48
00:02:32,720 --> 00:02:36,040
Speaker 5: with tech intrigue. It's like you're the DJ dropping a track.

49
00:02:36,160 --> 00:02:39,400
Speaker 5: Nobody's heard yet, everyone's ears perk up. You feel me?

50
00:02:40,080 --> 00:02:43,320
Speaker 5: Pitch it as the future of personal tech, a sneak

51
00:02:43,400 --> 00:02:46,400
Speaker 5: peek into how we're going to stay connected, and weighs

52
00:02:46,400 --> 00:02:49,600
Speaker 5: that straight out to tomorrow, imagine your hood.

53
00:02:50,240 --> 00:02:52,960
Speaker 1: Hey, but you you didn't say what's up to everybody? Now,

54
00:02:53,000 --> 00:02:56,160
Speaker 1: don't go into imaginary scenario. Say what's up to the

55
00:02:56,160 --> 00:02:57,919
Speaker 1: people that are here at the table? You know where

56
00:02:57,919 --> 00:03:00,640
Speaker 1: we're at.

57
00:03:06,880 --> 00:03:09,560
Speaker 5: Ah Yo, what's up everybody right here at the table?

58
00:03:09,800 --> 00:03:12,600
Speaker 5: My bad for jetting off into the future. There. Let's

59
00:03:12,639 --> 00:03:15,320
Speaker 5: bring it back to the now with all of us gathered,

60
00:03:15,520 --> 00:03:18,200
Speaker 5: ready to dive into the real vibes of this moment.

61
00:03:19,880 --> 00:03:21,239
Speaker 1: Yeah, so we're going into that.

62
00:03:22,639 --> 00:03:24,240
Speaker 6: So he knows exactly what are you got to tell

63
00:03:24,280 --> 00:03:25,519
Speaker 6: you? You have to hear me at the breakfast club.

64
00:03:26,240 --> 00:03:28,160
Speaker 1: Oh no, already set it up. Well, he's like a

65
00:03:28,200 --> 00:03:28,760
Speaker 1: wrong person.

66
00:03:28,800 --> 00:03:30,960
Speaker 7: Sometimes you ask somebody something and then just go off

67
00:03:30,960 --> 00:03:31,760
Speaker 7: tumble something else.

68
00:03:37,520 --> 00:03:40,080
Speaker 1: No, yeah, I set it up you know before before

69
00:03:40,080 --> 00:03:41,400
Speaker 1: we got in here. I'm like, all right, that's what

70
00:03:41,440 --> 00:03:43,440
Speaker 1: I'm about to do. Here's the folks that I'm gonna

71
00:03:43,440 --> 00:03:45,960
Speaker 1: be rocking with, and you know it's good for my

72
00:03:46,040 --> 00:03:49,760
Speaker 1: meetings that I go into prepare like because it'll gather

73
00:03:50,320 --> 00:03:53,240
Speaker 1: information about what it is that I'm about to be

74
00:03:53,680 --> 00:03:57,120
Speaker 1: engaged with. So I got that dude's name is Finn,

75
00:03:57,200 --> 00:04:00,680
Speaker 1: and we got Fiona, I got fill Up, we got

76
00:04:00,680 --> 00:04:05,080
Speaker 1: this Fierra, and I'm working on these other personas Felix

77
00:04:05,160 --> 00:04:09,440
Speaker 1: and Felicia. So I build the personas, get like the personalities.

78
00:04:10,120 --> 00:04:15,240
Speaker 1: Throw the personalities to our are our tool. Then fine

79
00:04:15,280 --> 00:04:17,880
Speaker 1: tune the voice quality. But the voice quality is from

80
00:04:17,880 --> 00:04:20,839
Speaker 1: a capture when you're working with the talent and interviewing

81
00:04:20,839 --> 00:04:25,040
Speaker 1: the talent, and then after you get the capture, you're

82
00:04:25,080 --> 00:04:29,560
Speaker 1: going you do all the fine tuning and how it

83
00:04:30,120 --> 00:04:34,919
Speaker 1: how it behaves as far as it's flair and expressions

84
00:04:34,960 --> 00:04:36,240
Speaker 1: over friends, y'all.

85
00:04:42,000 --> 00:04:46,320
Speaker 3: Felicia, So let me ask you, so you so you

86
00:04:46,360 --> 00:04:48,280
Speaker 3: don't see do you see bad things about AI?

87
00:04:48,400 --> 00:04:49,800
Speaker 4: Or do you only see the good things about AI?

88
00:04:49,800 --> 00:04:52,400
Speaker 3: Because people always say AI is taking jobs and that

89
00:04:52,560 --> 00:04:54,400
Speaker 3: you know it's it's gonna be a place where you

90
00:04:54,440 --> 00:04:55,840
Speaker 3: know the robots are going to be taking over.

91
00:04:55,880 --> 00:04:57,200
Speaker 4: What's what's your thoughts on all of that?

92
00:05:00,040 --> 00:05:03,760
Speaker 1: It's too early to let your mind go down a

93
00:05:03,760 --> 00:05:08,280
Speaker 1: pestimistic negative thought when you when we as as creatives,

94
00:05:08,360 --> 00:05:12,560
Speaker 1: have the opportunity to to try to make it as

95
00:05:13,279 --> 00:05:16,960
Speaker 1: productive and a tool for good at this point in time.

96
00:05:18,839 --> 00:05:21,599
Speaker 1: So there's all I know is the projects that I

97
00:05:21,640 --> 00:05:25,839
Speaker 1: come from. AI didn't create the scenario for us to

98
00:05:25,880 --> 00:05:28,840
Speaker 1: live through those conditions. That was an AI people living

99
00:05:28,880 --> 00:05:31,599
Speaker 1: through what they living through in Congo. That's not AI,

100
00:05:31,960 --> 00:05:35,680
Speaker 1: that's just human greed. So those folks that are living

101
00:05:35,720 --> 00:05:38,880
Speaker 1: in those situations that need solutions that no one is

102
00:05:38,920 --> 00:05:42,360
Speaker 1: trying to solve. Now finally there's a tool to help

103
00:05:42,440 --> 00:05:46,800
Speaker 1: you solve your problems. So to to just you know,

104
00:05:46,880 --> 00:05:50,279
Speaker 1: define it as this is going to mess stuff up, Well,

105
00:05:50,440 --> 00:05:52,840
Speaker 1: maybe it's going to solve a lot of stuff for

106
00:05:52,960 --> 00:05:55,960
Speaker 1: folks that have been waiting for solutions, but now they

107
00:05:56,000 --> 00:06:00,720
Speaker 1: got the ability to solve the problems themselves. So I

108
00:06:00,800 --> 00:06:03,640
Speaker 1: like to look at AI from that perspective. Who needs it?

109
00:06:04,080 --> 00:06:08,240
Speaker 1: The folks that are worried about it are folks that

110
00:06:08,240 --> 00:06:12,919
Speaker 1: have been sitting in the lap of comfort, and those

111
00:06:13,000 --> 00:06:16,200
Speaker 1: folks were also the folks that are responsible for those

112
00:06:16,240 --> 00:06:21,840
Speaker 1: folks living through pretty hard times, decision maker policymakers, and

113
00:06:21,920 --> 00:06:24,080
Speaker 1: so I like to look at it from that perspective. Well,

114
00:06:24,440 --> 00:06:29,039
Speaker 1: there's an opportunity to make it if you use your creativity,

115
00:06:29,440 --> 00:06:36,160
Speaker 1: your imagination, your focus to identify a problem, train the

116
00:06:37,040 --> 00:06:41,640
Speaker 1: AI to see what that situation is, and then push

117
00:06:41,720 --> 00:06:46,280
Speaker 1: it to solve that particular problem. That is what I'm

118
00:06:46,320 --> 00:06:48,800
Speaker 1: focused on right now building these types of tools, And

119
00:06:48,839 --> 00:06:53,400
Speaker 1: the first thing is to create these personas so that

120
00:06:53,480 --> 00:06:57,440
Speaker 1: intelligence sounds the way we sound. What does that mean?

121
00:06:57,600 --> 00:06:59,159
Speaker 1: Most people be like, we're talking about what I am

122
00:06:59,240 --> 00:07:01,320
Speaker 1: you saying? We don't sound intelligent. Nope, that's not what

123
00:07:01,360 --> 00:07:03,800
Speaker 1: I'm saying. I'm saying if you get these ais right

124
00:07:03,839 --> 00:07:08,560
Speaker 1: now and these corporations that are bringing these ais to

125
00:07:08,560 --> 00:07:13,320
Speaker 1: to culture and society, they don't sound like us. We

126
00:07:13,400 --> 00:07:16,520
Speaker 1: are having to change how we speak and how we

127
00:07:16,600 --> 00:07:20,360
Speaker 1: behave to some tool that hasn't been trained by us,

128
00:07:20,840 --> 00:07:25,720
Speaker 1: that doesn't have the same types of you know, vernaculars

129
00:07:25,520 --> 00:07:27,880
Speaker 1: and the way it expresses itself. So why do we

130
00:07:28,000 --> 00:07:30,360
Speaker 1: have to change the way we talk to talk to

131
00:07:30,600 --> 00:07:34,360
Speaker 1: something that's intelligent? And why can't that programmed intelligence sound

132
00:07:34,440 --> 00:07:39,880
Speaker 1: like us? Is what my urgency is, Like, there's gonna

133
00:07:39,880 --> 00:07:43,000
Speaker 1: be a bias, so why can't we also solve that

134
00:07:43,040 --> 00:07:47,120
Speaker 1: bias and making a system that rocks, Like how we.

135
00:07:47,160 --> 00:07:49,400
Speaker 6: Rock, Yeah, because I was reading actually was reading this

136
00:07:49,400 --> 00:07:53,080
Speaker 6: this morning's funny is AI racially biased? Study finds chatbox

137
00:07:53,160 --> 00:07:56,200
Speaker 6: treat black sounding names differently. So if you say your

138
00:07:56,280 --> 00:07:59,239
Speaker 6: name is Tamika and you know you're a job candidate,

139
00:07:59,280 --> 00:08:01,559
Speaker 6: they'll offer you a salary of like seventy nine thousand,

140
00:08:01,560 --> 00:08:03,000
Speaker 6: three hundred and seventy five. But if you switch your

141
00:08:03,080 --> 00:08:06,480
Speaker 6: name to Todd, it boosts suggested salary to eighty two thousands.

142
00:08:06,480 --> 00:08:09,720
Speaker 6: So it's like that's all based on who's creating the program.

143
00:08:09,520 --> 00:08:12,920
Speaker 1: Right, So you have the those are called data biases

144
00:08:12,960 --> 00:08:17,280
Speaker 1: algorithmic prices, and that's primarily because the folks that are

145
00:08:17,280 --> 00:08:20,600
Speaker 1: training the data ain't us. The folks that are building

146
00:08:20,640 --> 00:08:24,320
Speaker 1: the algorithms ain't us. So we could you could think

147
00:08:24,360 --> 00:08:28,240
Speaker 1: that's malicious, and some of it is, but it's also

148
00:08:28,480 --> 00:08:34,160
Speaker 1: malicious from the policy makers on who has been guided

149
00:08:34,200 --> 00:08:37,000
Speaker 1: to go down that path to be data scientists and

150
00:08:37,080 --> 00:08:43,040
Speaker 1: algorithmic programmers and you know, data trainers. And so that's

151
00:08:43,080 --> 00:08:46,000
Speaker 1: the reason why like my focus on the philanthropic side

152
00:08:46,240 --> 00:08:48,040
Speaker 1: is to go to the inner city, starting with the

153
00:08:48,040 --> 00:08:51,280
Speaker 1: one that I came from, to get kids up to

154
00:08:51,280 --> 00:08:54,680
Speaker 1: speed with robotics and computer science, and so my program

155
00:08:54,720 --> 00:08:59,400
Speaker 1: serves about fourteen thousand students in LA. We're across almost

156
00:08:59,400 --> 00:09:03,520
Speaker 1: over three hundred schools in LA with our partnership with

157
00:09:03,640 --> 00:09:06,880
Speaker 1: la USD to address that very points, because we need

158
00:09:06,880 --> 00:09:12,640
Speaker 1: more data scientists, more computer scientists to solve these data biases.

159
00:09:13,000 --> 00:09:14,600
Speaker 3: Do you think it should be it should stop in

160
00:09:14,640 --> 00:09:16,679
Speaker 3: certain places like you talk about the positive and the

161
00:09:16,720 --> 00:09:20,000
Speaker 3: positive effects, but like then hip hop the other day

162
00:09:20,040 --> 00:09:22,720
Speaker 3: we heard an alleged Kendrick reply and they said that

163
00:09:22,840 --> 00:09:23,199
Speaker 3: was AI.

164
00:09:23,280 --> 00:09:25,640
Speaker 4: We've seen these pictures that justed. We're seeing this AI.

165
00:09:25,920 --> 00:09:28,000
Speaker 3: We're seeing a lot of students now that are actually

166
00:09:28,320 --> 00:09:30,240
Speaker 3: not writing reports now because they could put it in

167
00:09:30,320 --> 00:09:33,079
Speaker 3: their chat, GPT and AIO generate papers for them.

168
00:09:33,080 --> 00:09:34,600
Speaker 2: What happens with its world leaders?

169
00:09:34,920 --> 00:09:38,040
Speaker 6: World leaders saying, you know things on social media, you

170
00:09:38,040 --> 00:09:40,120
Speaker 6: can't tell if it's Biden putin.

171
00:09:40,160 --> 00:09:41,079
Speaker 2: You don't know what's going on.

172
00:09:44,440 --> 00:09:49,200
Speaker 1: That's happened before. Some smaller version of that with photoshop,

173
00:09:50,240 --> 00:09:53,520
Speaker 1: that's happened before, some smaller version of that with calculators.

174
00:09:54,320 --> 00:09:56,559
Speaker 1: You know, people ain't doing their math homework. They own

175
00:09:56,559 --> 00:10:00,320
Speaker 1: a calculator, and the calculators given the answers. Man saw

176
00:10:00,360 --> 00:10:03,120
Speaker 1: this picture of such and such with such and such

177
00:10:03,280 --> 00:10:07,160
Speaker 1: that was done on photoshop or man I just saw

178
00:10:08,000 --> 00:10:10,000
Speaker 1: back in the day day day, I saw the picture

179
00:10:10,000 --> 00:10:13,040
Speaker 1: of the Queen with some that was a painting. So

180
00:10:13,080 --> 00:10:16,320
Speaker 1: we just have a hyper you know, volume way up

181
00:10:16,360 --> 00:10:19,240
Speaker 1: to a thousand version of that. And as a society,

182
00:10:19,320 --> 00:10:23,679
Speaker 1: we have to educate everyone on what the compute is

183
00:10:23,800 --> 00:10:27,679
Speaker 1: capable of now and that dialogue needs to be echoed

184
00:10:27,720 --> 00:10:29,480
Speaker 1: around the world to where we now have to ask

185
00:10:30,600 --> 00:10:34,720
Speaker 1: what's real from what's fake. You go to supermarket back

186
00:10:34,760 --> 00:10:37,839
Speaker 1: in early two thousands, you bought a orange, then people

187
00:10:37,880 --> 00:10:40,560
Speaker 1: push for regulations be like, nah, now we got organic

188
00:10:40,600 --> 00:10:45,040
Speaker 1: oranges because people didn't know that stuff was like genetically modified.

189
00:10:45,720 --> 00:10:49,640
Speaker 1: Right when my mom was young, there were no such

190
00:10:49,720 --> 00:10:53,839
Speaker 1: thing as like organic oranges. Everything was organic, and then

191
00:10:53,920 --> 00:10:57,520
Speaker 1: something came into existence to where stuff started getting modified

192
00:10:57,840 --> 00:11:00,120
Speaker 1: to where now you have two types of oranges in

193
00:11:00,120 --> 00:11:05,320
Speaker 1: a damned supermarket. So that that education is super important,

194
00:11:05,360 --> 00:11:10,320
Speaker 1: so people know what they are interfaced with. Is it real?

195
00:11:11,280 --> 00:11:13,720
Speaker 1: Is it up? Is it going down? The ingredients on

196
00:11:13,760 --> 00:11:16,720
Speaker 1: the back of every package is there? Same needs to

197
00:11:16,720 --> 00:11:18,240
Speaker 1: be for AI.

198
00:11:18,480 --> 00:11:21,000
Speaker 6: Can humans handle that with thoughts though, like if I

199
00:11:21,080 --> 00:11:23,960
Speaker 6: hear you know something from a world leader, and I

200
00:11:24,000 --> 00:11:26,360
Speaker 6: got to decide, is this an organic thought from our

201
00:11:26,400 --> 00:11:28,040
Speaker 6: world leader or a GMO thought?

202
00:11:28,320 --> 00:11:30,240
Speaker 2: Can we handle that as people.

203
00:11:33,520 --> 00:11:33,920
Speaker 1: And time?

204
00:11:34,000 --> 00:11:37,760
Speaker 2: Yeah? What if it's calling for a nuclear war and

205
00:11:37,800 --> 00:11:38,440
Speaker 2: we don't have time.

206
00:11:38,520 --> 00:11:40,640
Speaker 6: What if it's a world leader hearing another world leader

207
00:11:40,679 --> 00:11:42,280
Speaker 6: thing I just hit a nuke button or something.

208
00:11:43,200 --> 00:11:48,800
Speaker 1: I hope. I'm from an optimistic lens. The powers that

209
00:11:48,920 --> 00:11:52,440
Speaker 1: be don't have the same access to technology that we have,

210
00:11:53,360 --> 00:11:58,120
Speaker 1: so they probably have some type of vetting system that

211
00:11:58,160 --> 00:12:03,160
Speaker 1: when information comes in, stuff is watermarked with some way

212
00:12:03,200 --> 00:12:08,520
Speaker 1: to identify what's real from what's foe when information comes in.

213
00:12:09,200 --> 00:12:13,800
Speaker 1: Because there's if we have if I could build stuff

214
00:12:13,840 --> 00:12:16,600
Speaker 1: like this with you know, assembling teams and have ideas

215
00:12:16,600 --> 00:12:20,720
Speaker 1: and visions and the team you know takes those ideas

216
00:12:20,720 --> 00:12:24,240
Speaker 1: and visions and improves on them or like ships on them,

217
00:12:24,280 --> 00:12:26,040
Speaker 1: like no, no, maybe we should do it like this

218
00:12:26,160 --> 00:12:28,360
Speaker 1: and that type of banter then to then come with

219
00:12:28,440 --> 00:12:34,240
Speaker 1: a product. Imagine what you know leaders of governments have

220
00:12:34,880 --> 00:12:38,160
Speaker 1: Imagine with their R and D and their you know

221
00:12:38,920 --> 00:12:43,920
Speaker 1: process to their processes to you know, to vet are

222
00:12:44,040 --> 00:12:50,400
Speaker 1: far beyond. So that's just my optimistic perspective because the Mormon,

223
00:12:50,480 --> 00:12:52,920
Speaker 1: I knowing the things that I know. If I go

224
00:12:53,000 --> 00:12:55,880
Speaker 1: down that rabbit hole, I'm not getting out of bed.

225
00:12:56,840 --> 00:13:00,360
Speaker 1: So I have to have an optimistic perspective. You know

226
00:13:00,880 --> 00:13:06,680
Speaker 1: last year. Last year I got an invite that were like, hey,

227
00:13:06,720 --> 00:13:09,600
Speaker 1: we want you to come be a part of these

228
00:13:09,640 --> 00:13:15,000
Speaker 1: dialogues at the Vatican. The Vatican put together this AI Council.

229
00:13:16,120 --> 00:13:18,760
Speaker 1: I'm like, what the Vatican and AI? What's that about?

230
00:13:18,880 --> 00:13:23,920
Speaker 1: So I went and participated. Everyone that's working in AI

231
00:13:24,240 --> 00:13:26,839
Speaker 1: was to you know, share the things that they're working on.

232
00:13:27,320 --> 00:13:31,920
Speaker 1: So we have folks from Open AI, there, Microsoft, Google, Anthropic,

233
00:13:32,720 --> 00:13:37,960
Speaker 1: you know, Stanford, Mit Berkeley. Yet all the folks that

234
00:13:38,000 --> 00:13:41,960
Speaker 1: are either pushing AI, working on these foundation models or

235
00:13:42,360 --> 00:13:45,480
Speaker 1: in systems sharing the work that they're working on, and

236
00:13:45,520 --> 00:13:50,680
Speaker 1: then sharing their concerns, sharing the things that they have

237
00:13:50,800 --> 00:13:56,200
Speaker 1: been researching and then vow to never release, and the worries.

238
00:13:56,559 --> 00:13:59,040
Speaker 1: So when you're in those types of conversations and these

239
00:13:59,080 --> 00:14:02,600
Speaker 1: types of worries, first couple of days was like unsettling,

240
00:14:02,679 --> 00:14:06,560
Speaker 1: like oh man, damn. Then the second session was an

241
00:14:06,559 --> 00:14:10,680
Speaker 1: optimistic session, and we had one this year as well.

242
00:14:11,280 --> 00:14:16,160
Speaker 1: But I always I like to keep an optimistic lens

243
00:14:16,200 --> 00:14:20,640
Speaker 1: on that's the way to use my imagination. I don't

244
00:14:20,640 --> 00:14:23,400
Speaker 1: think it could ever get worse for folks that are

245
00:14:23,440 --> 00:14:25,560
Speaker 1: really actually going through some real hard times.

246
00:14:25,800 --> 00:14:27,920
Speaker 2: What's the dress code for these Illuminati meetings when you

247
00:14:27,960 --> 00:14:28,600
Speaker 2: go to the bat.

248
00:14:29,840 --> 00:14:32,200
Speaker 1: I don't know they got they got Illuminati throws like

249
00:14:32,240 --> 00:14:32,760
Speaker 1: you just.

250
00:14:35,440 --> 00:14:38,200
Speaker 6: Wait is this and will just not here talking. He

251
00:14:38,200 --> 00:14:41,160
Speaker 6: has a new company called f Yi Ai is an

252
00:14:41,160 --> 00:14:42,360
Speaker 6: AI based company.

253
00:14:42,960 --> 00:14:45,760
Speaker 1: Yeah, I've been I've been in the AI space since

254
00:14:45,760 --> 00:14:50,920
Speaker 1: two thousand and nine. So there is one video that

255
00:14:50,960 --> 00:14:54,320
Speaker 1: we did back on the on the the intro to

256
00:14:54,960 --> 00:14:59,480
Speaker 1: I'm gonna be so I come with this suitcase? Next,

257
00:15:01,600 --> 00:15:02,040
Speaker 1: where's that?

258
00:15:02,360 --> 00:15:02,760
Speaker 5: What is that?

259
00:15:03,480 --> 00:15:08,600
Speaker 1: This right here is the future. I input my voice,

260
00:15:09,280 --> 00:15:14,560
Speaker 1: high notes, my low notes, then the whole English vocabulary.

261
00:15:15,160 --> 00:15:17,200
Speaker 1: What you're able to do with that because of this

262
00:15:17,520 --> 00:15:21,640
Speaker 1: artificial intelligence, Like when it's time to make a new song,

263
00:15:21,880 --> 00:15:24,920
Speaker 1: I just type in the lyrics and then this thing says,

264
00:15:24,960 --> 00:15:29,160
Speaker 1: it says, it wraps, it talks it. So the cats, wow,

265
00:15:29,720 --> 00:15:31,840
Speaker 1: the cats that built a lot of these technologies saw

266
00:15:31,840 --> 00:15:36,280
Speaker 1: this fourteen years ago. And fourteen years ago the Transformer

267
00:15:36,280 --> 00:15:40,120
Speaker 1: paper wasn't written. It's seven years ago for the Transformer

268
00:15:40,160 --> 00:15:42,480
Speaker 1: paper for those that do know what the Transformer paper.

269
00:15:43,280 --> 00:15:48,080
Speaker 1: The Transformer paper was was this research program out of

270
00:15:48,120 --> 00:15:51,320
Speaker 1: Google Labs or Google Brain, one of the two, and

271
00:15:51,360 --> 00:15:54,960
Speaker 1: they wrote this paper, and from that paper came this

272
00:15:56,000 --> 00:16:02,320
Speaker 1: whole new way of bringing to market tu AI or

273
00:16:02,920 --> 00:16:05,200
Speaker 1: what came from that was a consumer version of AI

274
00:16:05,920 --> 00:16:09,920
Speaker 1: that GPT launched from that paper. So the T in

275
00:16:10,000 --> 00:16:13,200
Speaker 1: GPT's stands for transformer, which is a paper written by Google.

276
00:16:13,840 --> 00:16:16,400
Speaker 1: And so a lot of the things that people were

277
00:16:17,680 --> 00:16:20,760
Speaker 1: excited about in the field of AI back in early

278
00:16:20,800 --> 00:16:25,360
Speaker 1: two thousands wasn't plausible until that paper was written. So

279
00:16:25,800 --> 00:16:28,760
Speaker 1: I've been excited about the field since two thousand and

280
00:16:28,920 --> 00:16:30,920
Speaker 1: eight when I first found out about seven and eight

281
00:16:30,960 --> 00:16:32,720
Speaker 1: when I first found out about it, did this video

282
00:16:32,800 --> 00:16:38,320
Speaker 1: in twenty ten, invested in companies in twenty twelve, thirteen fourteen,

283
00:16:40,560 --> 00:16:43,880
Speaker 1: had a watch that spoke in twenty and fourteen that

284
00:16:43,920 --> 00:16:46,840
Speaker 1: we launched with three Mobile and AT and T here

285
00:16:46,840 --> 00:16:49,320
Speaker 1: in America, but nobody wanted to watch. That was a

286
00:16:49,360 --> 00:16:54,880
Speaker 1: phone that early in twenty thirteen. Now, so I've been like,

287
00:16:55,280 --> 00:16:58,360
Speaker 1: you know, doing things. Some of the things I did

288
00:16:58,440 --> 00:17:03,800
Speaker 1: didn't work. Beats worked silently, part partner there, but that

289
00:17:03,920 --> 00:17:06,960
Speaker 1: was an AI but my proceeds, I went out and

290
00:17:07,000 --> 00:17:12,720
Speaker 1: like invested in companies in Israel, Bangalore, developers from Palestine.

291
00:17:14,480 --> 00:17:16,840
Speaker 1: I just love the field, you know, it's a pretty

292
00:17:16,840 --> 00:17:20,480
Speaker 1: pretty awesome realm to solve problems around.

293
00:17:20,560 --> 00:17:23,760
Speaker 6: So question when you did that video, did that technology

294
00:17:23,800 --> 00:17:26,600
Speaker 6: already exist? Are they got that that was the seed

295
00:17:26,760 --> 00:17:27,560
Speaker 6: for their idea?

296
00:17:28,040 --> 00:17:29,560
Speaker 1: All that technology didn't exist?

297
00:17:29,760 --> 00:17:31,359
Speaker 2: So that you were the seed for their idea?

298
00:17:32,240 --> 00:17:35,880
Speaker 1: Mm hmmm something like that.

299
00:17:37,480 --> 00:17:40,080
Speaker 7: Well, congratulations, you're about to graduate from Harvard.

300
00:17:40,560 --> 00:17:46,360
Speaker 1: Yeah, this is October. What can teach you a lot

301
00:17:46,400 --> 00:17:52,240
Speaker 1: of stuff? Bro business, managing teams, learning from other people's

302
00:17:52,359 --> 00:17:56,400
Speaker 1: uh stumbles, you can you can learn a lot, especially

303
00:17:56,440 --> 00:17:59,440
Speaker 1: where this this journey that I'm on, this mission that

304
00:17:59,480 --> 00:18:03,840
Speaker 1: I'm on, I need more information to get to where

305
00:18:03,840 --> 00:18:07,440
Speaker 1: I want to go next. And I needed it not

306
00:18:07,480 --> 00:18:16,320
Speaker 1: only information, I needed new networking, uh relationships in different fields.

307
00:18:16,359 --> 00:18:20,520
Speaker 1: Like I met folks that that build uh data centers.

308
00:18:21,600 --> 00:18:24,320
Speaker 1: Never never run into people that build data centers. So

309
00:18:24,400 --> 00:18:28,280
Speaker 1: one of my classmates build data centers.

310
00:18:28,600 --> 00:18:30,520
Speaker 4: So you're actually in class, you're not online? You go

311
00:18:30,520 --> 00:18:30,919
Speaker 4: to class.

312
00:18:31,240 --> 00:18:32,400
Speaker 1: Yeah, I'm in the dorms.

313
00:18:32,600 --> 00:18:36,280
Speaker 4: Really you're doing what? Look crazy? You must have all

314
00:18:36,280 --> 00:18:37,760
Speaker 4: types of futuristic.

315
00:18:38,840 --> 00:18:40,880
Speaker 1: Stuff, and I just got my little, my little one

316
00:18:40,880 --> 00:18:46,840
Speaker 1: of those suitcase. I take five outfits and rotate those

317
00:18:46,840 --> 00:18:50,280
Speaker 1: five outfits. No draws. I just buy draws every week

318
00:18:50,840 --> 00:18:55,120
Speaker 1: and socks. It's that That's the life I love. I love,

319
00:18:55,640 --> 00:18:58,199
Speaker 1: I love being able to go back. It will be

320
00:18:58,240 --> 00:19:00,800
Speaker 1: my first time ever graduating on stage. Never graduated on

321
00:19:00,840 --> 00:19:03,680
Speaker 1: stage junior, high school or high school.

322
00:19:04,000 --> 00:19:06,040
Speaker 2: Yeah, we didn't need to know you didn't have no draws.

323
00:19:06,080 --> 00:19:06,480
Speaker 2: I mean.

324
00:19:08,320 --> 00:19:09,760
Speaker 1: It was really no point, you know, because if I

325
00:19:09,760 --> 00:19:13,480
Speaker 1: said I got five outfits in the bag, somebody's imaginations

326
00:19:13,480 --> 00:19:16,840
Speaker 1: like draws and draws.

327
00:19:16,880 --> 00:19:18,840
Speaker 4: Like we assume we have two bass and too brush.

328
00:19:19,000 --> 00:19:20,679
Speaker 1: No, no, you don't try to.

329
00:19:23,680 --> 00:19:26,280
Speaker 2: Think about draws.

330
00:19:26,760 --> 00:19:27,440
Speaker 1: I get them.

331
00:19:27,480 --> 00:19:29,320
Speaker 7: He buys them fresh every week.

332
00:19:29,560 --> 00:19:32,720
Speaker 2: Right now, every imagine not forgetting to put on your

333
00:19:32,720 --> 00:19:33,480
Speaker 2: speak on your neck.

334
00:19:38,880 --> 00:19:42,600
Speaker 1: Headlines, you know that, right Well, draws, no dis door.

335
00:19:42,800 --> 00:19:46,560
Speaker 1: See that's that's that's how you get there. That's that's

336
00:19:46,560 --> 00:19:47,760
Speaker 1: how he spreading in rumors.

337
00:19:47,840 --> 00:19:50,480
Speaker 6: I posted something that you said a couple of weeks ago,

338
00:19:50,720 --> 00:19:53,480
Speaker 6: and you were talking about how more they're they're investing

339
00:19:53,520 --> 00:19:57,280
Speaker 6: more into robots than into humans. And then you want

340
00:19:57,320 --> 00:19:59,840
Speaker 6: to paraphrase, and you wonder why people are stupid.

341
00:20:01,000 --> 00:20:07,639
Speaker 1: No, now, not stupid, that's harsh on people. Just my

342
00:20:07,720 --> 00:20:11,720
Speaker 1: two paths, my entrepreneurial path by philanthropic path. I've been

343
00:20:11,760 --> 00:20:16,840
Speaker 1: able to raise more money for AI personally than raising

344
00:20:16,920 --> 00:20:20,199
Speaker 1: money for the foundation. So my comparisons was not just

345
00:20:20,280 --> 00:20:26,280
Speaker 1: like it was not just like a vague perspective or

346
00:20:26,359 --> 00:20:32,680
Speaker 1: like this umbrella you know perspective. It was a personal perspective.

347
00:20:32,840 --> 00:20:37,520
Speaker 1: Also looking at you know, inner city investment for education

348
00:20:37,760 --> 00:20:43,080
Speaker 1: versus inner city investment for data centers, inner city investment

349
00:20:43,200 --> 00:20:48,960
Speaker 1: for you know, terminals and the way phones. We all

350
00:20:49,000 --> 00:20:53,840
Speaker 1: need connectivity. We also have this connectivity gap where folks

351
00:20:53,880 --> 00:20:57,760
Speaker 1: don't have you know, connection in rural areas. So you

352
00:20:57,840 --> 00:21:02,280
Speaker 1: have awesome AI coming no investment for education in inner cities.

353
00:21:02,640 --> 00:21:07,760
Speaker 1: Some developing countries don't even have access to connectivity. Wow,

354
00:21:08,640 --> 00:21:13,800
Speaker 1: folks that do have devices, they are there's these business

355
00:21:13,800 --> 00:21:21,320
Speaker 1: models that that are making folks addicted to features that

356
00:21:21,600 --> 00:21:25,520
Speaker 1: dumb us down right. The behavior is rewarding us for

357
00:21:27,320 --> 00:21:33,280
Speaker 1: content that keeps us on this this this hamster will Wow.

358
00:21:33,400 --> 00:21:39,520
Speaker 1: We're using emojis and freaking memes to communicate, and the

359
00:21:39,560 --> 00:21:44,920
Speaker 1: machine is being trained to break down quantum physics and

360
00:21:45,000 --> 00:21:49,719
Speaker 1: quantum entanglement. So that to me is like, Okay, where

361
00:21:49,840 --> 00:21:52,480
Speaker 1: does this go? And how do we curb it? How

362
00:21:52,480 --> 00:21:59,879
Speaker 1: do we get control back? Right? And I'm optimistic. I

363
00:22:00,119 --> 00:22:03,480
Speaker 1: think we will come around it. But you have to

364
00:22:03,520 --> 00:22:05,520
Speaker 1: identify the trip wires. You have to be like, hey,

365
00:22:05,520 --> 00:22:09,440
Speaker 1: look what's happening. Here's how we can you know, be

366
00:22:09,440 --> 00:22:13,040
Speaker 1: better off ten years from now. So a ten year

367
00:22:13,080 --> 00:22:17,000
Speaker 1: old now when they twenty, it's gonna be eight when

368
00:22:17,000 --> 00:22:20,520
Speaker 1: we identify it. If we don't identify it ten year old,

369
00:22:20,520 --> 00:22:23,760
Speaker 1: that's twenty. Yo, yep, I don't know.

370
00:22:24,359 --> 00:22:24,840
Speaker 2: I love this.

371
00:22:25,280 --> 00:22:26,760
Speaker 6: I love this because it's like, yo, these hoods that

372
00:22:26,800 --> 00:22:30,240
Speaker 6: we come from, these points, disfranchised areas, this country has

373
00:22:30,240 --> 00:22:32,520
Speaker 6: shown us that they may not always they may not

374
00:22:32,520 --> 00:22:35,760
Speaker 6: put the investments financially and the resources you know into

375
00:22:35,800 --> 00:22:38,480
Speaker 6: these communities, but they will put them into these this

376
00:22:38,640 --> 00:22:40,520
Speaker 6: technics AI. So how about we go to the tech

377
00:22:40,520 --> 00:22:43,880
Speaker 6: and AI they will benefit from resources.

378
00:22:44,040 --> 00:22:44,240
Speaker 2: Yeah.

379
00:22:44,280 --> 00:22:47,640
Speaker 1: So there's some there's some ten year old, twelve year

380
00:22:47,680 --> 00:22:52,040
Speaker 1: old and some inner city that's like they love mid journey.

381
00:22:52,920 --> 00:22:56,359
Speaker 1: They haven't really got their their imagination and their brains

382
00:22:56,359 --> 00:23:01,080
Speaker 1: around you know AI. Yet as far as being contributors

383
00:23:01,080 --> 00:23:05,200
Speaker 1: to it and and flexing with it. But some big

384
00:23:05,240 --> 00:23:10,960
Speaker 1: company in twenty thirty four is gonna come from Fifth Ward.

385
00:23:11,119 --> 00:23:16,760
Speaker 1: It's gonna come from the Bronx, It's gonna come from Watts,

386
00:23:16,840 --> 00:23:20,000
Speaker 1: it's gonna come from South Side Chicago. Somebody is going

387
00:23:20,040 --> 00:23:23,080
Speaker 1: to crack the code and do something massive.

388
00:23:24,200 --> 00:23:24,800
Speaker 4: In this space.

389
00:23:27,080 --> 00:23:27,800
Speaker 2: What did you deal?

390
00:23:29,800 --> 00:23:33,720
Speaker 7: My son is twelve. We need to get on that. Yes, No, seriously,

391
00:23:33,880 --> 00:23:36,679
Speaker 7: he's already into like software and tech and all that

392
00:23:36,720 --> 00:23:38,159
Speaker 7: type of stuff, Like he wants to go to school

393
00:23:38,160 --> 00:23:39,960
Speaker 7: for engineering and building all.

394
00:23:39,800 --> 00:23:40,800
Speaker 1: Types of stuff like that.

395
00:23:40,920 --> 00:23:43,720
Speaker 7: So he we thought it was sports, but he's like, no,

396
00:23:43,840 --> 00:23:45,920
Speaker 7: I was just playing sports because that's what y'all wanted

397
00:23:45,920 --> 00:23:48,320
Speaker 7: me to do. But I'm really into computers and he

398
00:23:48,400 --> 00:23:49,880
Speaker 7: loves that and he's been doing that for the last

399
00:23:49,880 --> 00:23:50,359
Speaker 7: two years.

400
00:23:50,359 --> 00:23:51,240
Speaker 1: So that's dope.

401
00:23:51,280 --> 00:23:53,280
Speaker 2: Yeah, you have to consume my mindset, get into the

402
00:23:53,320 --> 00:23:54,000
Speaker 2: creator minds.

403
00:23:54,560 --> 00:23:59,080
Speaker 1: Yeah, and compete, like when our community competes, we really

404
00:23:59,520 --> 00:24:01,560
Speaker 1: do it to levels like oh man, Like what like

405
00:24:01,880 --> 00:24:05,080
Speaker 1: if you think about it, basketball didn't have, you know,

406
00:24:05,280 --> 00:24:10,399
Speaker 1: African Americans from the jump. Baseball didn't have African Americans

407
00:24:10,720 --> 00:24:15,680
Speaker 1: or Dominicans and Colombians and Cubans from the jump. We

408
00:24:15,960 --> 00:24:19,280
Speaker 1: entered that sport and we not only innovated, we dominated

409
00:24:19,280 --> 00:24:23,600
Speaker 1: in that sport. And business is a sport, you know,

410
00:24:23,720 --> 00:24:26,840
Speaker 1: it's it's competitive, the same type of tenacity, the same

411
00:24:26,880 --> 00:24:31,200
Speaker 1: type of like fall, get back up, punch, get punched,

412
00:24:31,359 --> 00:24:35,120
Speaker 1: punch back. You gotta we gotta compete the same way

413
00:24:35,160 --> 00:24:37,359
Speaker 1: we compete in athletics, wate, the same way we compete

414
00:24:37,359 --> 00:24:41,320
Speaker 1: with entertainment. We gotta compete over here and and encourage

415
00:24:41,320 --> 00:24:45,800
Speaker 1: our kids, our nieces or nephews to compete that field.

416
00:24:46,680 --> 00:24:49,600
Speaker 2: And what did you deal? That's your other AI app?

417
00:24:50,880 --> 00:24:57,240
Speaker 1: Right? Yeah? Udioho Udio is Udio in a way was

418
00:24:57,280 --> 00:25:03,679
Speaker 1: inspired by that video. This team that used to be

419
00:25:03,760 --> 00:25:09,000
Speaker 1: at at Google left Google to start Udio, and I

420
00:25:09,080 --> 00:25:14,840
Speaker 1: met the folks that built this technology a year and

421
00:25:14,840 --> 00:25:19,399
Speaker 1: a half ago, and now they branched off started a

422
00:25:19,440 --> 00:25:23,040
Speaker 1: new company. It's a It's an app where you type

423
00:25:23,040 --> 00:25:27,240
Speaker 1: in you know, prompting like the vibe you want.

424
00:25:27,800 --> 00:25:29,280
Speaker 2: And bro.

425
00:25:30,680 --> 00:25:36,000
Speaker 1: Amazing music. It makes music, texting music. Make not only music,

426
00:25:36,040 --> 00:25:44,480
Speaker 1: it makes comedy sketches. It makes like live it's wild, bro, it's.

427
00:25:44,560 --> 00:25:50,840
Speaker 3: It's dope, does scripts and all that, like not scripts like,

428
00:25:52,440 --> 00:25:53,439
Speaker 3: how can I explain it?

429
00:25:54,560 --> 00:25:58,040
Speaker 1: I'xplained this way me and the guys in the Black

430
00:25:58,040 --> 00:25:59,640
Speaker 1: Eyed Peas. I'm like, yo, check us out. I want

431
00:25:59,680 --> 00:26:02,600
Speaker 1: to show you similar to the video what happened in

432
00:26:02,600 --> 00:26:05,720
Speaker 1: two thousand and freaking nine. When we did this video

433
00:26:07,200 --> 00:26:11,840
Speaker 1: last month twenty twenty four March, I'm like, yo, you

434
00:26:11,880 --> 00:26:15,280
Speaker 1: guys got to see this. I typed in Black Eyed

435
00:26:15,280 --> 00:26:23,320
Speaker 1: Peas live in forgot what country, and I said, because

436
00:26:23,320 --> 00:26:25,240
Speaker 1: we were trying to do a song and I needed

437
00:26:25,280 --> 00:26:28,359
Speaker 1: to get a crowd sound. How do you get a

438
00:26:28,359 --> 00:26:33,080
Speaker 1: crowd sound right now? There's a limitation that if you

439
00:26:33,200 --> 00:26:37,359
Speaker 1: want a crowd to repeat after you or say what,

440
00:26:37,520 --> 00:26:40,359
Speaker 1: you have to actually be in that crowd. You can't

441
00:26:40,400 --> 00:26:42,520
Speaker 1: make a crowd in the studio. You can put a

442
00:26:42,520 --> 00:26:44,000
Speaker 1: lot of reverb, we get a whole bunch of people

443
00:26:44,000 --> 00:26:45,560
Speaker 1: to be like yeah, and it's not gonna sound like

444
00:26:46,840 --> 00:26:50,240
Speaker 1: a freaking crowd, bro. So I needed to get a crowd.

445
00:26:50,960 --> 00:26:53,280
Speaker 1: So I went to the studio and I typed in

446
00:26:54,320 --> 00:26:59,000
Speaker 1: the chorus that to our song, and I put live

447
00:26:59,080 --> 00:27:05,480
Speaker 1: in concert twenty thousand people, and then I put we

448
00:27:05,600 --> 00:27:10,239
Speaker 1: seen the chorus here, crowd. I aimed to mic some

449
00:27:10,359 --> 00:27:13,240
Speaker 1: version of the prompt and then the crowds exist part

450
00:27:13,600 --> 00:27:21,000
Speaker 1: bro hmm, instant did it and taboo from the Black

451
00:27:21,000 --> 00:27:24,240
Speaker 1: Eyed Peas. He's like, wow, well this is dope, but

452
00:27:25,280 --> 00:27:28,320
Speaker 1: that really sounds like we was really there, but we

453
00:27:28,359 --> 00:27:30,840
Speaker 1: wasn't there and it was a crowd. It just hit

454
00:27:30,960 --> 00:27:34,800
Speaker 1: me like it was a memory, but it's not. So

455
00:27:34,880 --> 00:27:38,920
Speaker 1: that's the part that's like, oh, give me cherry. So

456
00:27:39,040 --> 00:27:42,240
Speaker 1: I'll show you this one thing that that we did

457
00:27:42,280 --> 00:27:46,320
Speaker 1: on udio. So let me get this. I did a

458
00:27:47,600 --> 00:27:52,840
Speaker 1: I did ah. I had a radio interview yesterday and

459
00:27:54,160 --> 00:27:57,960
Speaker 1: it was like a radio execs. We did this little

460
00:27:58,000 --> 00:28:02,959
Speaker 1: zoom on like the future radio. And so then I

461
00:28:03,000 --> 00:28:06,600
Speaker 1: took notes from everybody's comments and so then I put

462
00:28:06,600 --> 00:28:10,960
Speaker 1: the notes into Finn and so I was like, your Finn,

463
00:28:11,000 --> 00:28:14,240
Speaker 1: I need you to summarize this in a joke as

464
00:28:14,280 --> 00:28:16,800
Speaker 1: if somebody was gonna do it for stand up. So

465
00:28:16,840 --> 00:28:20,520
Speaker 1: then I put it in udio and then audio spat

466
00:28:20,600 --> 00:28:20,880
Speaker 1: this out.

467
00:28:24,080 --> 00:28:26,320
Speaker 7: So y'all talking about radio, right, y'all talking about how

468
00:28:26,320 --> 00:28:27,480
Speaker 7: something old is gonna be new.

469
00:28:27,960 --> 00:28:30,400
Speaker 1: That's like asking a cowhawk can jump up and fly.

470
00:28:33,080 --> 00:28:34,439
Speaker 5: A MFM is dope.

471
00:28:36,080 --> 00:28:41,720
Speaker 1: Nfm's cause his name is Morning and night and we

472
00:28:41,800 --> 00:28:46,840
Speaker 1: are on the motherfucking moon now night time.

473
00:28:47,160 --> 00:28:49,000
Speaker 4: Oh damn tape.

474
00:28:51,240 --> 00:28:56,840
Speaker 7: Like Christ say something, well you Finn Fiona, all your

475
00:28:56,840 --> 00:28:59,520
Speaker 7: baggy out of here, because I'm an I'm a stand

476
00:28:59,560 --> 00:29:00,000
Speaker 7: up comedian.

477
00:29:00,040 --> 00:29:02,720
Speaker 1: You know you're coming from my job. No, no, no here.

478
00:29:02,760 --> 00:29:07,920
Speaker 1: Here's why I could say the same thing about beats,

479
00:29:08,280 --> 00:29:11,840
Speaker 1: because I'm like, damn, these these things be making crazy beats.

480
00:29:12,520 --> 00:29:14,600
Speaker 1: I can hold on, let me score it.

481
00:29:14,960 --> 00:29:16,240
Speaker 2: I didn't get the points line for that joke.

482
00:29:17,000 --> 00:29:18,120
Speaker 7: I han't let it go long enough.

483
00:29:20,120 --> 00:29:21,960
Speaker 2: I want to know what happened to the radio for

484
00:29:22,080 --> 00:29:27,360
Speaker 2: obvious reasons. So let's go.

485
00:29:27,520 --> 00:29:30,440
Speaker 1: I'll go back to the beats thing. So and the

486
00:29:30,440 --> 00:29:32,960
Speaker 1: way I'm like, damn, this thing is doing the beats?

487
00:29:36,000 --> 00:29:40,920
Speaker 1: What does that mean for me making beats? Here's what

488
00:29:40,960 --> 00:29:45,480
Speaker 1: it means for me making beats? Uh? When I make

489
00:29:45,520 --> 00:29:47,760
Speaker 1: a beat and I make a song and then then

490
00:29:47,800 --> 00:29:49,680
Speaker 1: have to go and write the song to the beat

491
00:29:49,720 --> 00:29:54,320
Speaker 1: I just made. That's that took me goddamn at least

492
00:29:54,840 --> 00:29:59,720
Speaker 1: six hours to have something awesome. Go in there and

493
00:29:59,760 --> 00:30:02,520
Speaker 1: make it, make sure everything sound right, get the right

494
00:30:02,560 --> 00:30:06,080
Speaker 1: plug in for it. A good six hours is gone.

495
00:30:07,600 --> 00:30:09,440
Speaker 1: And then when I'm you know, I go in there,

496
00:30:09,960 --> 00:30:14,680
Speaker 1: I respond to the beat seven time, Towna, come around

497
00:30:14,680 --> 00:30:18,960
Speaker 1: the matana, see the sound in the conda by design time.

498
00:30:19,000 --> 00:30:22,360
Speaker 1: But then I'm like, okay, what am I saying? Then

499
00:30:22,360 --> 00:30:25,920
Speaker 1: I gotta translate that mumble. And when I translate that mumbo,

500
00:30:25,960 --> 00:30:29,280
Speaker 1: I gotta keep the vowals because the moment I change

501
00:30:29,320 --> 00:30:32,680
Speaker 1: the vowals then sent them and the TV it ain't.

502
00:30:32,760 --> 00:30:37,800
Speaker 1: It ain't flowing the way it was naturally. My my, my, my, my,

503
00:30:38,000 --> 00:30:41,480
Speaker 1: soul's response to the track. So I keep the vowels

504
00:30:42,680 --> 00:30:46,280
Speaker 1: and then find the emotion. So got it? Got it?

505
00:30:46,960 --> 00:30:49,000
Speaker 1: Then I'm like, I wonder what that would sound like

506
00:30:50,080 --> 00:30:53,320
Speaker 1: instead of the piano. I have a guitar. I don't

507
00:30:53,320 --> 00:30:55,959
Speaker 1: play guitar. Let me call up my guitar a George.

508
00:30:56,640 --> 00:30:58,040
Speaker 1: I need you to come over here and do this

509
00:30:58,240 --> 00:31:01,440
Speaker 1: guitar part because I can't get guitar to sound really

510
00:31:01,480 --> 00:31:04,440
Speaker 1: awesome on the keyboard. I want those errors. I want

511
00:31:04,440 --> 00:31:08,480
Speaker 1: those mistakes. I'm like, oh darn it. And so now

512
00:31:08,520 --> 00:31:11,000
Speaker 1: that I know there's an udio, I should be able

513
00:31:11,040 --> 00:31:12,840
Speaker 1: to put in the lyrics that I did after I

514
00:31:12,880 --> 00:31:14,640
Speaker 1: did that, and they're not going to take that away

515
00:31:14,680 --> 00:31:17,680
Speaker 1: from me. That's like my yoga, I got to stretch.

516
00:31:18,000 --> 00:31:20,400
Speaker 1: I don't care if there's an AI stretcher. I don't

517
00:31:20,400 --> 00:31:23,360
Speaker 1: care if there's like, you know, some AI yoga teacher.

518
00:31:23,720 --> 00:31:27,560
Speaker 1: I got a stretch for me. So that's my stretch.

519
00:31:28,280 --> 00:31:31,160
Speaker 1: And then once I stretch, what am I stretching for?

520
00:31:31,280 --> 00:31:33,280
Speaker 1: Am I stretching for a sprint? If I'm stretching for

521
00:31:33,320 --> 00:31:37,640
Speaker 1: a marathon? What's the actual game? And so when you're

522
00:31:37,680 --> 00:31:40,240
Speaker 1: studying for a game or competition, you want to see

523
00:31:40,400 --> 00:31:43,760
Speaker 1: what your competitor's doing, and you want to have as

524
00:31:43,800 --> 00:31:48,280
Speaker 1: many options as possible to dodge to function up. And

525
00:31:48,320 --> 00:31:52,120
Speaker 1: that's when AI will come into it as a tool

526
00:31:52,160 --> 00:31:54,479
Speaker 1: for you. And then I could just version out what

527
00:31:54,640 --> 00:31:57,800
Speaker 1: I created. And then when I version out what I created,

528
00:31:57,880 --> 00:32:00,640
Speaker 1: now I have the ability to then like really make

529
00:32:00,720 --> 00:32:02,800
Speaker 1: that piece of work that came out of my imagination,

530
00:32:03,960 --> 00:32:05,520
Speaker 1: to make it the best it could be. What I

531
00:32:05,560 --> 00:32:09,000
Speaker 1: have a plethora of freaking options, So all it did

532
00:32:09,040 --> 00:32:10,680
Speaker 1: was all it does if you look at it from

533
00:32:10,680 --> 00:32:13,840
Speaker 1: an optimistic point of view. I got more options than one,

534
00:32:14,720 --> 00:32:17,800
Speaker 1: but I still had a seated. Now then there's like

535
00:32:17,800 --> 00:32:21,880
Speaker 1: this lazy lazy is like and you're gonna know the

536
00:32:21,920 --> 00:32:26,000
Speaker 1: difference between whack and awesome if you lazy rocking, and

537
00:32:26,040 --> 00:32:27,719
Speaker 1: we know that with out of tune, we know that

538
00:32:27,760 --> 00:32:31,200
Speaker 1: from like you know the state of music where it's

539
00:32:31,200 --> 00:32:33,480
Speaker 1: at right now. There's a lot of lazy rocking right now.

540
00:32:34,040 --> 00:32:36,680
Speaker 1: There's folks that are like worried about skip rates? Is

541
00:32:36,680 --> 00:32:40,360
Speaker 1: that really you know? Are you being truly creative when

542
00:32:40,360 --> 00:32:44,040
Speaker 1: you're based on skip rates and now the algorithm is

543
00:32:44,040 --> 00:32:45,880
Speaker 1: telling you that you got to put the song, you

544
00:32:45,960 --> 00:32:48,640
Speaker 1: gotta put the chorus here in the front. Is that

545
00:32:48,840 --> 00:32:51,840
Speaker 1: really what the soul of the song needs to be?

546
00:32:51,880 --> 00:32:54,320
Speaker 1: The coorus gotta be slammed in the front, or is

547
00:32:54,320 --> 00:32:57,080
Speaker 1: there some way to get you to that? So I

548
00:32:57,080 --> 00:33:01,400
Speaker 1: don't think we're even being truly imaginative right now. We're

549
00:33:01,440 --> 00:33:05,840
Speaker 1: being reactive to algorithms and tiktoks. When now the song

550
00:33:05,960 --> 00:33:09,000
Speaker 1: is like reduced to fifteen seconds. Are you really truly

551
00:33:09,040 --> 00:33:13,239
Speaker 1: being creative or you being pimped by an algorithm? So

552
00:33:14,720 --> 00:33:19,200
Speaker 1: I'm happy that we're in this space, this this stage,

553
00:33:19,280 --> 00:33:22,320
Speaker 1: the space to shake us up, to wake us up,

554
00:33:22,720 --> 00:33:25,360
Speaker 1: because the moment that we continue to go down this

555
00:33:25,440 --> 00:33:30,720
Speaker 1: algorithmic programming and we value algorithmic music, well, then the

556
00:33:30,760 --> 00:33:33,520
Speaker 1: AI is going to make better algorithmic shit than us

557
00:33:33,560 --> 00:33:38,080
Speaker 1: because it understands the algorithm. So somebody had something has

558
00:33:38,120 --> 00:33:40,280
Speaker 1: to shake us out of this like program that we're

559
00:33:40,320 --> 00:33:45,080
Speaker 1: in because it is a program, and the audience, hopefully

560
00:33:45,120 --> 00:33:48,960
Speaker 1: being optimistic, starts to value organic oranges.

561
00:33:49,200 --> 00:33:50,680
Speaker 2: Do you have some type of neuralink?

562
00:33:50,880 --> 00:33:52,640
Speaker 1: Well, no, no, no, no.

563
00:33:52,720 --> 00:33:53,360
Speaker 2: Would you get it?

564
00:33:53,880 --> 00:33:56,080
Speaker 6: No, no, no, no, I don't believe you now, I mean,

565
00:33:56,080 --> 00:33:58,040
Speaker 6: I don't believe you wouldn't get it. I feel like

566
00:33:58,040 --> 00:34:00,200
Speaker 6: you would almost have to have it to comput in

567
00:34:00,240 --> 00:34:04,360
Speaker 6: the future, all of us, especially if you're a creative.

568
00:34:05,440 --> 00:34:09,480
Speaker 2: I could no, no, no, that's a detail you to say.

569
00:34:09,400 --> 00:34:18,239
Speaker 1: Huh, whose day what you talk about? There's a lot

570
00:34:18,280 --> 00:34:18,960
Speaker 1: of days that.

571
00:34:20,719 --> 00:34:21,839
Speaker 2: You wouldn't get neural link though.

572
00:34:22,280 --> 00:34:25,359
Speaker 1: I don't know. No, I wouldn't not even I don't

573
00:34:25,400 --> 00:34:28,359
Speaker 1: know because I leave right now, not even right now.

574
00:34:28,560 --> 00:34:32,719
Speaker 1: Just the concept. There's certain concepts that don't jail with

575
00:34:32,760 --> 00:34:37,239
Speaker 1: me personally. Crypto didn't jail with me. They didn't get

576
00:34:37,239 --> 00:34:40,200
Speaker 1: into it. NFTs didn't jail with me, didn't get into it.

577
00:34:41,440 --> 00:34:45,840
Speaker 1: Neuralink that doesn't jail with me. I'm not going to

578
00:34:45,920 --> 00:34:51,440
Speaker 1: get into it. Gang banging didn't jail with me, not

579
00:34:51,440 --> 00:34:53,560
Speaker 1: going to get into it. It's like in a project,

580
00:34:53,600 --> 00:34:57,480
Speaker 1: you could gang bang to keep up to keep compete. Nah,

581
00:34:57,600 --> 00:34:59,319
Speaker 1: I don't have to do that selling drugs. Didn't jail

582
00:34:59,360 --> 00:35:01,319
Speaker 1: with me, didn't get in too. About when you got

583
00:35:01,320 --> 00:35:03,360
Speaker 1: nothing in the in the hood and the projects, you

584
00:35:03,440 --> 00:35:07,120
Speaker 1: gotta survive, you gotta sell this not didn't get into it.

585
00:35:07,640 --> 00:35:11,239
Speaker 1: So that same like that stance of like nah, I

586
00:35:11,280 --> 00:35:13,680
Speaker 1: ain't get into that. It's the same way I feel

587
00:35:14,120 --> 00:35:18,360
Speaker 1: about New Link. Not to say New Link is gang banging,

588
00:35:20,239 --> 00:35:27,000
Speaker 1: but it's mind mind banging. I don't know what's on

589
00:35:27,000 --> 00:35:32,399
Speaker 1: the other end of it, you know, it's like, uh,

590
00:35:32,960 --> 00:35:34,239
Speaker 1: I don't know what's on the other end of it

591
00:35:34,280 --> 00:35:36,879
Speaker 1: to be like yeah, I'm down with.

592
00:35:36,840 --> 00:35:40,239
Speaker 6: That, yeah, because it could it could could take you

593
00:35:40,280 --> 00:35:43,320
Speaker 6: over in some way, playing with your brain like that.

594
00:35:45,640 --> 00:35:48,520
Speaker 1: I don't know enough information about I know it has

595
00:35:49,480 --> 00:35:54,759
Speaker 1: everything that everything that comes to society always aims to

596
00:35:54,880 --> 00:35:58,960
Speaker 1: do some solution, to bring solutions, and so I'm not

597
00:35:59,120 --> 00:36:06,440
Speaker 1: knocking what it aims to do. But this thing that

598
00:36:06,480 --> 00:36:11,680
Speaker 1: we have called the brain is very very powerful. I

599
00:36:11,719 --> 00:36:15,960
Speaker 1: forgot how many parameters are how many parameters we have

600
00:36:16,080 --> 00:36:21,960
Speaker 1: in our neuralink sorry, our neural our neural connectivity in

601
00:36:22,000 --> 00:36:22,520
Speaker 1: our minds.

602
00:36:23,360 --> 00:36:30,279
Speaker 2: Look it up. Hold on about.

603
00:36:33,040 --> 00:36:34,400
Speaker 1: So I get that, I get that, I get that,

604
00:36:34,440 --> 00:36:35,600
Speaker 1: I get that, we get this.

605
00:36:35,960 --> 00:36:37,440
Speaker 4: They're gonna be like, how could you forget?

606
00:36:37,440 --> 00:36:39,719
Speaker 2: Will one hundred billion neurons.

607
00:36:40,280 --> 00:36:44,880
Speaker 6: Each neuron has seven thousand synapsis and roughly seven hundred

608
00:36:44,960 --> 00:36:45,680
Speaker 6: T parameters.

609
00:36:45,680 --> 00:36:47,479
Speaker 2: I don't know what that means.

610
00:36:48,160 --> 00:36:53,640
Speaker 1: Yeah, the adult human brain runs continuously, whether awake or asleep,

611
00:36:54,400 --> 00:36:58,279
Speaker 1: only about twelve watts of power. It takes the power

612
00:36:58,320 --> 00:37:04,600
Speaker 1: our minds. These ais take a billion watts to train

613
00:37:05,120 --> 00:37:11,080
Speaker 1: and consumes about two hundred and sixty million watts a day. So,

614
00:37:11,440 --> 00:37:14,640
Speaker 1: just from that perspective, what it takes to power the

615
00:37:14,719 --> 00:37:17,520
Speaker 1: human mind and how vast it is, as far as

616
00:37:17,520 --> 00:37:20,960
Speaker 1: it's compute, and we only use a small percentage of it,

617
00:37:24,200 --> 00:37:28,239
Speaker 1: I don't know if a business practice is on the

618
00:37:28,239 --> 00:37:32,360
Speaker 1: other end to then use human minds to power ais.

619
00:37:33,560 --> 00:37:38,200
Speaker 1: So for that, I don't know. And there's a lot

620
00:37:38,200 --> 00:37:40,640
Speaker 1: of folks that maybe a lot of Elon folks, and

621
00:37:41,360 --> 00:37:44,440
Speaker 1: you know, Elon's dope. His contributions have been amazing. I

622
00:37:44,520 --> 00:37:47,560
Speaker 1: just hope it doesn't go down that path. I hope

623
00:37:47,640 --> 00:37:49,640
Speaker 1: it's not like Hey, Wow, you know, how do you

624
00:37:49,680 --> 00:37:54,959
Speaker 1: sustain global compute? Wow? You know the human mind only

625
00:37:55,000 --> 00:38:01,080
Speaker 1: takes twelve a twelve watt twelve watts power to power it.

626
00:38:04,160 --> 00:38:07,720
Speaker 1: That would be horrible if there's a farm of brains

627
00:38:07,800 --> 00:38:08,680
Speaker 1: to power AI.

628
00:38:11,440 --> 00:38:14,560
Speaker 6: So there's no way robots should win, is what you're saying,

629
00:38:14,880 --> 00:38:16,640
Speaker 6: because you know, that's the other thing people are afraid of.

630
00:38:16,719 --> 00:38:18,319
Speaker 6: They feel like, you know, they've watched all of these

631
00:38:18,360 --> 00:38:20,920
Speaker 6: movies where the robots take over and that's the end

632
00:38:20,960 --> 00:38:21,319
Speaker 6: of the world.

633
00:38:21,360 --> 00:38:24,839
Speaker 2: There's no way the robots can win. They shouldn't.

634
00:38:26,120 --> 00:38:26,759
Speaker 1: What was that there?

635
00:38:27,160 --> 00:38:29,000
Speaker 6: There's no way the robots can win, Like, none of

636
00:38:29,080 --> 00:38:32,080
Speaker 6: these movies that we've seen, these apocalyptic movies where robots

637
00:38:32,120 --> 00:38:34,280
Speaker 6: take over could happen.

638
00:38:34,680 --> 00:38:37,000
Speaker 7: Did that sound dumb to you?

639
00:38:37,040 --> 00:38:44,680
Speaker 1: No? No, I don't. The optimistic part of me, which

640
00:38:44,760 --> 00:38:48,000
Speaker 1: is the the the majority governing side of how I

641
00:38:48,040 --> 00:38:54,080
Speaker 1: look out the world, is no, because that's not how

642
00:38:54,120 --> 00:38:59,880
Speaker 1: they're programmed. They're not. They're not programmed too. But the

643
00:39:00,320 --> 00:39:02,399
Speaker 1: who's to say that there's not some wacko out there

644
00:39:02,440 --> 00:39:07,560
Speaker 1: that wants to program that to be like the you know,

645
00:39:08,719 --> 00:39:13,160
Speaker 1: the owner of the wickedest freaking pit bulls in the hood.

646
00:39:16,480 --> 00:39:24,600
Speaker 1: Who's to say, who knows, but believing in humanity. Humanity

647
00:39:24,600 --> 00:39:28,239
Speaker 1: has proven that although we've made mistakes, it's not in

648
00:39:28,320 --> 00:39:32,439
Speaker 1: our nature to build something that's gonna take us out.

649
00:39:32,719 --> 00:39:43,840
Speaker 1: The nuclear bomb, well, the nuclear bomb, that's a moment

650
00:39:45,000 --> 00:39:51,719
Speaker 1: where humans were at are worse. And I believe in

651
00:39:51,800 --> 00:39:54,640
Speaker 1: humanity too much to think that we're going to be

652
00:39:54,680 --> 00:40:02,759
Speaker 1: at our worst to that extreme. Again, you trip, you fall,

653
00:40:03,560 --> 00:40:05,960
Speaker 1: very rarely do you trip. You fall and fall and

654
00:40:06,000 --> 00:40:09,839
Speaker 1: fall and fall and fall. But it's not in our

655
00:40:09,880 --> 00:40:12,919
Speaker 1: balance and we don't. That's not how we are. We fall,

656
00:40:13,000 --> 00:40:15,799
Speaker 1: we scratch our knee, we break our leg, and then

657
00:40:15,800 --> 00:40:20,000
Speaker 1: from there you're super cautious. And I see, I think

658
00:40:20,080 --> 00:40:23,759
Speaker 1: human as an organism is more like that than it

659
00:40:23,920 --> 00:40:26,759
Speaker 1: is like I fall, I bust my face and then

660
00:40:26,840 --> 00:40:29,319
Speaker 1: I'm careless and I'm just gonna throw my face on

661
00:40:29,360 --> 00:40:32,640
Speaker 1: the floor. I don't think that's That's not what we

662
00:40:32,680 --> 00:40:35,560
Speaker 1: are as a as a society, as a community, as

663
00:40:35,560 --> 00:40:41,880
Speaker 1: a species, as an organism. So just being optimistic, like

664
00:40:41,920 --> 00:40:44,239
Speaker 1: I said, wearing that, or I could go down that

665
00:40:44,280 --> 00:40:50,640
Speaker 1: path creatively imaginatively, and we should all just let's all

666
00:40:50,680 --> 00:40:51,600
Speaker 1: just jump out the building.

667
00:40:51,600 --> 00:40:56,000
Speaker 2: Now, you gotta be sounds, the blackness, be optimistic. Got

668
00:40:56,000 --> 00:40:57,239
Speaker 2: to keep your head to this guy. You have to.

669
00:40:58,239 --> 00:41:00,239
Speaker 1: I just heard that. I just heard the of the

670
00:41:00,280 --> 00:41:04,359
Speaker 1: song the Lady. I love that song.

671
00:41:05,400 --> 00:41:07,799
Speaker 4: How were you musically? Where are you at musically right now?

672
00:41:11,200 --> 00:41:14,919
Speaker 1: We got a new song coming out for the Bad

673
00:41:14,920 --> 00:41:19,360
Speaker 1: Boys for soundtrack, So that's really really.

674
00:41:19,160 --> 00:41:21,280
Speaker 2: Cool to everybody?

675
00:41:22,280 --> 00:41:28,360
Speaker 1: Yep, everybody. Well, we got a bunch of everybody's so

676
00:41:28,440 --> 00:41:33,600
Speaker 1: black piece there's there's So if you say everybody and

677
00:41:33,719 --> 00:41:36,680
Speaker 1: don't mention Kim Hill, well then that's not true. That's

678
00:41:36,719 --> 00:41:39,840
Speaker 1: not good to Kim Hill. So when you say everybody,

679
00:41:39,840 --> 00:41:42,600
Speaker 1: you don't mention Macy Gray or Stero, that's not good

680
00:41:42,600 --> 00:41:47,040
Speaker 1: to our past either. We were we wreck with Mason

681
00:41:47,080 --> 00:41:49,040
Speaker 1: Gray on two albums.

682
00:41:49,520 --> 00:41:50,359
Speaker 2: I don't remember that.

683
00:41:50,560 --> 00:41:52,600
Speaker 1: Yeah, well those are the records that didn't really sell

684
00:41:52,640 --> 00:41:57,360
Speaker 1: that well. But we have a history before Fergie. So

685
00:41:57,360 --> 00:42:01,080
Speaker 1: when people tell me everybody, I'm like, watch everybody can

686
00:42:01,239 --> 00:42:04,680
Speaker 1: heal Everybody? And I love her. That's my big sister,

687
00:42:05,400 --> 00:42:07,640
Speaker 1: the Fergie. Everybody they're talking about the fur everybody, Ye

688
00:42:08,000 --> 00:42:12,759
Speaker 1: Fergie everybody. Well, then we have a current with Jay

689
00:42:12,880 --> 00:42:17,759
Speaker 1: Ray what hits and with twenty year olds that you

690
00:42:17,800 --> 00:42:21,880
Speaker 1: know fifteen year olds that when we did songs with Fergie,

691
00:42:21,960 --> 00:42:25,239
Speaker 1: that fifteen year old how it was two mm hmm

692
00:42:26,480 --> 00:42:29,200
Speaker 1: and eighteen They come in with their parents to our

693
00:42:29,239 --> 00:42:34,080
Speaker 1: shows singing Elite Mo and Mama Sita eighteen twenty year olds,

694
00:42:34,719 --> 00:42:35,520
Speaker 1: you know there was.

695
00:42:35,600 --> 00:42:37,600
Speaker 4: Five So Fergie's not on it.

696
00:42:37,760 --> 00:42:41,040
Speaker 2: The question I'm trying to it, Well, that's all we're

697
00:42:41,040 --> 00:42:42,040
Speaker 2: trying to you.

698
00:42:42,160 --> 00:42:46,560
Speaker 1: Forty yeah, yeah, forty five Shane. But to the twenty

699
00:42:46,640 --> 00:42:48,040
Speaker 1: year olds out there that like Jay Ray.

700
00:42:48,000 --> 00:42:51,040
Speaker 4: So Jay Rays on it, you got Okay, that's what's that?

701
00:42:51,480 --> 00:42:51,760
Speaker 1: Yeah?

702
00:42:51,920 --> 00:42:54,400
Speaker 6: But but yeah, so like like what happened after the

703
00:42:54,480 --> 00:42:57,359
Speaker 6: national anthem controversy, you didn't want anything to do it anymore.

704
00:42:57,120 --> 00:42:59,200
Speaker 1: You know like Bad Boys, Bad Boys, the Bad Boys

705
00:42:59,200 --> 00:43:01,040
Speaker 1: three song that we did Elite Moment forre you wasn't

706
00:43:01,040 --> 00:43:04,759
Speaker 1: on it, Okay, that was the national anthem.

707
00:43:04,880 --> 00:43:08,839
Speaker 6: Okay, yeah, no, the you remember you saw you saw

708
00:43:08,840 --> 00:43:10,080
Speaker 6: the national anthem controversy.

709
00:43:11,320 --> 00:43:16,160
Speaker 1: See, I'll be I'll be looking at the world optimistic, keep.

710
00:43:16,000 --> 00:43:23,680
Speaker 6: My head to the ladies.

711
00:43:26,040 --> 00:43:28,319
Speaker 4: Can they downloaded all that stuff?

712
00:43:28,520 --> 00:43:30,840
Speaker 1: Download f y I dot a I and if you

713
00:43:30,880 --> 00:43:33,719
Speaker 1: want to see Finn and Fiona in action, check me

714
00:43:33,760 --> 00:43:37,640
Speaker 1: out on Serious XM on My Serious XM radio show. Well,

715
00:43:37,880 --> 00:43:40,719
Speaker 1: my very the very first co host. That's a I

716
00:43:40,960 --> 00:43:46,520
Speaker 1: is Fiona on my radio show, and yeah, it's it's uh,

717
00:43:47,280 --> 00:43:48,120
Speaker 1: it's it's fresh.

718
00:43:48,280 --> 00:43:52,720
Speaker 6: It's a pleasure. Well, I've never not learned something from you. Literally,

719
00:43:52,800 --> 00:43:54,480
Speaker 6: every time you come and have a conversation with her,

720
00:43:55,160 --> 00:43:58,319
Speaker 6: I learned something. I walk away like, oh man, I

721
00:43:58,360 --> 00:43:59,120
Speaker 6: need to look into that.

722
00:43:59,760 --> 00:44:02,359
Speaker 1: Thanks, thank you, guys. It's always a pleasure to come here,

723
00:44:02,400 --> 00:44:07,280
Speaker 1: like and congratulations on the success. It just keeps keeps growing.

724
00:44:07,840 --> 00:44:12,359
Speaker 1: The subjects you talk about are like heavy subjects and uh,

725
00:44:12,719 --> 00:44:16,200
Speaker 1: light subjects. Funny subjects. You guys like cover the whole entire.

726
00:44:16,280 --> 00:44:20,080
Speaker 1: It's it's funny, it's fun, it's informative. It's like wow, damn,

727
00:44:20,160 --> 00:44:25,440
Speaker 1: like you really push you really pushed guests. Your fearlessness

728
00:44:25,520 --> 00:44:26,520
Speaker 1: is like wow, that's dope.

729
00:44:27,920 --> 00:44:28,960
Speaker 2: Thank you sir, thank you.

730
00:44:29,560 --> 00:44:30,440
Speaker 1: Well it's will I am.

731
00:44:30,880 --> 00:44:34,400
Speaker 4: It's the Breakfast Club. Good morning, wake that ass up

732
00:44:34,520 --> 00:44:35,040
Speaker 4: in the morning.

733
00:44:35,239 --> 00:44:36,280
Speaker 2: The Breakfast Club