1 00:00:00,280 --> 00:00:00,560 Speaker 1: Mala. 2 00:00:00,680 --> 00:00:04,519 Speaker 2: Have you listened to an AI podcast? 3 00:00:04,840 --> 00:00:08,000 Speaker 1: Oh, an AI podcast? You mean like this podcast? Look out, 4 00:00:08,160 --> 00:00:11,280 Speaker 1: our radio AI hosted has been the whole time you 5 00:00:11,360 --> 00:00:13,480 Speaker 1: might have nothing. And that's and that's the plot of 6 00:00:13,520 --> 00:00:14,160 Speaker 1: the movie. 7 00:00:14,880 --> 00:00:15,640 Speaker 2: Let's write it down. 8 00:00:15,760 --> 00:00:18,400 Speaker 1: That's the big reveal. We were We were the original 9 00:00:18,440 --> 00:00:20,160 Speaker 1: bots from day one. 10 00:00:20,480 --> 00:00:25,680 Speaker 2: That's like Ai meets Terminator, meets all all of the 11 00:00:25,680 --> 00:00:26,760 Speaker 2: horrible things. 12 00:00:26,880 --> 00:00:29,040 Speaker 1: But also meets like real women have curves and sister 13 00:00:29,080 --> 00:00:31,680 Speaker 1: heard of the traveling fans. It's all wrapped up into one. 14 00:00:31,840 --> 00:00:32,800 Speaker 2: That's a movie I'd watched. 15 00:00:35,080 --> 00:00:35,680 Speaker 1: Let's write it. 16 00:00:39,360 --> 00:00:42,280 Speaker 2: Hey, look a mo is viosa here. I'm so excited 17 00:00:42,320 --> 00:00:45,320 Speaker 2: to share that. Look at our radio and Signora Sex said, 18 00:00:45,760 --> 00:00:49,920 Speaker 2: our finalist for the Signal Awards. We are also up 19 00:00:49,960 --> 00:00:53,480 Speaker 2: for the Listener's Choice Award, so we're asking for your help. 20 00:00:54,040 --> 00:00:57,320 Speaker 2: Please vote for us by heading to vote dot Signal 21 00:00:57,400 --> 00:01:01,040 Speaker 2: Award dot com or click the link in the show notes. 22 00:01:01,160 --> 00:01:03,680 Speaker 2: It would mean so much to us if you vote 23 00:01:03,680 --> 00:01:08,360 Speaker 2: for us and show the LATINX voices and our stories matter. 24 00:01:08,800 --> 00:01:16,640 Speaker 2: Thank you so much. Besitos Ola la loka morees, I'm. 25 00:01:16,440 --> 00:01:18,520 Speaker 1: Theosa and I'm Mala. 26 00:01:18,680 --> 00:01:24,640 Speaker 2: Today we're talking about the intersections of artificial intelligence, the 27 00:01:24,720 --> 00:01:29,119 Speaker 2: creative industry, and the environment. Oh A lot to unpack here. 28 00:01:29,240 --> 00:01:30,080 Speaker 2: I hope we get through. 29 00:01:29,920 --> 00:01:33,400 Speaker 1: All of it. This is a big, tangled, tangled mess. 30 00:01:33,480 --> 00:01:38,360 Speaker 1: Yes here, the world is on fire and AI is 31 00:01:38,400 --> 00:01:41,920 Speaker 1: taking over, just like every Hollywood movie for the past 32 00:01:41,959 --> 00:01:43,480 Speaker 1: thirty years has warned us. 33 00:01:43,480 --> 00:01:46,960 Speaker 2: The vow was nobody paying attention during Will Smith's AI 34 00:01:47,120 --> 00:01:48,520 Speaker 2: film like Hello. 35 00:01:48,560 --> 00:01:53,400 Speaker 1: I Robot Yes, Yeah, which was based on books Yes, 36 00:01:53,480 --> 00:01:57,080 Speaker 1: I Robot Yes, which also predicted this very thing where 37 00:01:57,120 --> 00:02:00,640 Speaker 1: the artificial intelligence we create becomes so intelligent that it 38 00:02:00,760 --> 00:02:03,480 Speaker 1: destroys us in order to preserve itself. Yeah. 39 00:02:03,520 --> 00:02:06,520 Speaker 2: I'm just like everybody like signed reading go for it, 40 00:02:06,600 --> 00:02:09,760 Speaker 2: like nineteen eighty four, Fahrenheid hohrfe do one. Let's bring 41 00:02:09,800 --> 00:02:12,920 Speaker 2: it back, read them, study them, parable of this. 42 00:02:13,000 --> 00:02:16,640 Speaker 1: Hour, like yeah, do it. Watch all the Terminator movies. 43 00:02:17,680 --> 00:02:18,520 Speaker 1: I mean it's right there. 44 00:02:18,720 --> 00:02:21,560 Speaker 2: Yeah. Not to be an alarmist, right and like so 45 00:02:21,720 --> 00:02:24,960 Speaker 2: doom and gloom, But I do think, like we on 46 00:02:25,000 --> 00:02:27,679 Speaker 2: this podcast and the Audio art Hive that it is 47 00:02:27,840 --> 00:02:30,960 Speaker 2: we have to have this conversation of AI. We've like 48 00:02:31,040 --> 00:02:33,799 Speaker 2: dabbled here and there this past season and over the 49 00:02:33,880 --> 00:02:38,359 Speaker 2: seasons we've talked about disappearing art and the way AI 50 00:02:38,919 --> 00:02:42,959 Speaker 2: is like stealing from existing art right that's out there 51 00:02:42,960 --> 00:02:47,600 Speaker 2: in the creative world, and then art that exists being 52 00:02:47,639 --> 00:02:50,120 Speaker 2: taken down from streamers and platforms. That's like the way 53 00:02:50,120 --> 00:02:52,519 Speaker 2: we've talked about it in the past. This season, we've 54 00:02:52,520 --> 00:02:56,040 Speaker 2: talked about it in terms of chatgybt and it being 55 00:02:56,120 --> 00:02:59,200 Speaker 2: used this therapy, but today we're talking about it in 56 00:02:59,240 --> 00:03:02,680 Speaker 2: the creative field and how that also intersects to the 57 00:03:02,760 --> 00:03:05,480 Speaker 2: literal material world that we live in, that is the environment. 58 00:03:06,160 --> 00:03:11,160 Speaker 1: Absolutely. So on a past episode earlier this season, we 59 00:03:11,280 --> 00:03:14,200 Speaker 1: discussed the crash out or crisis. Is it a crash 60 00:03:14,240 --> 00:03:16,560 Speaker 1: out online or is it a mental health crisis. We 61 00:03:16,600 --> 00:03:20,440 Speaker 1: also released our episode on AI psychosis and this phenomenon 62 00:03:20,560 --> 00:03:24,440 Speaker 1: of utilizing chat shept as therapy, And so we're just 63 00:03:24,520 --> 00:03:26,839 Speaker 1: kind of like building off of these ideas and we're 64 00:03:26,840 --> 00:03:29,720 Speaker 1: bringing it, like the USA said, to the creative, to 65 00:03:29,760 --> 00:03:34,400 Speaker 1: the creative industries, and to the environment, because these things overlap. 66 00:03:34,440 --> 00:03:37,520 Speaker 1: And I just want to say, you know, in general, 67 00:03:38,360 --> 00:03:42,080 Speaker 1: I think that right now we're talking about how AI 68 00:03:42,240 --> 00:03:44,800 Speaker 1: is like coming for creative jobs and for the creative 69 00:03:44,840 --> 00:03:48,480 Speaker 1: industry and like replacing artists and I think in general, 70 00:03:48,760 --> 00:03:52,640 Speaker 1: like the arts and education are just very easy targets, 71 00:03:53,000 --> 00:03:56,600 Speaker 1: like when it comes to defunding, when it comes to minimizing, 72 00:03:56,640 --> 00:04:00,720 Speaker 1: when it comes to removing, and it's never it never 73 00:04:00,760 --> 00:04:04,200 Speaker 1: stops there. You may defund the arts and education, but 74 00:04:04,320 --> 00:04:08,360 Speaker 1: it's going to expand into other things like healthcare and infrastructure. 75 00:04:08,800 --> 00:04:11,480 Speaker 1: And I think there's a similar phenomenon going on here 76 00:04:11,560 --> 00:04:17,720 Speaker 1: where AI is starting by replacing artists and creatives and 77 00:04:17,760 --> 00:04:21,640 Speaker 1: by minimizing opportunity for human beings in the arts and creativity. 78 00:04:21,920 --> 00:04:24,520 Speaker 1: But that's just where it's beginning, and it's going to 79 00:04:24,560 --> 00:04:27,000 Speaker 1: branch out and expand into other areas. And when the 80 00:04:27,120 --> 00:04:29,800 Speaker 1: arts are targeted, we always need to be concerned, We 81 00:04:29,880 --> 00:04:32,360 Speaker 1: always need to be worried. It's just a testing ground 82 00:04:32,560 --> 00:04:34,760 Speaker 1: because if you can succeed here, you're going to succeed 83 00:04:34,800 --> 00:04:36,599 Speaker 1: in other places. So I kind of want to just 84 00:04:36,600 --> 00:04:38,360 Speaker 1: I've been thinking about that. I want to like pin 85 00:04:38,440 --> 00:04:40,240 Speaker 1: that at the top of our episode. 86 00:04:40,360 --> 00:04:43,160 Speaker 2: I love that, And that's a really good analysis and opener. 87 00:04:43,279 --> 00:04:47,520 Speaker 2: Breaking news in our industry this past week is this 88 00:04:47,920 --> 00:04:52,560 Speaker 2: startup that is creating AI podcast. They have been able 89 00:04:52,600 --> 00:04:56,520 Speaker 2: to create five thousand podcasts and three thousand episodes a 90 00:04:56,560 --> 00:05:00,000 Speaker 2: week and with the cost of one dollar per episode, 91 00:05:00,080 --> 00:05:03,560 Speaker 2: and this is reported by the Hollywood Reporter. One of 92 00:05:03,560 --> 00:05:07,479 Speaker 2: the reasons this was really striking, I think in our 93 00:05:07,600 --> 00:05:11,599 Speaker 2: digital community and our podcast creative circle is because this 94 00:05:11,760 --> 00:05:17,120 Speaker 2: AI startup called Inception Point AI is founded by a 95 00:05:17,160 --> 00:05:21,480 Speaker 2: former Wondery exec and a Wondery is a podcast studio 96 00:05:21,680 --> 00:05:25,800 Speaker 2: that has made incredible podcast over the year over the years, 97 00:05:25,920 --> 00:05:30,000 Speaker 2: and so it definitely is disheartening to see a former 98 00:05:30,080 --> 00:05:34,760 Speaker 2: exec championing the use of AI podcast and this is 99 00:05:34,800 --> 00:05:39,680 Speaker 2: not just AI podcasts that are using AI to script 100 00:05:39,680 --> 00:05:43,440 Speaker 2: their episodes and to possibly edit, but to also host 101 00:05:44,040 --> 00:05:49,359 Speaker 2: these podcasts, which is I think the opposite of what 102 00:05:49,480 --> 00:05:52,239 Speaker 2: a podcast is meant to be. A podcast is about 103 00:05:52,279 --> 00:05:58,440 Speaker 2: connection and familiarity and storytelling and that cannot be replaced 104 00:05:58,560 --> 00:06:02,560 Speaker 2: by an AI, by a robot. That is the point 105 00:06:03,160 --> 00:06:06,520 Speaker 2: of radio and podcasting is the connection with the host 106 00:06:06,640 --> 00:06:09,360 Speaker 2: and the way we connect with our listeners in turn, 107 00:06:09,440 --> 00:06:12,240 Speaker 2: it's mutual, it's a we give and we take in 108 00:06:12,279 --> 00:06:12,719 Speaker 2: that way. 109 00:06:13,000 --> 00:06:18,719 Speaker 1: Yes, it's people telling stories, people sharing news of people 110 00:06:19,000 --> 00:06:23,080 Speaker 1: opening up and being vulnerable and creating art which is 111 00:06:23,120 --> 00:06:27,320 Speaker 1: meant to connect people and so people feel less alone, 112 00:06:27,839 --> 00:06:31,000 Speaker 1: people feel less isolated, they feel heard, they feel seen, 113 00:06:31,480 --> 00:06:35,560 Speaker 1: and I just don't know that artificial technology can do 114 00:06:35,720 --> 00:06:38,480 Speaker 1: that for human beings. I think that we're seeing more 115 00:06:38,520 --> 00:06:42,120 Speaker 1: and more of it, where people are seeking connection, They're 116 00:06:42,160 --> 00:06:45,480 Speaker 1: seeking a therapist through their chatbot, They're seeking a boyfriend 117 00:06:45,960 --> 00:06:49,440 Speaker 1: through their chatbot. I even heard this week at school 118 00:06:49,520 --> 00:06:54,080 Speaker 1: someone pitching a documentary about grief bots that are taking 119 00:06:54,120 --> 00:06:56,640 Speaker 1: like the voices of your deceased loved one and like 120 00:06:56,880 --> 00:07:00,120 Speaker 1: videos and photos to create like a grief bot so 121 00:07:00,120 --> 00:07:04,360 Speaker 1: you can continue to have like contemporary conversations with your 122 00:07:04,360 --> 00:07:09,680 Speaker 1: deceased love one, and so the bots being used as 123 00:07:09,720 --> 00:07:13,640 Speaker 1: a stand in for real human connection. And I think 124 00:07:13,640 --> 00:07:16,760 Speaker 1: that we've seen enough Black Mirror episodes. We've seen, like 125 00:07:16,800 --> 00:07:20,280 Speaker 1: we said, enough movies and TV shows that sort of 126 00:07:20,720 --> 00:07:24,520 Speaker 1: imagine for us what taking this road like where it 127 00:07:24,560 --> 00:07:28,360 Speaker 1: will lead us. And it's this question of art imitating 128 00:07:28,400 --> 00:07:32,840 Speaker 1: life and life imitating art, and technology imitating life. Is 129 00:07:32,880 --> 00:07:36,400 Speaker 1: I think what really we're seeing here, and something we 130 00:07:36,440 --> 00:07:40,560 Speaker 1: talked about before we started recording is that my belief, 131 00:07:40,600 --> 00:07:44,360 Speaker 1: my understanding is that in order to have an AI host, 132 00:07:44,520 --> 00:07:48,239 Speaker 1: that voice needs to come from somewhere, and there's enough 133 00:07:48,280 --> 00:07:52,720 Speaker 1: podcasts and radio shows and music and YouTube videos and 134 00:07:52,760 --> 00:07:55,640 Speaker 1: TikTok videos. There's enough content out there with people's, real 135 00:07:55,680 --> 00:07:59,800 Speaker 1: people's voices. That has to be where the AI bots 136 00:08:00,080 --> 00:08:03,320 Speaker 1: are sourcing their voices and their inflections and their accents from, 137 00:08:03,560 --> 00:08:06,800 Speaker 1: you know, like they have to farm real people's voices. 138 00:08:07,120 --> 00:08:10,600 Speaker 2: Absolutely, And I think, you know, one of the really 139 00:08:10,920 --> 00:08:14,680 Speaker 2: beautiful things and why podcasts have been so successful is 140 00:08:14,680 --> 00:08:19,800 Speaker 2: that it podcasts did democratize what we considered like radio 141 00:08:20,040 --> 00:08:22,760 Speaker 2: host right when we as much as I love MPR, 142 00:08:22,960 --> 00:08:26,360 Speaker 2: it did in its heyday, it did have its moment 143 00:08:26,400 --> 00:08:28,480 Speaker 2: where it was a lot of white hosts and it 144 00:08:28,680 --> 00:08:32,320 Speaker 2: sounded a certain way. It was Ira Glass, right. It 145 00:08:32,440 --> 00:08:36,400 Speaker 2: was these podcast hosts that you know, sounded like professional 146 00:08:36,480 --> 00:08:39,679 Speaker 2: radio hosts, which is fantastic. But then that also meant that, 147 00:08:40,480 --> 00:08:44,160 Speaker 2: depending on where you lived, you probably didn't sound like 148 00:08:44,240 --> 00:08:46,520 Speaker 2: that either, you know, And so one of the beautiful 149 00:08:46,520 --> 00:08:49,480 Speaker 2: things about the podcast is that you could listen to 150 00:08:49,600 --> 00:08:52,600 Speaker 2: folks that sound like you, that have your accent, that 151 00:08:52,640 --> 00:08:56,640 Speaker 2: are from your community, and you can really see that 152 00:08:56,760 --> 00:09:01,120 Speaker 2: representation and you know, not to like minim you know, 153 00:09:01,600 --> 00:09:05,400 Speaker 2: podcast to representation, but that was I think one of 154 00:09:05,440 --> 00:09:09,600 Speaker 2: the ways folks felt really connected at the advent of podcasts, 155 00:09:09,640 --> 00:09:12,719 Speaker 2: diversifying and like this in a way like anyone can 156 00:09:12,760 --> 00:09:15,559 Speaker 2: do it. Yeah, And so I think that that has 157 00:09:15,600 --> 00:09:17,920 Speaker 2: been one of the many reasons that podcasting has been 158 00:09:18,200 --> 00:09:21,520 Speaker 2: so successful and why audiences connect with the hosts that 159 00:09:21,559 --> 00:09:25,319 Speaker 2: they connect with. And to your point, according to the 160 00:09:25,360 --> 00:09:29,479 Speaker 2: Hollywood Reporter, this team is in the midst of navigating 161 00:09:29,520 --> 00:09:34,240 Speaker 2: the ethics around creating AI personalities as the technology advances. 162 00:09:34,720 --> 00:09:38,360 Speaker 2: As of right now, each host identifies themselves as being 163 00:09:38,480 --> 00:09:41,280 Speaker 2: AI at the top of the episode, and they've stayed 164 00:09:41,320 --> 00:09:45,320 Speaker 2: away from having the host invent their quote backstories, but 165 00:09:45,400 --> 00:09:46,480 Speaker 2: that could come. 166 00:09:47,320 --> 00:09:51,360 Speaker 1: Yeah, And the name of this company Inception Point AI. 167 00:09:52,320 --> 00:09:55,920 Speaker 1: They're like basically building a stable of AI talent, again 168 00:09:55,960 --> 00:09:59,559 Speaker 1: from the Hollywood Reporter, and not only to host podcasts, 169 00:09:59,640 --> 00:10:03,520 Speaker 1: but to be come like influencers across social media platforms 170 00:10:04,040 --> 00:10:10,400 Speaker 1: and apparently literature and more. Hosting producing podcast I guess 171 00:10:10,640 --> 00:10:14,640 Speaker 1: is relatively expensive, not more expensive than making a movie 172 00:10:14,760 --> 00:10:17,640 Speaker 1: or a TV show, but you know, you're paying for 173 00:10:17,960 --> 00:10:22,520 Speaker 1: human labor and human talent, and I think that it's 174 00:10:22,720 --> 00:10:26,480 Speaker 1: a scary place where we say it's too expensive to 175 00:10:26,559 --> 00:10:29,120 Speaker 1: pay people for their work and for their art and 176 00:10:29,200 --> 00:10:31,920 Speaker 1: for their originality. So we're just going to replace the people. 177 00:10:32,960 --> 00:10:37,120 Speaker 1: And I think that it's really creepy. You know, we've 178 00:10:37,160 --> 00:10:39,960 Speaker 1: seen here and there, like Okay, there's an there's an 179 00:10:40,000 --> 00:10:47,040 Speaker 1: AI influencer. There's even like yeah, AI profiles on dating apps, 180 00:10:47,200 --> 00:10:51,480 Speaker 1: and there's this intersection of like AI and romance with people. 181 00:10:52,280 --> 00:10:55,600 Speaker 1: That movie with Joaquin Phoenix her, you know, and it's 182 00:10:55,600 --> 00:10:58,560 Speaker 1: not the only one. And it's just very eerie. And 183 00:10:58,800 --> 00:11:02,199 Speaker 1: I also think it speaks to like something that has 184 00:11:02,240 --> 00:11:05,080 Speaker 1: been talked about out there, this like loneliness epidemic, like 185 00:11:05,120 --> 00:11:10,200 Speaker 1: people are very lonely, and so instead of somehow connecting 186 00:11:10,200 --> 00:11:14,120 Speaker 1: with other human beings, Well, the thing that's most accessible 187 00:11:14,160 --> 00:11:16,760 Speaker 1: and closest and at our fingertips is the is the phone, 188 00:11:16,840 --> 00:11:17,480 Speaker 1: is the computer. 189 00:11:18,280 --> 00:11:19,719 Speaker 2: Don't go anywhere, locomotives. 190 00:11:19,840 --> 00:11:26,400 Speaker 1: We'll be right back, and we're back with more of 191 00:11:26,440 --> 00:11:27,439 Speaker 1: our episode. 192 00:11:27,960 --> 00:11:31,720 Speaker 2: Since launching, you know, they are producing more than three 193 00:11:31,720 --> 00:11:35,560 Speaker 2: thousand episodes a week, and I think what that also 194 00:11:36,600 --> 00:11:39,360 Speaker 2: is what we should also consider is that it's then 195 00:11:40,040 --> 00:11:44,920 Speaker 2: flooding the media landscape. Yeah, and they've already seen ten 196 00:11:44,960 --> 00:11:50,080 Speaker 2: million downloads since September twenty twenty three, so that means 197 00:11:50,120 --> 00:11:53,120 Speaker 2: there is an audience for it, you know, as and 198 00:11:53,160 --> 00:11:57,080 Speaker 2: so I'm super curious as to who is listening to 199 00:11:57,240 --> 00:12:00,920 Speaker 2: these AI generated podcasts and what it is that they 200 00:12:01,080 --> 00:12:04,959 Speaker 2: enjoy about it, because clearly, as of now, the host 201 00:12:05,040 --> 00:12:08,560 Speaker 2: is identifying themselves as AI, so it's not a trick, right, 202 00:12:08,640 --> 00:12:11,199 Speaker 2: the listener knows that it's an AI podcast. So I'm 203 00:12:11,240 --> 00:12:16,199 Speaker 2: really curious as to what about it makes it desirable 204 00:12:16,240 --> 00:12:18,800 Speaker 2: to listen to. That is definitely not something I would 205 00:12:18,880 --> 00:12:22,040 Speaker 2: seek out, But clearly there is an audience, and we 206 00:12:22,080 --> 00:12:24,320 Speaker 2: can say that about all of the AI that we're seeing. 207 00:12:24,440 --> 00:12:27,920 Speaker 2: Like as much as we have our qualms, our reservations, 208 00:12:28,000 --> 00:12:31,040 Speaker 2: our maybe our own personal ethics around using AI, it 209 00:12:31,120 --> 00:12:33,560 Speaker 2: is still being used. And I think in this case, 210 00:12:33,760 --> 00:12:37,160 Speaker 2: clearly we're seeing the podcast are still being listened to. 211 00:12:38,000 --> 00:12:43,200 Speaker 2: And this startup is just to note, like the employees 212 00:12:43,240 --> 00:12:45,480 Speaker 2: are not yet salaried, they're not getting paid yet. So 213 00:12:45,600 --> 00:12:49,640 Speaker 2: the small team that's making these AI podcasts are still 214 00:12:49,679 --> 00:12:52,240 Speaker 2: not getting paid for their own labor. Interesting and that 215 00:12:52,400 --> 00:12:54,400 Speaker 2: it to me is incredibly ironic. 216 00:12:54,679 --> 00:12:57,800 Speaker 1: Very ironic. So then eventually they're going to be aied 217 00:12:57,840 --> 00:12:59,679 Speaker 1: out of their own startum job. 218 00:13:00,160 --> 00:13:03,560 Speaker 2: Yeah, the startup is currently seeking outside funding. That is 219 00:13:03,600 --> 00:13:06,640 Speaker 2: normal for that's what a startup does. That's that is 220 00:13:06,679 --> 00:13:10,200 Speaker 2: not outrageous. But I think the irony is still there 221 00:13:10,760 --> 00:13:13,920 Speaker 2: that the folks that are working are making these podcasts, 222 00:13:13,920 --> 00:13:17,000 Speaker 2: scripting it through an AI tool, however they're editing it, 223 00:13:17,360 --> 00:13:19,880 Speaker 2: and then they're not getting paid for it either. 224 00:13:20,160 --> 00:13:24,440 Speaker 1: Hmmm, they have to So it's it's they have to 225 00:13:24,440 --> 00:13:27,840 Speaker 1: be getting something out of it or they see uh, 226 00:13:27,880 --> 00:13:34,440 Speaker 1: some payoff in the future, and yeah, strange. 227 00:13:34,480 --> 00:13:39,000 Speaker 2: Yeah, yeah, and it's it's been it to me based 228 00:13:39,040 --> 00:13:41,240 Speaker 2: on what everything I've read in this Hollywood Reporter and 229 00:13:41,240 --> 00:13:43,680 Speaker 2: based on the chatter that I've seen online from my 230 00:13:44,240 --> 00:13:47,680 Speaker 2: creative circle, you know, it seems like the point of 231 00:13:47,800 --> 00:13:51,760 Speaker 2: this business model is just profit. It's not storytelling, it's 232 00:13:51,840 --> 00:13:54,840 Speaker 2: not connection, it's profit. And so I think that's also 233 00:13:54,880 --> 00:13:57,440 Speaker 2: something to keep in mind. Is that the kind of 234 00:13:57,440 --> 00:13:58,360 Speaker 2: thing you want to support? 235 00:13:58,760 --> 00:14:04,880 Speaker 1: You know, it's also scary. You know, you're a fan 236 00:14:04,960 --> 00:14:08,000 Speaker 1: of someone, you're a follower, you're a listener, you're a subscriber, 237 00:14:09,040 --> 00:14:12,960 Speaker 1: whether it's a recording artist, whether it's a podcast host, 238 00:14:13,000 --> 00:14:17,920 Speaker 1: whether it's an influencer, a creative of some kind. Nobody 239 00:14:17,960 --> 00:14:21,320 Speaker 1: is perfect, right, and quite often the people that you 240 00:14:21,480 --> 00:14:23,800 Speaker 1: are listening to are going to disappoint you at one 241 00:14:23,880 --> 00:14:26,320 Speaker 1: time or another, or let you down, or they're not 242 00:14:26,360 --> 00:14:28,600 Speaker 1: as accessible as you want them to be. And I 243 00:14:28,680 --> 00:14:32,000 Speaker 1: think that like an AI host can be tailored to 244 00:14:32,080 --> 00:14:35,440 Speaker 1: be perfect for you, right, and to never disappoint you 245 00:14:35,480 --> 00:14:37,640 Speaker 1: and never let you down and always show up and 246 00:14:37,720 --> 00:14:40,440 Speaker 1: always give you what you're looking for, where like humans 247 00:14:40,520 --> 00:14:42,960 Speaker 1: just can't do that, you know. And I think that 248 00:14:43,000 --> 00:14:44,920 Speaker 1: we've talked about this on the show before, but there's 249 00:14:45,000 --> 00:14:48,120 Speaker 1: kind of a rage cycle, especially in the online space. 250 00:14:49,480 --> 00:14:53,160 Speaker 1: Audiences get mad at their favorite influencer. You know, People 251 00:14:53,400 --> 00:14:56,680 Speaker 1: fall off, people get called out, people get dragged. There's 252 00:14:57,600 --> 00:15:03,760 Speaker 1: this human tendency to falter or to fail and to disappoint. 253 00:15:03,880 --> 00:15:07,000 Speaker 1: It's just part of the human experience because nobody is perfect. 254 00:15:07,480 --> 00:15:13,000 Speaker 1: But something that is programmed like this could analyze so 255 00:15:13,120 --> 00:15:15,680 Speaker 1: much data that it can be tailored to be as 256 00:15:15,680 --> 00:15:21,600 Speaker 1: close to perfect for you as possible. That is humanly impossible, right, 257 00:15:21,880 --> 00:15:25,120 Speaker 1: And that is something that I think is maybe enticing 258 00:15:25,240 --> 00:15:27,960 Speaker 1: for people because it's always going to give you what 259 00:15:28,000 --> 00:15:31,120 Speaker 1: you want. It's never going to disappoint you because it 260 00:15:31,240 --> 00:15:38,480 Speaker 1: knows all your keystrokes. But it's like that I think 261 00:15:38,560 --> 00:15:41,040 Speaker 1: is very scary. That I think is very scary. 262 00:15:41,520 --> 00:15:44,120 Speaker 2: I think that that is an excellent pointment because it 263 00:15:44,960 --> 00:15:50,440 Speaker 2: shows us that what we're looking for is perfection, perhaps 264 00:15:50,760 --> 00:15:57,880 Speaker 2: from our podcast host, influencer, social media personality therapists, therapist 265 00:15:57,920 --> 00:16:00,560 Speaker 2: even yes, and you know, to be the human is 266 00:16:00,600 --> 00:16:03,000 Speaker 2: to be imperfect, not to be trite. But this is 267 00:16:03,000 --> 00:16:04,920 Speaker 2: how we learn and we grow and we get better 268 00:16:05,760 --> 00:16:08,920 Speaker 2: is by the human error and mistake, you know. 269 00:16:08,920 --> 00:16:12,560 Speaker 1: And learning from it and changing. Change is like the 270 00:16:12,600 --> 00:16:19,480 Speaker 1: only constant in life is time continues, Things change, people change, 271 00:16:19,520 --> 00:16:24,920 Speaker 1: seasons change, change is the only constant. And AI might 272 00:16:25,040 --> 00:16:28,360 Speaker 1: study all of us, you know, and learn from us 273 00:16:28,520 --> 00:16:31,720 Speaker 1: to like feed us and give us exactly what it 274 00:16:31,760 --> 00:16:35,680 Speaker 1: thinks we want, you know. But then like but then 275 00:16:35,800 --> 00:16:39,120 Speaker 1: like if we're not changing, then does the AI change? 276 00:16:39,440 --> 00:16:40,400 Speaker 1: And then where do we go? 277 00:16:40,640 --> 00:16:43,760 Speaker 2: Yeah? Well, I think it just like in my mind, 278 00:16:43,800 --> 00:16:47,200 Speaker 2: AI just like teaches itself. It like continues to learn 279 00:16:47,960 --> 00:16:50,520 Speaker 2: from what you've fed it, so it will like evolve 280 00:16:50,560 --> 00:16:53,000 Speaker 2: beyond you, which I think is what every sci fi 281 00:16:53,120 --> 00:16:56,760 Speaker 2: film and book has told us. And I think, you know, 282 00:16:56,840 --> 00:16:59,600 Speaker 2: when we think about I'm thinking historically like novels, right, 283 00:17:00,080 --> 00:17:02,760 Speaker 2: where there has been some type of resistance to AI, 284 00:17:03,120 --> 00:17:05,640 Speaker 2: and yes there's resistance now, but there's also a lot 285 00:17:05,640 --> 00:17:08,720 Speaker 2: of opting in, and there's also a lot of forced 286 00:17:08,840 --> 00:17:12,280 Speaker 2: opt in. I think about now when I use Google, Yes, 287 00:17:12,560 --> 00:17:14,720 Speaker 2: you know you have to in order to not get 288 00:17:14,760 --> 00:17:19,080 Speaker 2: an AI generated summary, you have to put minus or AI, 289 00:17:19,840 --> 00:17:22,560 Speaker 2: and so you don't even get the option to opt 290 00:17:22,560 --> 00:17:24,960 Speaker 2: out anymore. You have to manually opt out. And I'm 291 00:17:25,000 --> 00:17:27,720 Speaker 2: seeing that for a lot a lot more programs these days, 292 00:17:28,080 --> 00:17:31,640 Speaker 2: whether that be Canva, whether that be Adobe or Photoshop, 293 00:17:31,880 --> 00:17:34,960 Speaker 2: I'm seeing more and more AI plugins, you know, which 294 00:17:35,240 --> 00:17:38,320 Speaker 2: you can't opt into those, but some like you can't 295 00:17:38,520 --> 00:17:41,760 Speaker 2: opt out of even like with Google and Gemini, you know, 296 00:17:41,880 --> 00:17:45,520 Speaker 2: even on your email. And so I feel like this 297 00:17:45,640 --> 00:17:49,520 Speaker 2: also ties really well the use of AI into the 298 00:17:49,600 --> 00:17:53,000 Speaker 2: surveillance state. We are so willing to opt in to 299 00:17:53,359 --> 00:17:56,280 Speaker 2: the surveillance state, whether that be through like self surveilling 300 00:17:56,320 --> 00:18:01,399 Speaker 2: and documenting everything we do, to training a large language 301 00:18:01,400 --> 00:18:06,439 Speaker 2: model like CHADGBT or an AI bot. We're training it 302 00:18:06,480 --> 00:18:10,000 Speaker 2: to think like us, like ourselves. And so I do 303 00:18:10,040 --> 00:18:14,600 Speaker 2: feel like that laps into like the surveillance state and 304 00:18:14,640 --> 00:18:16,960 Speaker 2: how this technology can be used against us. 305 00:18:17,359 --> 00:18:21,720 Speaker 1: Oh absolutely. I mean, if you're using your phone and 306 00:18:21,760 --> 00:18:25,680 Speaker 1: you're using any app at all, the app always asks you, oh, 307 00:18:25,680 --> 00:18:29,080 Speaker 1: can it track your location at all times? And you 308 00:18:29,160 --> 00:18:31,840 Speaker 1: have to say yes or no? But then sometimes but 309 00:18:31,880 --> 00:18:33,400 Speaker 1: then for the app to work, you have to say 310 00:18:33,480 --> 00:18:35,399 Speaker 1: yes or else. Why do you have the app? 311 00:18:35,600 --> 00:18:35,800 Speaker 2: Right? 312 00:18:36,080 --> 00:18:38,960 Speaker 1: You know? And so there's somebody somewhere who always knows 313 00:18:39,040 --> 00:18:42,760 Speaker 1: or you are scary. It's very scary. It's very scary. 314 00:18:42,960 --> 00:18:45,359 Speaker 1: And you know, we were talking about like movies and 315 00:18:45,480 --> 00:18:50,520 Speaker 1: film that have sort of predicted these things. Isaac Asimov wrote, 316 00:18:50,680 --> 00:18:54,280 Speaker 1: I Robot in nineteen fifty, you know, like cell phones 317 00:18:54,280 --> 00:18:57,359 Speaker 1: were not invented, then the Internet was not around, but 318 00:18:57,680 --> 00:19:02,399 Speaker 1: in less than one hundred years later, like here we are. Yeah, 319 00:19:02,440 --> 00:19:06,640 Speaker 1: and it tells us a lot about how quickly, how 320 00:19:06,720 --> 00:19:10,679 Speaker 1: quickly the technology can develop and then how exponentially it 321 00:19:10,680 --> 00:19:13,399 Speaker 1: can grow and then like impact us because that's a 322 00:19:13,520 --> 00:19:14,560 Speaker 1: very quick turnaround. 323 00:19:14,760 --> 00:19:16,200 Speaker 2: Don't go anywhere, look amotives. 324 00:19:16,320 --> 00:19:22,600 Speaker 1: We'll be right back, and we're back with more of 325 00:19:22,640 --> 00:19:24,040 Speaker 1: our episode. 326 00:19:24,160 --> 00:19:25,920 Speaker 2: For me, my point of reference is always fair onhead 327 00:19:25,960 --> 00:19:27,840 Speaker 2: four fifty one. That book changed me in middle school, 328 00:19:28,000 --> 00:19:29,960 Speaker 2: you know, and I have it by my nights stand 329 00:19:29,960 --> 00:19:32,520 Speaker 2: now because I'm like going back to it here and there. 330 00:19:32,560 --> 00:19:35,040 Speaker 2: I'm like, this is a time to reread it. And 331 00:19:35,320 --> 00:19:40,680 Speaker 2: you know, there was this resistance right by the narrator 332 00:19:40,840 --> 00:19:43,640 Speaker 2: and wanting to uncover you know, wait, what's really going 333 00:19:43,680 --> 00:19:46,879 Speaker 2: on in this reality? Here in my reality? And I 334 00:19:47,000 --> 00:19:49,560 Speaker 2: feel that this is not to say there's no resistance 335 00:19:49,600 --> 00:19:52,000 Speaker 2: to AI. Of course that's that's not the case. But 336 00:19:52,760 --> 00:19:57,840 Speaker 2: it also feels like we are being forced to use AI, 337 00:19:58,240 --> 00:20:01,159 Speaker 2: whether we agree with it or not, in terms of 338 00:20:01,240 --> 00:20:05,720 Speaker 2: labor and jobs and like staying relevant where even if 339 00:20:05,720 --> 00:20:08,800 Speaker 2: we ethically don't agree, it's like, do you know, like 340 00:20:08,840 --> 00:20:10,720 Speaker 2: you mentioned the other day, do you know how to 341 00:20:10,840 --> 00:20:14,200 Speaker 2: use AI? Is being asked in your in your classes. 342 00:20:14,440 --> 00:20:17,480 Speaker 1: Yeah, there's a at the School of Cinematic Arts, there's 343 00:20:17,520 --> 00:20:21,960 Speaker 1: a directing AI class, And I've seen some of the 344 00:20:21,960 --> 00:20:25,320 Speaker 1: projects and it looks like it's shot on location in 345 00:20:25,359 --> 00:20:29,080 Speaker 1: Italy with human actors. You have you have to really 346 00:20:29,119 --> 00:20:31,680 Speaker 1: really have a very trained eye to see that it's 347 00:20:32,119 --> 00:20:34,840 Speaker 1: it's AI generated. And that's the thing is the technology 348 00:20:34,880 --> 00:20:39,080 Speaker 1: is only going to get better, right, and it's there 349 00:20:39,200 --> 00:20:42,280 Speaker 1: is AI that has been incorporated into filmmaking for some 350 00:20:42,440 --> 00:20:46,200 Speaker 1: time now. This is just like a whole new level. 351 00:20:46,600 --> 00:20:48,240 Speaker 1: And the thing is, like the school would not be 352 00:20:48,280 --> 00:20:52,119 Speaker 1: incorporating this sort of class if there wasn't a very real, 353 00:20:52,960 --> 00:20:58,719 Speaker 1: uh reality in the job market post graduation that this 354 00:20:58,840 --> 00:21:01,280 Speaker 1: is a necessary tool, like a necessary skill that you 355 00:21:01,320 --> 00:21:04,840 Speaker 1: should learn. And I know that there's been different thoughts 356 00:21:04,880 --> 00:21:07,840 Speaker 1: about like, well, in the right hands, it can be 357 00:21:07,880 --> 00:21:14,119 Speaker 1: a democratizing tool, right, Like you can use an AI 358 00:21:14,280 --> 00:21:19,919 Speaker 1: tool to basically replace like a test shoot day, and 359 00:21:20,000 --> 00:21:24,440 Speaker 1: you can pre light things and figure out your location 360 00:21:24,680 --> 00:21:29,240 Speaker 1: and your blocking and your camera placement without leaving your desk, 361 00:21:29,800 --> 00:21:32,520 Speaker 1: whereas to do that in person, it might cost you 362 00:21:32,560 --> 00:21:35,439 Speaker 1: ten thousand dollars, you know, just to do a test. 363 00:21:36,160 --> 00:21:39,200 Speaker 1: And in some ways, like is that going to allow 364 00:21:39,800 --> 00:21:44,840 Speaker 1: people from underresourced backgrounds to like break into an industry 365 00:21:44,920 --> 00:21:49,280 Speaker 1: that is otherwise like really expensive to break into. And 366 00:21:49,280 --> 00:21:52,880 Speaker 1: one of the greatest barriers into filmmaking is money, you know, 367 00:21:53,200 --> 00:21:55,679 Speaker 1: That's really the thing that keeps people out. It's just 368 00:21:55,760 --> 00:21:59,960 Speaker 1: so expensive to make movies. So there are these like 369 00:22:00,280 --> 00:22:04,359 Speaker 1: competing schools of thought. But then at the same time, 370 00:22:05,440 --> 00:22:08,479 Speaker 1: if you can replace a whole lighting team, you know, 371 00:22:08,800 --> 00:22:13,919 Speaker 1: that means you're replacing like tons of skilled workers. And 372 00:22:14,320 --> 00:22:15,960 Speaker 1: that's also not good either. 373 00:22:16,400 --> 00:22:19,840 Speaker 2: And this is at the tale of the strikes. 374 00:22:19,480 --> 00:22:21,920 Speaker 1: At the tale of yes, you know, like this is. 375 00:22:21,760 --> 00:22:25,560 Speaker 2: This I wonder if this is also maybe not that 376 00:22:25,640 --> 00:22:30,359 Speaker 2: the strikes caused it, but as a way to remove 377 00:22:30,680 --> 00:22:36,320 Speaker 2: like the human labor in a very horrible way, retaliation 378 00:22:36,560 --> 00:22:38,639 Speaker 2: of it. This is this is I'm not basing this 379 00:22:38,680 --> 00:22:41,240 Speaker 2: off of anything. I'm just wondering, Okay, this is you know, 380 00:22:41,800 --> 00:22:48,439 Speaker 2: a horrible retaliation because of you know, humans asking for 381 00:22:48,520 --> 00:22:53,159 Speaker 2: better wages and protection and you know, respect for their labor. 382 00:22:53,760 --> 00:22:58,080 Speaker 1: Yeah. And and this being you know, union labor, unionized labor, 383 00:22:58,760 --> 00:23:02,399 Speaker 1: and so I can see how the execs and the 384 00:23:02,440 --> 00:23:05,560 Speaker 1: powers it be in the funders instead of playing ball 385 00:23:05,600 --> 00:23:09,199 Speaker 1: with the unions, well, we're just gonna there's just not 386 00:23:09,200 --> 00:23:11,720 Speaker 1: going to be any more work for you, right, And 387 00:23:12,520 --> 00:23:15,200 Speaker 1: I think that the history of labor in this country 388 00:23:16,040 --> 00:23:20,160 Speaker 1: tells us that that's not too much of a reach, right, 389 00:23:20,280 --> 00:23:21,120 Speaker 1: that theory. 390 00:23:21,160 --> 00:23:25,879 Speaker 2: Right, Yeah, And it's it's horrible. I mean, it's a 391 00:23:25,880 --> 00:23:29,320 Speaker 2: horrible thing to consider that, you know, labor and humans 392 00:23:29,359 --> 00:23:32,680 Speaker 2: are so devalued, you know. I think about that even 393 00:23:32,760 --> 00:23:35,520 Speaker 2: in terms of you know, when I go to the 394 00:23:35,560 --> 00:23:38,240 Speaker 2: grocery store, and now there's like self checkout, and that 395 00:23:38,320 --> 00:23:42,399 Speaker 2: seems fine and dandy, but that means there's less cashiers 396 00:23:41,800 --> 00:23:46,439 Speaker 2: on the clock, you know, and the labor is then 397 00:23:46,480 --> 00:23:49,080 Speaker 2: put on us to check out ourselves. And it's not 398 00:23:49,119 --> 00:23:51,520 Speaker 2: that I'm above labor. I'm not above doing it myself. 399 00:23:51,880 --> 00:23:54,480 Speaker 2: But there's a person that could be getting paid a 400 00:23:54,640 --> 00:23:57,959 Speaker 2: union worker at that yeah, to be working this shift. 401 00:23:58,080 --> 00:24:01,439 Speaker 2: My mother was a union worker cashier at Kroger for 402 00:24:01,480 --> 00:24:04,640 Speaker 2: twenty plus years and she does not like the self 403 00:24:04,720 --> 00:24:07,720 Speaker 2: checkout right because she sees it as like this, this 404 00:24:07,920 --> 00:24:10,119 Speaker 2: labor is now on me. Even when we go to 405 00:24:10,240 --> 00:24:13,959 Speaker 2: a restaurant and we have to order ourselves on our 406 00:24:14,000 --> 00:24:17,400 Speaker 2: phone and like the QR code crazy, I'm like, that 407 00:24:17,920 --> 00:24:19,880 Speaker 2: is now labor being put on me. And the person 408 00:24:19,880 --> 00:24:23,080 Speaker 2: who pointed that out to me is labor journalist and 409 00:24:23,160 --> 00:24:26,760 Speaker 2: my homegirl from USC siek Lali, because we went to 410 00:24:26,840 --> 00:24:28,800 Speaker 2: have a drink with our class and we were still 411 00:24:28,840 --> 00:24:31,359 Speaker 2: in grad school and she was the one that pointed 412 00:24:31,359 --> 00:24:32,879 Speaker 2: it out. She's like, now the labor's on you to 413 00:24:32,920 --> 00:24:35,359 Speaker 2: put in your order. And since she told that to 414 00:24:35,400 --> 00:24:37,919 Speaker 2: me years ago, I have not unseen it. Yes, And 415 00:24:37,960 --> 00:24:40,520 Speaker 2: this is again not that anyone is above doing labor. 416 00:24:41,000 --> 00:24:44,119 Speaker 2: But now there is a person who is not getting 417 00:24:44,200 --> 00:24:46,399 Speaker 2: paid to take your order. 418 00:24:46,760 --> 00:24:52,479 Speaker 1: Yeah, that's an entire household. You know that In a 419 00:24:52,480 --> 00:24:56,879 Speaker 1: previous version of America, like a person could feed themselves 420 00:24:56,920 --> 00:25:00,280 Speaker 1: and pay their bills and like take care of their kids. 421 00:25:00,800 --> 00:25:06,320 Speaker 1: With a union job like that, working as a cashier, 422 00:25:07,280 --> 00:25:10,280 Speaker 1: you know, a person could sustain themselves. And these jobs 423 00:25:10,280 --> 00:25:13,600 Speaker 1: still exist, but there we're seeing them slowly being chipped 424 00:25:13,600 --> 00:25:15,919 Speaker 1: away at. There's not as many as there used to be, 425 00:25:16,800 --> 00:25:20,880 Speaker 1: you know, And then we wonder why are people struggling well, 426 00:25:20,920 --> 00:25:23,760 Speaker 1: because we are just eliminating jobs left and right and 427 00:25:23,840 --> 00:25:27,000 Speaker 1: not replacing them. It would be one thing if these 428 00:25:27,080 --> 00:25:31,040 Speaker 1: jobs went away, but we're creating more jobs. But we're 429 00:25:31,080 --> 00:25:34,240 Speaker 1: not creating more jobs. We're simply hacking away at the jobs. 430 00:25:34,400 --> 00:25:37,040 Speaker 2: This is an argument not to be tangential, but this 431 00:25:37,080 --> 00:25:39,320 Speaker 2: is an argument to why we need universal income. 432 00:25:39,680 --> 00:25:41,480 Speaker 1: Yes we do, universal basic income. 433 00:25:41,640 --> 00:25:43,800 Speaker 2: Universal basic income is what we need. 434 00:25:43,880 --> 00:25:46,080 Speaker 1: If you're not going to let people work, how are 435 00:25:46,119 --> 00:25:49,159 Speaker 1: people supposed to survive? Yes, there has to be something 436 00:25:49,160 --> 00:25:50,800 Speaker 1: else in place. 437 00:25:50,680 --> 00:25:53,240 Speaker 2: I mean, and it ties to AI in some way. 438 00:25:53,280 --> 00:25:55,400 Speaker 2: I'm going to get us there. Because if you think 439 00:25:55,440 --> 00:25:58,960 Speaker 2: about if everyone had access to universal basic income, you 440 00:25:59,000 --> 00:26:02,159 Speaker 2: could probably be away working living artists. Yeah, if you 441 00:26:02,200 --> 00:26:04,480 Speaker 2: could cover your rent, yeah, and there would probably be 442 00:26:04,520 --> 00:26:05,639 Speaker 2: more people making art. 443 00:26:05,800 --> 00:26:10,000 Speaker 1: Yeah yeah, and even folks with jobs, Like we're learning 444 00:26:10,000 --> 00:26:13,639 Speaker 1: that folks with jobs, folks who are homeowners but who 445 00:26:13,720 --> 00:26:19,040 Speaker 1: live near these data centers are also struggling because in 446 00:26:19,160 --> 00:26:21,919 Speaker 1: order to house all of this data, because this is 447 00:26:22,000 --> 00:26:24,560 Speaker 1: like data heavy, there's a lot of information and it 448 00:26:24,600 --> 00:26:28,399 Speaker 1: has to go somewhere. There is a physical location where 449 00:26:28,440 --> 00:26:31,920 Speaker 1: all this AI code and information has to be stored 450 00:26:32,160 --> 00:26:36,160 Speaker 1: in big old computers, and there is a true environmental 451 00:26:36,200 --> 00:26:40,320 Speaker 1: cost to it. And something that we're learning is that 452 00:26:40,600 --> 00:26:44,919 Speaker 1: people who live next to data centers are running out 453 00:26:44,920 --> 00:26:48,960 Speaker 1: of water. They don't have running water anymore, Their water 454 00:26:49,119 --> 00:26:53,359 Speaker 1: is polluted visibly, and there is like a very real 455 00:26:53,560 --> 00:26:59,080 Speaker 1: toll on again, just working people, even homeowners. And usually 456 00:26:59,119 --> 00:27:05,119 Speaker 1: we think about environmental degradation and like urban communities, communities 457 00:27:05,119 --> 00:27:08,480 Speaker 1: of color, right, who live near airports, who live near 458 00:27:09,320 --> 00:27:13,159 Speaker 1: oil manufacturing plants. This is a whole new wave. 459 00:27:13,840 --> 00:27:18,600 Speaker 2: It adds another layer to environmental racism because it's we 460 00:27:18,720 --> 00:27:21,280 Speaker 2: know that they are not going into affluent communities and 461 00:27:21,320 --> 00:27:24,320 Speaker 2: building data centers, right, we know that they're going to 462 00:27:24,400 --> 00:27:29,080 Speaker 2: go to either rural, low income neighborhoods and build their 463 00:27:29,119 --> 00:27:33,240 Speaker 2: data centers there, and so that is also something to consider. 464 00:27:33,440 --> 00:27:37,200 Speaker 2: And it's not just water that these AI data centers 465 00:27:37,240 --> 00:27:40,040 Speaker 2: are using, but it's also drinking water. So we are 466 00:27:40,119 --> 00:27:46,040 Speaker 2: quite literally sacrificing our drinking water for these AI data centers. 467 00:27:46,280 --> 00:27:49,479 Speaker 2: And it might feel like there's an endless supply of water, 468 00:27:50,240 --> 00:27:53,720 Speaker 2: but there's not. And we are not the only nation 469 00:27:54,280 --> 00:27:58,160 Speaker 2: that struggles with water and access to clean water. 470 00:27:58,920 --> 00:28:02,040 Speaker 1: It's very scary. I know, it's a very scary thing. 471 00:28:02,080 --> 00:28:04,720 Speaker 1: You guys. I think about this all the time. 472 00:28:04,960 --> 00:28:07,239 Speaker 2: Oh malla does think about running out of water? You 473 00:28:07,280 --> 00:28:10,879 Speaker 2: do all this your fear? Yeah, I forgot about that 474 00:28:10,920 --> 00:28:12,000 Speaker 2: when I said it. I'm so sorry. 475 00:28:12,000 --> 00:28:14,560 Speaker 1: No, it's okay, but it's true, especially in LA. My 476 00:28:14,800 --> 00:28:17,360 Speaker 1: partner asked me once, is there something that like like 477 00:28:17,440 --> 00:28:19,840 Speaker 1: that you think about that bothers you? And I said yes, 478 00:28:19,920 --> 00:28:22,400 Speaker 1: that there's going to be a natural disaster in Los Angeles. 479 00:28:22,440 --> 00:28:23,960 Speaker 1: We're going to be cut off from our water supply 480 00:28:24,200 --> 00:28:25,520 Speaker 1: and we're going to run out of water in like 481 00:28:25,520 --> 00:28:27,840 Speaker 1: twenty four hours, and all hell is going to break loose, 482 00:28:28,400 --> 00:28:32,560 Speaker 1: and it's going to be chaos. It's going to be 483 00:28:32,560 --> 00:28:34,960 Speaker 1: really hard to get out of here. It's going to 484 00:28:35,040 --> 00:28:39,320 Speaker 1: be impossible to find like life sustaining supplies like water. 485 00:28:39,400 --> 00:28:42,120 Speaker 1: Everything's going to go off the shelves immediately, and we 486 00:28:42,240 --> 00:28:44,760 Speaker 1: have no other source of clean water. We just don't. 487 00:28:45,120 --> 00:28:47,800 Speaker 2: This is like your version of doomsday prepping. You're thinking 488 00:28:47,840 --> 00:28:48,760 Speaker 2: about the water. 489 00:28:48,680 --> 00:28:52,160 Speaker 1: Yes, I'm thinking about the water. And if the water, 490 00:28:52,240 --> 00:28:55,239 Speaker 1: the drinking water is being used in AI centers and 491 00:28:55,280 --> 00:29:01,560 Speaker 1: not in like accessible facilities for catastrophee, you know, then 492 00:29:01,640 --> 00:29:03,840 Speaker 1: where do where do we go? Where do we get it? 493 00:29:04,360 --> 00:29:07,840 Speaker 1: Because there is a finite supply and we can't treat 494 00:29:07,920 --> 00:29:11,040 Speaker 1: water ourselves. We don't have the tools, we don't have 495 00:29:11,120 --> 00:29:13,479 Speaker 1: the mechanisms, we don't have the chemicals, we don't have 496 00:29:13,520 --> 00:29:16,760 Speaker 1: the facilities. Like it's a whole thing to treat water 497 00:29:16,800 --> 00:29:19,040 Speaker 1: so that it's pottable and drinkable, and we just can't 498 00:29:19,120 --> 00:29:19,880 Speaker 1: do it ourselves. 499 00:29:19,960 --> 00:29:25,040 Speaker 2: There are efforts to treat recycled water, especially in La, 500 00:29:25,560 --> 00:29:28,160 Speaker 2: in the city of La, but I don't think that 501 00:29:28,200 --> 00:29:30,320 Speaker 2: we're there yet, but there are efforts being made. But 502 00:29:30,560 --> 00:29:33,000 Speaker 2: it is a real anxiety, especially as we add AI 503 00:29:33,480 --> 00:29:37,440 Speaker 2: to the conversation and the way it's being used like 504 00:29:37,760 --> 00:29:40,200 Speaker 2: a search engine. Right, it's not just a tool, it's 505 00:29:40,240 --> 00:29:42,200 Speaker 2: a search engine, which is what we talked about in 506 00:29:42,240 --> 00:29:44,920 Speaker 2: a previous episode, you know. And so I think that 507 00:29:44,960 --> 00:29:48,400 Speaker 2: there's an overuse of AI when we're using it in 508 00:29:48,400 --> 00:29:52,160 Speaker 2: that way of it being a search engine versus hey, 509 00:29:52,160 --> 00:29:54,280 Speaker 2: can you I'm going to run this through and give 510 00:29:54,320 --> 00:29:56,640 Speaker 2: me some feedback. You know, however it works, you know, 511 00:29:56,720 --> 00:29:59,560 Speaker 2: it's when it's being used I think it to be 512 00:29:59,600 --> 00:30:03,479 Speaker 2: your you're all in one everything, you know, your tutor, 513 00:30:03,600 --> 00:30:07,560 Speaker 2: your therapist, your friend, your partner. You know, it's that 514 00:30:07,840 --> 00:30:10,960 Speaker 2: means you're running it all the time. Yes, the way 515 00:30:11,040 --> 00:30:13,120 Speaker 2: we need connection all the time. 516 00:30:13,200 --> 00:30:19,000 Speaker 1: All the time. And in the Washington Post there is 517 00:30:19,040 --> 00:30:25,480 Speaker 1: this quote, there's a resistance against infrastructures advancing AI a quote. Generally, 518 00:30:25,520 --> 00:30:28,280 Speaker 1: it's important to note that data centers increasingly are becoming 519 00:30:28,280 --> 00:30:32,040 Speaker 1: critical necessary infrastructure to meet the growing needs of our 520 00:30:32,080 --> 00:30:35,760 Speaker 1: connected digital world. Data centers also bring a multitude of 521 00:30:35,760 --> 00:30:39,760 Speaker 1: benefits to communities big and small. The data center industry 522 00:30:39,880 --> 00:30:42,160 Speaker 1: is in growth mode, and every place they try to 523 00:30:42,160 --> 00:30:44,760 Speaker 1: put one, there's probably going to be resistance. The more 524 00:30:44,800 --> 00:30:48,520 Speaker 1: places they put them, the more resistance will spread. And 525 00:30:49,480 --> 00:30:52,959 Speaker 1: you know, so, I just we have to hope that 526 00:30:54,240 --> 00:30:56,920 Speaker 1: people are going to resist, that people continue to resist 527 00:30:57,240 --> 00:31:00,960 Speaker 1: for our own self preservation as a society, as a society. 528 00:31:01,280 --> 00:31:04,960 Speaker 1: But what that also means there has to be some regulation, 529 00:31:05,120 --> 00:31:10,720 Speaker 1: legislative regulation, some cap to how big this thing can get, 530 00:31:10,760 --> 00:31:13,880 Speaker 1: how much it can grow, because only if there's a 531 00:31:13,920 --> 00:31:16,600 Speaker 1: cap will there be a cap on the number of 532 00:31:16,680 --> 00:31:18,000 Speaker 1: data centers that are built. 533 00:31:18,200 --> 00:31:25,719 Speaker 2: Absolutely so, when thinking about necessary regulation and infrastructure and 534 00:31:25,880 --> 00:31:33,640 Speaker 2: policy to protect us from overuse of overconstruction of data 535 00:31:33,640 --> 00:31:39,400 Speaker 2: centers and AI, we actually learned that the White House 536 00:31:40,080 --> 00:31:44,160 Speaker 2: and the Administration has a website called AI dot gov 537 00:31:44,400 --> 00:31:48,920 Speaker 2: where we learned there's actually plans to accelerate federal permitting 538 00:31:49,080 --> 00:31:52,480 Speaker 2: of data center infrastructure. So quite the opposite I think 539 00:31:52,520 --> 00:31:55,480 Speaker 2: of what we feel that we need as a society. 540 00:31:56,120 --> 00:31:59,800 Speaker 1: Absolutely so. According to AI dot CoV the AI Action 541 00:32:00,240 --> 00:32:02,880 Speaker 1: and you guys can go online and type this into 542 00:32:02,960 --> 00:32:05,720 Speaker 1: your search engines AI dot gov, there's a big old 543 00:32:05,760 --> 00:32:08,320 Speaker 1: picture of Donald Trump right in the front. It's horrible. 544 00:32:08,880 --> 00:32:11,120 Speaker 1: The AI Action plan. The United States is in a 545 00:32:11,200 --> 00:32:15,040 Speaker 1: race to achieve global dominance in artificial intelligence. Whoever has 546 00:32:15,080 --> 00:32:18,760 Speaker 1: the largest AI ecosystem will set the global standards and 547 00:32:18,800 --> 00:32:22,719 Speaker 1: reap broad economic and security benefits. Under President Trump, our 548 00:32:22,840 --> 00:32:25,840 Speaker 1: nation will win, ushering in a new golden age of innovation, 549 00:32:26,040 --> 00:32:30,120 Speaker 1: human flourishing, and technological achievement for the American people. America's 550 00:32:30,160 --> 00:32:34,680 Speaker 1: AI Action Plan has three policy pillars, accelerating innovation, building 551 00:32:34,720 --> 00:32:39,840 Speaker 1: AI infrastructure, and leading international diplomacy and security. So it 552 00:32:39,920 --> 00:32:46,480 Speaker 1: is definitely a goal and an initiative to expand in 553 00:32:46,520 --> 00:32:51,040 Speaker 1: advance AI across the country. According to AI dot gov, 554 00:32:51,760 --> 00:32:53,880 Speaker 1: So all the scary things that we were talking about 555 00:32:53,920 --> 00:32:56,080 Speaker 1: that we hope don't happen, it seems like they're definitely 556 00:32:56,120 --> 00:33:01,200 Speaker 1: going to happen. And it's yeah, it's all right there 557 00:33:01,360 --> 00:33:02,240 Speaker 1: in plain English. 558 00:33:02,320 --> 00:33:05,320 Speaker 2: It sounds like he wrote it. Is what my immediate 559 00:33:05,320 --> 00:33:09,080 Speaker 2: thought was, Our nation will win golden age of innovation. 560 00:33:09,320 --> 00:33:11,240 Speaker 1: Yeah, yeah, it's very Trumpian. 561 00:33:11,400 --> 00:33:12,240 Speaker 2: Yeah he wrote it. 562 00:33:12,240 --> 00:33:13,680 Speaker 1: It's very trumpy in the whole thing. 563 00:33:14,400 --> 00:33:15,640 Speaker 2: I bet you he copy ed its. 564 00:33:16,600 --> 00:33:18,640 Speaker 1: He does his own copy of it, bet you he does. 565 00:33:19,360 --> 00:33:21,680 Speaker 1: He's like, you have to add win, He's like, can 566 00:33:21,680 --> 00:33:24,240 Speaker 1: you add my picture? No? For sure? 567 00:33:25,360 --> 00:33:25,479 Speaker 2: Ay? 568 00:33:25,560 --> 00:33:27,240 Speaker 1: Yeah? Yea, I yes, y'all. 569 00:33:28,440 --> 00:33:30,520 Speaker 2: Well, it's tough. 570 00:33:30,680 --> 00:33:31,280 Speaker 1: It's tough. 571 00:33:31,360 --> 00:33:35,240 Speaker 2: This My sigh is guttural, it's real, it's a deep sigh. 572 00:33:35,240 --> 00:33:38,640 Speaker 2: It's a deep sigh because there's just so much to consider, 573 00:33:38,720 --> 00:33:41,560 Speaker 2: and I think, you know, we never want our listeners 574 00:33:41,600 --> 00:33:45,040 Speaker 2: to feel overwhelmed when they after listening to an episode, 575 00:33:45,160 --> 00:33:48,400 Speaker 2: or feel like there's no hope and there's no you know, 576 00:33:48,640 --> 00:33:51,920 Speaker 2: nothing matters, or nihilistic. Even I was when we were 577 00:33:52,200 --> 00:33:55,280 Speaker 2: prepping for this episode. I was saying, like, it is 578 00:33:55,520 --> 00:33:59,600 Speaker 2: really easy to fall into nihilism and to forget that 579 00:33:59,720 --> 00:34:04,280 Speaker 2: RAT is a huge tool in our toolbox and one 580 00:34:04,320 --> 00:34:08,480 Speaker 2: that we need to really champion right now because it 581 00:34:08,520 --> 00:34:11,840 Speaker 2: can feel really scary. Times are changing. It feels like 582 00:34:11,880 --> 00:34:15,920 Speaker 2: it's changing so rapidly, I think, faster than before. And 583 00:34:16,000 --> 00:34:18,080 Speaker 2: I think that's part of the anxiety that many of 584 00:34:18,160 --> 00:34:21,440 Speaker 2: us are feeling. And it's I think just important to 585 00:34:21,719 --> 00:34:24,600 Speaker 2: remember when we're thinking about AI, like who really benefits 586 00:34:24,600 --> 00:34:27,719 Speaker 2: from all of these quickening changes? And I mean it 587 00:34:27,760 --> 00:34:31,880 Speaker 2: seems like the billionaires that are running the country, the 588 00:34:32,600 --> 00:34:37,400 Speaker 2: venture capitalists, the tech bros, you know, and so just 589 00:34:37,440 --> 00:34:42,080 Speaker 2: thinking about, you know, ways that we can divest from 590 00:34:42,120 --> 00:34:46,040 Speaker 2: AI and creativity at that, you know, And I think 591 00:34:46,080 --> 00:34:48,440 Speaker 2: one of the beautiful things that I love about being 592 00:34:48,480 --> 00:34:52,600 Speaker 2: in this creative industry is the brainstorming is sitting in 593 00:34:52,640 --> 00:34:56,799 Speaker 2: a meeting with our team and ideating and brainstorming and 594 00:34:56,840 --> 00:35:01,160 Speaker 2: getting those creative juices flowing. And AI can not rob 595 00:35:01,239 --> 00:35:05,640 Speaker 2: that experience, you know. And so does that mean like 596 00:35:05,760 --> 00:35:09,040 Speaker 2: unplugging and like going to the library and doing your 597 00:35:09,080 --> 00:35:14,040 Speaker 2: own research probably you know, reading a book. And I 598 00:35:14,080 --> 00:35:16,680 Speaker 2: don't know if it's just my algorithm, and it's like 599 00:35:17,200 --> 00:35:19,319 Speaker 2: again another AI told the way I've trained it. But 600 00:35:19,480 --> 00:35:22,640 Speaker 2: I'm seeing like a lot of pushes for people to 601 00:35:22,960 --> 00:35:27,719 Speaker 2: like bring back analog and bring back the landline. And 602 00:35:28,520 --> 00:35:31,520 Speaker 2: I saw a video of someone who literally chained her 603 00:35:31,680 --> 00:35:34,839 Speaker 2: phone to the wall and so she could only use 604 00:35:34,880 --> 00:35:37,759 Speaker 2: it in one spot of her house, and so she 605 00:35:37,920 --> 00:35:40,360 Speaker 2: went on and did all of her errands without a phone. 606 00:35:40,640 --> 00:35:43,160 Speaker 2: And you know, there's obviously like I don't want to 607 00:35:43,200 --> 00:35:46,440 Speaker 2: be without a phone safety wise, you know, but there 608 00:35:46,480 --> 00:35:49,000 Speaker 2: are what I'm the point is, I'm seeing like real 609 00:35:49,400 --> 00:35:54,480 Speaker 2: push from people from individuals to like do like digital 610 00:35:54,560 --> 00:36:00,799 Speaker 2: detoxes and like and goma sama craft and be in 611 00:36:00,880 --> 00:36:04,040 Speaker 2: community with each other. And so that does give me hope, 612 00:36:04,080 --> 00:36:06,960 Speaker 2: and I hope that for whoever's for I hope that 613 00:36:07,000 --> 00:36:10,600 Speaker 2: if you're listening, you feel inspired and hopeful to like 614 00:36:11,200 --> 00:36:14,560 Speaker 2: get out there with your community and do these like 615 00:36:15,200 --> 00:36:17,120 Speaker 2: analogue things. Yeah, you know. 616 00:36:17,760 --> 00:36:23,120 Speaker 1: Yes, the technology is supposed to help us to live 617 00:36:23,640 --> 00:36:27,760 Speaker 1: like better human lives, not to replace our human lives. 618 00:36:28,320 --> 00:36:32,479 Speaker 1: And I think that we have to love people more 619 00:36:32,520 --> 00:36:38,120 Speaker 1: than we love the technology, and remember that it's really 620 00:36:38,160 --> 00:36:40,759 Speaker 1: about loving ourselves and loving each other and not loving 621 00:36:40,840 --> 00:36:44,120 Speaker 1: the tools, right, because the tools cannot love us back, right, 622 00:36:44,200 --> 00:36:46,399 Speaker 1: and they actually don't take care of us. No, we 623 00:36:46,440 --> 00:36:50,799 Speaker 1: take care of them, and hope, I think is the 624 00:36:50,840 --> 00:36:54,560 Speaker 1: most active form of love, you know. So we have 625 00:36:54,640 --> 00:36:58,160 Speaker 1: to keep loving each other and putting each other above 626 00:36:58,600 --> 00:37:03,480 Speaker 1: the technology, so that means resisting, you know, and using 627 00:37:03,520 --> 00:37:08,200 Speaker 1: it to help us thrive, but not to like eliminate us. Yes, 628 00:37:08,600 --> 00:37:09,640 Speaker 1: it's a slippery. 629 00:37:09,280 --> 00:37:12,239 Speaker 2: Slope, slippery slope at that. I think that's a really 630 00:37:12,239 --> 00:37:16,719 Speaker 2: beautiful note to end on. You've been really wholesome this season, right, 631 00:37:16,880 --> 00:37:20,560 Speaker 2: Her takeaways have been like so wholesome and lovely this season. 632 00:37:21,200 --> 00:37:22,480 Speaker 2: Thank you, Yeah, thank you. 633 00:37:22,800 --> 00:37:26,240 Speaker 1: I'm in a very optimistic space that's good in spite 634 00:37:26,239 --> 00:37:26,720 Speaker 1: of everything. 635 00:37:26,800 --> 00:37:28,759 Speaker 2: In spite of everything, that's a good place to be 636 00:37:28,880 --> 00:37:32,520 Speaker 2: in the hope and the optimism that things are going 637 00:37:32,560 --> 00:37:35,680 Speaker 2: to be okay, because if not, it can feel really 638 00:37:35,760 --> 00:37:39,120 Speaker 2: dark and scary. Yeah, and that is what they want 639 00:37:39,200 --> 00:37:39,760 Speaker 2: us to feel. 640 00:37:39,920 --> 00:37:44,000 Speaker 1: They want us to feel hopeless and to tear ourselves 641 00:37:44,040 --> 00:37:45,160 Speaker 1: apart and each other. 642 00:37:45,400 --> 00:37:47,359 Speaker 2: Yeah, and we're really good at doing it. 643 00:37:47,480 --> 00:37:49,880 Speaker 1: We're really good at tearing each other apart and tearing 644 00:37:49,920 --> 00:37:55,440 Speaker 1: each other down. And that's how you you weaken the resistance, 645 00:37:56,000 --> 00:37:58,600 Speaker 1: you know. But yeah, we just we gotta we have 646 00:37:58,640 --> 00:37:59,440 Speaker 1: to stay positive. 647 00:37:59,680 --> 00:38:01,959 Speaker 2: We got got to keep going, gotta keep going. Yes, 648 00:38:02,480 --> 00:38:04,960 Speaker 2: all right, y'all, thank you for listening to another Capito 649 00:38:05,480 --> 00:38:09,239 Speaker 2: lok A Radio. We'll catch you next time. Bessitos lok 650 00:38:09,239 --> 00:38:13,200 Speaker 2: A Radio is executive produced by Viosa Fem and Mala Munios. 651 00:38:13,520 --> 00:38:16,000 Speaker 1: Stephanie Franco is our producer. 652 00:38:15,880 --> 00:38:17,319 Speaker 2: Story editing by Me. 653 00:38:17,600 --> 00:38:19,960 Speaker 1: Diosa creative direction by Me Mala. 654 00:38:20,200 --> 00:38:23,120 Speaker 2: Look At Radio is a part of iHeartRadio's Michael Tura 655 00:38:23,200 --> 00:38:24,120 Speaker 2: podcast Network. 656 00:38:24,360 --> 00:38:26,919 Speaker 1: You can listen to lok A Radio on the iHeartRadio 657 00:38:26,960 --> 00:38:28,960 Speaker 1: app or wherever you get your podcasts. 658 00:38:29,200 --> 00:38:31,400 Speaker 2: Leave us a review and share with your Prima or 659 00:38:31,440 --> 00:38:32,840 Speaker 2: share with your homegirl. 660 00:38:32,520 --> 00:38:35,040 Speaker 1: And thank you to our local motives to our listeners 661 00:38:35,040 --> 00:38:37,600 Speaker 1: for tuning in each and every week Besitos 662 00:38:41,200 --> 00:38:41,600 Speaker 2: Loca