1 00:00:04,160 --> 00:00:05,880 Speaker 1: There Are No Girls on the Internet, as a production 2 00:00:05,960 --> 00:00:13,440 Speaker 1: of iHeartRadio and Unbossed Creative. I'm Bridge Todd and this 3 00:00:13,560 --> 00:00:18,000 Speaker 1: is There Are No Girls on the Internet. It is 4 00:00:18,000 --> 00:00:20,759 Speaker 1: the fiftieth anniversary of hip hop, and that is a 5 00:00:20,760 --> 00:00:24,040 Speaker 1: great opportunity to dig into one of my favorite subjects 6 00:00:24,040 --> 00:00:26,799 Speaker 1: to nerd out about, which is audio and kind of 7 00:00:26,880 --> 00:00:29,880 Speaker 1: trends and audio how we respond to those trends and what. 8 00:00:29,840 --> 00:00:31,080 Speaker 2: It says about us as the culture. 9 00:00:31,520 --> 00:00:34,280 Speaker 1: The history of auto tune is a fascinating one and 10 00:00:34,320 --> 00:00:36,559 Speaker 1: I think it really explains why I'm so hung up 11 00:00:36,600 --> 00:00:39,800 Speaker 1: on audio trends as like a nerdy little side interest. 12 00:00:40,320 --> 00:00:43,600 Speaker 1: So when I say auto tune, Mike, what comes to 13 00:00:43,600 --> 00:00:45,680 Speaker 1: mind for you? Is there any one person that comes 14 00:00:45,680 --> 00:00:48,240 Speaker 1: to mind when you think about auto tune? Yeah? 15 00:00:48,240 --> 00:00:50,800 Speaker 3: I mean, I'm no auto tune expert, but I think 16 00:00:51,000 --> 00:00:54,760 Speaker 3: Tea Pain really is the artist who took it and 17 00:00:55,000 --> 00:00:58,520 Speaker 3: ran with it and made it like his thing. 18 00:00:59,520 --> 00:01:00,680 Speaker 2: Yes, great answer. 19 00:01:01,040 --> 00:01:04,480 Speaker 1: I recently watched the t Pain NPR Tiny Death Concert, 20 00:01:04,480 --> 00:01:07,600 Speaker 1: which is like a masterpiece. People should definitely watch it. 21 00:01:07,640 --> 00:01:11,400 Speaker 1: But it kind of made me happy to see Tea 22 00:01:11,480 --> 00:01:16,280 Speaker 1: Pain kind of coming full circle and kind of getting 23 00:01:16,319 --> 00:01:19,160 Speaker 1: the flowers that he so richly deserves for being an 24 00:01:19,240 --> 00:01:22,160 Speaker 1: innovator when it comes to autotune, and the history of 25 00:01:22,200 --> 00:01:26,040 Speaker 1: how autotune went from this like kind of niche thing 26 00:01:26,480 --> 00:01:30,480 Speaker 1: to being everywhere, to being hated to now being like commonplace, 27 00:01:30,560 --> 00:01:33,000 Speaker 1: I think is such an interesting one. And the reason 28 00:01:33,000 --> 00:01:35,240 Speaker 1: that I want to tell this story now, in addition 29 00:01:35,240 --> 00:01:37,920 Speaker 1: to being the fiftieth anniversary of hip hop, is that 30 00:01:38,160 --> 00:01:41,679 Speaker 1: I think the conversation that we're having around technology and 31 00:01:41,720 --> 00:01:45,760 Speaker 1: how it intersects with art and creativity really mirrors the 32 00:01:45,800 --> 00:01:49,080 Speaker 1: conversation that we had around autotune. I'm specifically thinking here 33 00:01:49,080 --> 00:01:52,880 Speaker 1: about conversations around AI. You know, how will AI shape 34 00:01:52,880 --> 00:01:55,040 Speaker 1: things like hip hop is a question that we are 35 00:01:55,080 --> 00:01:58,720 Speaker 1: seeing artists grapple with in real time, And I think 36 00:01:58,720 --> 00:02:01,000 Speaker 1: that's kind of because hip hop has always had this 37 00:02:01,080 --> 00:02:05,240 Speaker 1: particular unique relationship to technology. It's always been this medium 38 00:02:05,320 --> 00:02:09,200 Speaker 1: grounded in technology, and so historically rappers and hip hop 39 00:02:09,280 --> 00:02:13,480 Speaker 1: artists can't really shy away from embracing technology. Hip hop 40 00:02:13,520 --> 00:02:16,440 Speaker 1: has always been about innovation and trying new things and 41 00:02:16,520 --> 00:02:20,880 Speaker 1: using technology to create something totally new. So I really 42 00:02:20,880 --> 00:02:24,280 Speaker 1: feel that if anybody can use new technology like AI 43 00:02:24,639 --> 00:02:27,640 Speaker 1: to do something that does not feel like exploitation or 44 00:02:27,680 --> 00:02:32,480 Speaker 1: derivative or lazy. It is hip hop artists because the 45 00:02:32,560 --> 00:02:36,720 Speaker 1: creativity and innovation of traditionally marginalized voices is that powerful. 46 00:02:37,320 --> 00:02:39,040 Speaker 1: So here's a little bit of a rundown of how 47 00:02:39,080 --> 00:02:42,040 Speaker 1: hip hop artists are grappling with that question around technology 48 00:02:42,040 --> 00:02:44,400 Speaker 1: and AI and how it will impact hip hop today. 49 00:02:45,240 --> 00:02:48,760 Speaker 1: Rapper Lupe Fiasco just announced a partnership with Google for 50 00:02:48,800 --> 00:02:51,840 Speaker 1: a program called text Fx, which he says is meant 51 00:02:51,840 --> 00:02:54,440 Speaker 1: to help artists during the songwriting process through the use 52 00:02:54,440 --> 00:02:58,560 Speaker 1: of generating alternative meanings, spellings, and phrases to words initially 53 00:02:58,639 --> 00:03:01,600 Speaker 1: chosen by the human song writer. It kind of sounds 54 00:03:01,639 --> 00:03:05,560 Speaker 1: like an AI powered rhyming dictionary for rappers, and Lupey 55 00:03:05,600 --> 00:03:07,960 Speaker 1: really says that this partnership was really grounded in the 56 00:03:08,000 --> 00:03:11,920 Speaker 1: relationship that rap has always has his technology, saying rap 57 00:03:11,960 --> 00:03:14,600 Speaker 1: is born out of technology. Rap wouldn't exist if not 58 00:03:14,639 --> 00:03:18,200 Speaker 1: for technological advancements. It kind of sounds like this tool 59 00:03:18,360 --> 00:03:21,600 Speaker 1: is not meant to replace rappers or songwriters, but rather 60 00:03:21,680 --> 00:03:26,440 Speaker 1: be a supplement to a human songwriter. Lupey explained, it 61 00:03:26,480 --> 00:03:28,639 Speaker 1: does require you to do the work. It's not doing 62 00:03:28,720 --> 00:03:30,800 Speaker 1: the work for you. It's just providing you with different 63 00:03:30,800 --> 00:03:34,400 Speaker 1: opportunities and workflow for being efficient and offloading certain things 64 00:03:34,400 --> 00:03:36,960 Speaker 1: so that your mind can focus on other things. And 65 00:03:37,000 --> 00:03:39,880 Speaker 1: then you have producers like Timbaland, who is exploring with 66 00:03:39,960 --> 00:03:43,400 Speaker 1: making songs using AI generated versions of rappers who have 67 00:03:43,480 --> 00:03:46,960 Speaker 1: died so that those more established rappers can be on 68 00:03:47,040 --> 00:03:50,200 Speaker 1: tracks with up and coming artists. Obviously, this kind of 69 00:03:50,280 --> 00:03:52,880 Speaker 1: thing has gotten a lot of negative attention as being 70 00:03:52,920 --> 00:03:57,840 Speaker 1: disrespectful or just plain creepy, but Timberland told Forbes that 71 00:03:57,880 --> 00:04:00,720 Speaker 1: he feels the industry and consumers need to see a 72 00:04:00,760 --> 00:04:04,520 Speaker 1: more serious, well intentioned, and transparent effort to integrate AI 73 00:04:04,560 --> 00:04:07,440 Speaker 1: technology into hip hop, saying, I don't want to be 74 00:04:07,480 --> 00:04:09,200 Speaker 1: afraid of what's going on. I want to be the 75 00:04:09,200 --> 00:04:12,040 Speaker 1: guy that figures out a solution. But on the other hand, 76 00:04:12,120 --> 00:04:14,520 Speaker 1: you have rappers like ice Cube, who said that AI 77 00:04:14,560 --> 00:04:17,560 Speaker 1: will make artists lazier and less creative and threaten to 78 00:04:17,600 --> 00:04:21,600 Speaker 1: sue any platform or person who promotes an artificial intelligence 79 00:04:21,640 --> 00:04:24,880 Speaker 1: generated version of his voice or likeness. And then there's 80 00:04:24,920 --> 00:04:27,400 Speaker 1: Little Wayne, who I think had the best response to 81 00:04:27,440 --> 00:04:28,960 Speaker 1: the rise of AI and hip hop. 82 00:04:28,839 --> 00:04:32,120 Speaker 2: Which is basically bring it on, lol. 83 00:04:32,160 --> 00:04:35,279 Speaker 1: Wayne said, I'm like is this AI thing going to 84 00:04:35,279 --> 00:04:39,719 Speaker 1: be amazing too, because I am naturally organically amazing. I 85 00:04:39,800 --> 00:04:42,240 Speaker 1: am one of a kind, So actually I would love 86 00:04:42,279 --> 00:04:45,680 Speaker 1: to see that thing try to duplicate this motherfucker. I 87 00:04:45,720 --> 00:04:48,360 Speaker 1: think the variety of responses to AI and hip hop 88 00:04:48,400 --> 00:04:51,599 Speaker 1: that we're seeing now really mirrors the conversation that artists 89 00:04:51,640 --> 00:04:53,800 Speaker 1: we're having back in the two thousands about the use 90 00:04:53,839 --> 00:04:56,719 Speaker 1: of auto tune and music. Will it make musicians lazy, 91 00:04:56,839 --> 00:05:00,839 Speaker 1: less creative? Will it displace authentic vocalists? We're basically seeing 92 00:05:00,920 --> 00:05:03,560 Speaker 1: artists have that very same conversation they were having about 93 00:05:03,560 --> 00:05:06,400 Speaker 1: auto tune in the two thousands now with AI and 94 00:05:06,440 --> 00:05:09,359 Speaker 1: the story of autotune is one where I think something 95 00:05:09,360 --> 00:05:12,279 Speaker 1: that was once perceived as being a gimmick at best 96 00:05:12,480 --> 00:05:16,440 Speaker 1: or at worst, a way to manipulate human artistry in 97 00:05:16,480 --> 00:05:19,719 Speaker 1: a way that kind of creates this vibe that like, oh, 98 00:05:19,839 --> 00:05:24,440 Speaker 1: like humans are expected to sound like perfect machines. I 99 00:05:24,440 --> 00:05:26,520 Speaker 1: think that was like a big concern that folks were 100 00:05:26,560 --> 00:05:30,719 Speaker 1: grappling with around autotune. There was this big concern that 101 00:05:30,800 --> 00:05:34,719 Speaker 1: autotune was going to de incentivize artists kind of needing 102 00:05:34,760 --> 00:05:36,640 Speaker 1: to be good or needing to work on their craft. 103 00:05:36,680 --> 00:05:37,360 Speaker 2: Or perfect their. 104 00:05:37,279 --> 00:05:39,320 Speaker 1: Craft because you could just hit a button on a computer. 105 00:05:39,640 --> 00:05:42,840 Speaker 1: But today, nobody thinks of autotune as just a gimmick. 106 00:05:42,960 --> 00:05:45,400 Speaker 1: Nobody thinks of auto tune as something that, you know, 107 00:05:46,120 --> 00:05:50,120 Speaker 1: de incentivizes artists to be creative or perfect their craft. 108 00:05:50,360 --> 00:05:53,680 Speaker 1: It's just something that is totally commonplace in audio today. 109 00:05:53,760 --> 00:05:56,119 Speaker 1: It's used in all kinds of interesting and creative ways 110 00:05:56,160 --> 00:05:58,800 Speaker 1: because in the end, creative artists were able to make 111 00:05:58,839 --> 00:06:02,440 Speaker 1: something cool and innovative to create a cultural shift. And 112 00:06:02,480 --> 00:06:04,720 Speaker 1: so I think that story as we talk about things 113 00:06:04,760 --> 00:06:08,719 Speaker 1: like the intersection of music and AI, that story seems 114 00:06:08,720 --> 00:06:11,400 Speaker 1: even more relevant right now. So let's talk about how 115 00:06:11,400 --> 00:06:15,679 Speaker 1: autotune came to be. Autotune was originally developed by doctor 116 00:06:15,720 --> 00:06:19,400 Speaker 1: Andy Hildebrand, a research engineer, and weirdly enough, he was 117 00:06:19,480 --> 00:06:21,760 Speaker 1: not working in the music industry when he came up 118 00:06:21,800 --> 00:06:23,919 Speaker 1: with the idea for autotune. He was working in the 119 00:06:23,920 --> 00:06:27,240 Speaker 1: gas industry. He worked for Exxon and created a complex 120 00:06:27,279 --> 00:06:30,920 Speaker 1: set of algorithms to interpret sonar generated data to locate 121 00:06:30,960 --> 00:06:34,159 Speaker 1: oil deposits deep underground. But he was also a musician, 122 00:06:34,200 --> 00:06:36,200 Speaker 1: a flute player, and he always wanted to find a 123 00:06:36,200 --> 00:06:38,719 Speaker 1: way to be more involved in the audio space. Him 124 00:06:38,760 --> 00:06:42,479 Speaker 1: creating autotune was kind of a fluke. A wife of 125 00:06:42,520 --> 00:06:45,440 Speaker 1: his colleague was joking about how bad her singing voice is, 126 00:06:45,760 --> 00:06:47,279 Speaker 1: and she was like, Oh, if only there was a 127 00:06:47,279 --> 00:06:49,800 Speaker 1: technology I could sing into that would help me sing 128 00:06:49,839 --> 00:06:52,640 Speaker 1: in tune. And I guess this idea really stayed with 129 00:06:52,720 --> 00:06:55,520 Speaker 1: him and he got to thinking could the tool that. 130 00:06:55,520 --> 00:06:59,560 Speaker 2: He used in the oil industry also help correct pitch? Answer? 131 00:07:00,160 --> 00:07:03,480 Speaker 1: Yes, So it might be wondering why is this such 132 00:07:03,520 --> 00:07:06,720 Speaker 1: a big deal. Well, before autotune, there were ways to 133 00:07:06,839 --> 00:07:09,800 Speaker 1: correct pitch, but it was so time consuming that it 134 00:07:09,840 --> 00:07:13,680 Speaker 1: was generally not considered to be worth it. Meanwhile, autotune 135 00:07:13,680 --> 00:07:17,720 Speaker 1: was incredibly good, easy and fast to correct pitch, so 136 00:07:17,800 --> 00:07:20,040 Speaker 1: it was a total game changer. This is something that 137 00:07:20,120 --> 00:07:22,320 Speaker 1: kind of comes up in podcasting a lot too. You know, 138 00:07:22,800 --> 00:07:27,160 Speaker 1: when you're podcasting, the best take is generally your most 139 00:07:27,280 --> 00:07:29,960 Speaker 1: natural take, and so when you say something and it 140 00:07:30,040 --> 00:07:34,480 Speaker 1: just sounds so right, but maybe you caw or stumble 141 00:07:34,680 --> 00:07:37,680 Speaker 1: or you know, trip over your words a little bit, 142 00:07:38,320 --> 00:07:40,960 Speaker 1: if you redo it, it's never going to sound as good. 143 00:07:41,040 --> 00:07:43,280 Speaker 1: And so one of the reasons why this technology is 144 00:07:43,320 --> 00:07:45,760 Speaker 1: such a game changer is that it allows you to 145 00:07:45,880 --> 00:07:49,560 Speaker 1: keep that natural first best take, but just polish the 146 00:07:49,600 --> 00:07:53,360 Speaker 1: parts that don't work. As you describe to NPR, the 147 00:07:53,400 --> 00:07:55,960 Speaker 1: singer's first take is often their best. It's full of 148 00:07:56,040 --> 00:07:59,920 Speaker 1: vitality and emotion. After their take, the producer will announce great, 149 00:08:00,160 --> 00:08:03,120 Speaker 1: but the second phrase was pitchy, so let's do it again. Well, 150 00:08:03,160 --> 00:08:05,640 Speaker 1: now the singer's worried about pitch and has to focus 151 00:08:05,680 --> 00:08:08,680 Speaker 1: on intonation, and the vitality and emotion are gone from 152 00:08:08,680 --> 00:08:11,480 Speaker 1: their performance. What auto tune lets the producer do is 153 00:08:11,560 --> 00:08:14,440 Speaker 1: fix the first take, which makes a lot of sense. 154 00:08:14,800 --> 00:08:17,320 Speaker 1: In nineteen ninety six, he implemented the algorithm on a 155 00:08:17,320 --> 00:08:20,640 Speaker 1: custom Macintosh computer and presented the results at the National 156 00:08:20,680 --> 00:08:24,160 Speaker 1: Association of Music Merchants, a trade show for audio professionals, 157 00:08:24,360 --> 00:08:30,400 Speaker 1: where it was instantly a massive hit. I can understand why, because, yeah, 158 00:08:30,400 --> 00:08:34,400 Speaker 1: the frustration of having to completely redo a take because 159 00:08:34,400 --> 00:08:36,960 Speaker 1: you're not able to just go in and perfect it 160 00:08:37,000 --> 00:08:39,200 Speaker 1: is very frustrating. So I can understand why. When this 161 00:08:39,360 --> 00:08:41,760 Speaker 1: was released people were like, oh my god, a huge 162 00:08:41,760 --> 00:08:45,040 Speaker 1: game changer. So autotune becomes a thing, but at this 163 00:08:45,160 --> 00:08:48,800 Speaker 1: point it's kind of treated like a trade secret. Engineers 164 00:08:48,800 --> 00:08:52,880 Speaker 1: were using autotune to discreetly correct pitch without really advertising 165 00:08:52,920 --> 00:08:57,800 Speaker 1: it until someone sweeps in in nineteen ninety eight with 166 00:08:57,960 --> 00:09:01,200 Speaker 1: shimmer and bangles and hellowing long black hair. 167 00:09:02,000 --> 00:09:06,199 Speaker 2: Can you guess who I am talking about? It's sure, 168 00:09:08,440 --> 00:09:09,000 Speaker 2: it's fair. 169 00:09:09,840 --> 00:09:15,320 Speaker 1: So Cher's Belief in nineteen ninety eight was the song 170 00:09:15,520 --> 00:09:18,120 Speaker 1: of the late nineties, right. Not only was it a 171 00:09:18,200 --> 00:09:21,520 Speaker 1: musical departure for Share as an artist, but it's a 172 00:09:21,559 --> 00:09:26,160 Speaker 1: song about transformations. So she as a as an artist 173 00:09:26,480 --> 00:09:29,240 Speaker 1: is going through a transformation in her career, but also 174 00:09:29,520 --> 00:09:32,520 Speaker 1: singing about the importance of transformation. And I think it's 175 00:09:32,880 --> 00:09:35,640 Speaker 1: I think it's an arguable that Believe is SHARE's most 176 00:09:35,720 --> 00:09:39,520 Speaker 1: important song. I think it would be really easy for 177 00:09:39,559 --> 00:09:42,600 Speaker 1: an artist like Share to kind of become a nostalgia 178 00:09:42,640 --> 00:09:45,000 Speaker 1: act from her Sunday and Share days of the seventies, 179 00:09:45,080 --> 00:09:48,360 Speaker 1: but she didn't. She kept evolving and kept innovating. At 180 00:09:48,360 --> 00:09:50,600 Speaker 1: the Grammys that year, the song was nominated for Record 181 00:09:50,679 --> 00:09:53,160 Speaker 1: of the Year and Best Dance Record, winning the latter, 182 00:09:53,640 --> 00:09:56,680 Speaker 1: and it was a huge commercial hit too. Believe is 183 00:09:56,679 --> 00:09:59,360 Speaker 1: one of the best selling singles in music history. And 184 00:09:59,440 --> 00:10:00,720 Speaker 1: this is actually something. 185 00:10:00,520 --> 00:10:02,560 Speaker 2: That is like pretty hard to do. 186 00:10:02,960 --> 00:10:05,400 Speaker 1: The music scene of the late nineties was really fragmented, 187 00:10:05,600 --> 00:10:09,360 Speaker 1: but Believe broke through as this global smash. It's also 188 00:10:09,480 --> 00:10:12,120 Speaker 1: just really endured culturally, Like there's a Sex in the 189 00:10:12,120 --> 00:10:15,200 Speaker 1: City episode where Charlotte thinks her boyfriend might secretly be gay, 190 00:10:15,679 --> 00:10:18,040 Speaker 1: and one of the pieces of evidence is that he 191 00:10:18,080 --> 00:10:20,440 Speaker 1: puts on shares Believe when they're like in the kitchen, 192 00:10:20,840 --> 00:10:24,800 Speaker 1: And so I think it's important to really highlight what 193 00:10:25,080 --> 00:10:28,880 Speaker 1: a big deal this song is. Like if you've ever 194 00:10:28,920 --> 00:10:32,080 Speaker 1: been on a dance floor and heard Believe, come on, 195 00:10:32,760 --> 00:10:35,800 Speaker 1: it is like an emotionally resonant experience to be dancing 196 00:10:35,840 --> 00:10:38,840 Speaker 1: to Believe on a dance floor, Like the song just 197 00:10:38,880 --> 00:10:40,920 Speaker 1: does something to you when you hear it. And I 198 00:10:40,920 --> 00:10:44,240 Speaker 1: also think it's interesting in that it's a song about 199 00:10:44,280 --> 00:10:48,400 Speaker 1: someone who has been dumped, but that act of being 200 00:10:48,480 --> 00:10:51,120 Speaker 1: dumped is like a badge of honor. It is like that, 201 00:10:51,320 --> 00:10:54,120 Speaker 1: like usually in a love song, the person who has 202 00:10:54,160 --> 00:10:59,640 Speaker 1: been dumped is like sad or you know, grieving a 203 00:10:59,679 --> 00:11:03,079 Speaker 1: lost relationship, grieving that they weren't enough for this person. 204 00:11:02,800 --> 00:11:03,560 Speaker 2: Who dumped them. 205 00:11:03,800 --> 00:11:08,400 Speaker 1: But in Belief, she is like righteous. She is not 206 00:11:08,600 --> 00:11:11,400 Speaker 1: saying like I am sad, I'll get over this. She 207 00:11:11,520 --> 00:11:14,640 Speaker 1: is saying I will transform. This moment is going to 208 00:11:14,640 --> 00:11:16,920 Speaker 1: be a moment of transformation for me. And it's just 209 00:11:16,960 --> 00:11:19,520 Speaker 1: one of those things like the line. 210 00:11:19,880 --> 00:11:22,600 Speaker 2: Do you believe in life after love? 211 00:11:22,760 --> 00:11:25,400 Speaker 1: Like it just it's one of those lines that I 212 00:11:25,440 --> 00:11:29,360 Speaker 1: can't believe is not an established phrase or idiom because 213 00:11:29,360 --> 00:11:32,520 Speaker 1: it just hits so perfectly. So in the song Believe, 214 00:11:32,679 --> 00:11:36,760 Speaker 1: autotune was used to create unnaturally rapid corrections and shares vocals. 215 00:11:37,240 --> 00:11:38,920 Speaker 2: They basically removed. 216 00:11:38,480 --> 00:11:41,800 Speaker 1: That natural slide between pitches and singing, in effect creating 217 00:11:41,840 --> 00:11:44,400 Speaker 1: that kind of robotic voice that you hear in the 218 00:11:44,440 --> 00:11:48,680 Speaker 1: song Believes. Producer Mark Taylor originally did not want to 219 00:11:48,679 --> 00:11:50,719 Speaker 1: tell people that he had used auto tune to get 220 00:11:50,720 --> 00:11:53,840 Speaker 1: that sound. No, not because auto tune had a negative 221 00:11:53,840 --> 00:11:56,880 Speaker 1: connotation yet, but because he wanted to protect the method 222 00:11:56,920 --> 00:11:57,920 Speaker 1: as a trade secret. 223 00:11:58,400 --> 00:12:00,560 Speaker 2: So the team initially came up with a whole. 224 00:12:00,320 --> 00:12:02,760 Speaker 1: Cover story that if they were asked, they were going 225 00:12:02,800 --> 00:12:05,480 Speaker 1: to say that they got that robotic vocal effect by 226 00:12:05,520 --> 00:12:07,920 Speaker 1: using a vocoder pedal a vocoder. If you don't know 227 00:12:07,920 --> 00:12:11,640 Speaker 1: what that is, it's similar to auto tune with different Apparently, 228 00:12:11,760 --> 00:12:14,440 Speaker 1: Cher's label wanted her to remove the autotune effect, and 229 00:12:14,480 --> 00:12:18,200 Speaker 1: she flat out refused. They even started calling autotune the 230 00:12:18,360 --> 00:12:21,560 Speaker 1: share effect. So while all of this is going down 231 00:12:21,559 --> 00:12:25,679 Speaker 1: in nineteen ninety nine, Tea Pain is just starting his 232 00:12:25,760 --> 00:12:29,160 Speaker 1: musical career. So when t Pain was three, he got 233 00:12:29,160 --> 00:12:32,120 Speaker 1: interested in music because a family friend, jazz singer and 234 00:12:32,160 --> 00:12:36,320 Speaker 1: producer Ben Tankard, allowed him to spend time quote twisting 235 00:12:36,360 --> 00:12:40,079 Speaker 1: knobs at his recording studio. At age ten, t Payin 236 00:12:40,160 --> 00:12:43,120 Speaker 1: turned his entire bedroom into a little music studio with 237 00:12:43,200 --> 00:12:45,959 Speaker 1: a beat machine and a keyboard and everything. T Pain 238 00:12:46,080 --> 00:12:49,160 Speaker 1: actually got his start as a rapper, but after being discovered, 239 00:12:49,400 --> 00:12:51,720 Speaker 1: he decided that he wanted to sing instead of rap. 240 00:12:52,240 --> 00:12:54,400 Speaker 1: It's the early two thousands and T Pain they've been 241 00:12:54,440 --> 00:12:56,400 Speaker 1: looking for a way to make his voice stand out. 242 00:12:56,720 --> 00:12:59,240 Speaker 1: He hears the Dark Child remix of the nineteen ninety 243 00:12:59,280 --> 00:13:02,079 Speaker 1: nine song if You Had My Love by Jennifer Lopez, which, 244 00:13:02,120 --> 00:13:04,000 Speaker 1: by the way, I love that song, which uses a 245 00:13:04,000 --> 00:13:07,120 Speaker 1: little bit of auto tune. He's also really inspired by 246 00:13:07,160 --> 00:13:09,600 Speaker 1: the R and B graats like Teddy Riley, who used 247 00:13:09,640 --> 00:13:12,160 Speaker 1: things like talk boxes and vocoders, which are kind of 248 00:13:12,200 --> 00:13:14,760 Speaker 1: similar to autotune but a little bit different. T Pain 249 00:13:14,840 --> 00:13:18,640 Speaker 1: records his debut album, Rappa Turnt singer in two thousand 250 00:13:18,640 --> 00:13:20,720 Speaker 1: and six and it gets to thirty three on the 251 00:13:20,760 --> 00:13:24,000 Speaker 1: Billboard two hundred and a certified gold. He releases his 252 00:13:24,040 --> 00:13:27,319 Speaker 1: second album, Epiphany, which is kind of like T Pain's 253 00:13:27,360 --> 00:13:28,160 Speaker 1: magnum opis. 254 00:13:28,320 --> 00:13:29,120 Speaker 2: It's his thriller. 255 00:13:29,480 --> 00:13:31,280 Speaker 1: It's like the album that you think of when you 256 00:13:31,320 --> 00:13:34,000 Speaker 1: think of T Pain. The album includes the song buy 257 00:13:34,040 --> 00:13:35,840 Speaker 1: You a Drank, which is probably. 258 00:13:35,440 --> 00:13:36,199 Speaker 2: His biggest hit. 259 00:13:36,400 --> 00:13:38,600 Speaker 1: The song peaked at number one on the US Billboard 260 00:13:38,600 --> 00:13:41,520 Speaker 1: Hot one hundred, making it his highest charting single as 261 00:13:41,559 --> 00:13:44,320 Speaker 1: a lead artist. So something to know about this is 262 00:13:44,320 --> 00:13:46,920 Speaker 1: that when that album first came out, it wasn't like 263 00:13:47,000 --> 00:13:50,400 Speaker 1: people were criticizing T Pain as like a gimmicky artist. 264 00:13:50,880 --> 00:13:53,880 Speaker 2: The reviews were admittedly mixed, but people. 265 00:13:53,600 --> 00:13:56,600 Speaker 1: Were talking about him as a serious artist to watch, 266 00:13:56,880 --> 00:13:59,520 Speaker 1: not like some kind of a joke one trip pony. 267 00:14:00,000 --> 00:14:01,959 Speaker 1: So something that you really need to know about Tea 268 00:14:02,000 --> 00:14:06,160 Speaker 1: Pain is that he legitimately saw autotune as his thing right. 269 00:14:06,200 --> 00:14:09,599 Speaker 1: He did not see auto tune as popping on to 270 00:14:09,720 --> 00:14:12,720 Speaker 1: a trend or a gimmick or a joke, because it 271 00:14:12,760 --> 00:14:15,400 Speaker 1: was his way of exploring his own voice as a singer. 272 00:14:16,080 --> 00:14:19,000 Speaker 2: But everybody sees how successful. 273 00:14:18,440 --> 00:14:21,160 Speaker 1: Tea Pain is at doing auto tune and they all 274 00:14:21,160 --> 00:14:23,840 Speaker 1: start jumping on the bandwagon. Snoop Dogg does it in 275 00:14:23,840 --> 00:14:26,960 Speaker 1: two thousand and seven on his song Sensual Seduction. Loll 276 00:14:27,000 --> 00:14:28,720 Speaker 1: Wayne does it in two thousand and eight with his 277 00:14:28,800 --> 00:14:32,280 Speaker 1: song Lollipop. Kanye West releases the album Ato Eights and 278 00:14:32,360 --> 00:14:34,520 Speaker 1: Heartbreak in two thousand and eight, a project that t 279 00:14:34,680 --> 00:14:37,440 Speaker 1: Pain actually worked on. What's interesting about that is that 280 00:14:37,480 --> 00:14:41,680 Speaker 1: I've heard te Pain talk about Ato Eights and Heartbreak. 281 00:14:41,200 --> 00:14:43,760 Speaker 2: Even though he was I think a consultant on the album. 282 00:14:44,360 --> 00:14:46,920 Speaker 1: I think he felt some type of way about how 283 00:14:46,960 --> 00:14:48,240 Speaker 1: that album was received. 284 00:14:48,880 --> 00:14:50,119 Speaker 2: He talks about how. 285 00:14:50,120 --> 00:14:53,760 Speaker 1: He didn't feel like Kanye West did auto tune correctly 286 00:14:53,800 --> 00:14:56,560 Speaker 1: on that album. It sounds like Tea Pain thought that 287 00:14:56,600 --> 00:14:59,480 Speaker 1: the critical praise that Atoweights and Heartbreak got should have 288 00:14:59,520 --> 00:15:02,560 Speaker 1: gone to him as a pioneer of the technology. What's 289 00:15:02,600 --> 00:15:05,920 Speaker 1: interesting about this is that people associated Tea Pain specifically 290 00:15:05,960 --> 00:15:09,600 Speaker 1: with autotune and a kind of cheapening of the music 291 00:15:09,640 --> 00:15:13,320 Speaker 1: industry in general. But he was legitimately trying to use 292 00:15:13,320 --> 00:15:15,880 Speaker 1: this technology to create something unique as an artist, just 293 00:15:15,920 --> 00:15:18,360 Speaker 1: like Share was. He said in an interview, like It's 294 00:15:18,360 --> 00:15:20,440 Speaker 1: not like I was telling other artists to do this. 295 00:15:20,680 --> 00:15:22,600 Speaker 1: I was the one doing it. Other people started doing it. 296 00:15:22,640 --> 00:15:23,920 Speaker 1: I didn't tell them to do that. It's not my 297 00:15:23,960 --> 00:15:26,520 Speaker 1: fault that this became a trend. In a really good 298 00:15:26,560 --> 00:15:29,280 Speaker 1: long read about auto tune and pitchfork that will link 299 00:15:29,320 --> 00:15:32,480 Speaker 1: to in the show notes, they talk about how this 300 00:15:32,600 --> 00:15:35,480 Speaker 1: kind of put tea Pain in this like very weird position. 301 00:15:36,080 --> 00:15:38,520 Speaker 1: He was at once a pioneer of this technology but 302 00:15:38,640 --> 00:15:40,440 Speaker 1: also a critic of the way that other people were 303 00:15:40,520 --> 00:15:42,960 Speaker 1: using it. The piece points out that he claimed that 304 00:15:43,000 --> 00:15:46,080 Speaker 1: he spent two years researching auto tune and thinking about it, 305 00:15:46,160 --> 00:15:49,720 Speaker 1: including meeting with doctor Hildebrand, before attempting to use it. 306 00:15:50,320 --> 00:15:52,560 Speaker 1: So when Tea Pain as compared to other artists who 307 00:15:52,560 --> 00:15:55,360 Speaker 1: were jumping on the auto tune trend, he actually feels 308 00:15:55,400 --> 00:15:58,520 Speaker 1: offended by this. He says, a lot of math went 309 00:15:58,560 --> 00:16:01,000 Speaker 1: into that shit. It would take us a billion fucking 310 00:16:01,040 --> 00:16:04,040 Speaker 1: minutes to explain to regular motherfuckers. But I really studied 311 00:16:04,040 --> 00:16:06,240 Speaker 1: that shit. I know why it catches certain notes and 312 00:16:06,240 --> 00:16:07,640 Speaker 1: why it doesn't catch other notes. 313 00:16:08,160 --> 00:16:09,800 Speaker 2: So t Pain really saw. 314 00:16:09,680 --> 00:16:12,920 Speaker 1: Himself as like someone who was learning about a craft 315 00:16:13,120 --> 00:16:16,960 Speaker 1: to explore his own artistry, and when other people saw 316 00:16:17,000 --> 00:16:18,600 Speaker 1: this as a bandwagon to jump on. 317 00:16:19,000 --> 00:16:20,600 Speaker 2: He became the punching bag for it. 318 00:16:21,160 --> 00:16:24,840 Speaker 1: People associating te Pain negatively with autotune was something that he. 319 00:16:24,760 --> 00:16:25,640 Speaker 2: Really struggled with. 320 00:16:26,200 --> 00:16:28,160 Speaker 1: He's talked about this in interviews that there was this 321 00:16:28,280 --> 00:16:31,800 Speaker 1: moment where he meets Usher, the singer Usher, and that 322 00:16:31,960 --> 00:16:36,040 Speaker 1: Usher is the singer that he really respects, and Usher 323 00:16:36,120 --> 00:16:40,600 Speaker 1: essentially personally blames him for a negative shift in the 324 00:16:40,680 --> 00:16:43,400 Speaker 1: music industry. He says that Usher told him that he 325 00:16:43,480 --> 00:16:46,880 Speaker 1: messed up music for real singers, and that Usher as 326 00:16:46,920 --> 00:16:50,680 Speaker 1: this great vocalist who te Pain really respected. In an interview, 327 00:16:50,720 --> 00:16:52,880 Speaker 1: he said, that is the very moment, and I don't 328 00:16:52,880 --> 00:16:54,880 Speaker 1: even think I realized this for a long time, but 329 00:16:55,000 --> 00:16:57,360 Speaker 1: that very moment started a four year depression for me. 330 00:16:58,080 --> 00:17:02,560 Speaker 1: And just fyi, Usher famously used auto tune in his 331 00:17:02,640 --> 00:17:05,200 Speaker 1: song oh My God. So it definitely is a thing 332 00:17:05,200 --> 00:17:10,480 Speaker 1: where artists who vocally criticized autotune even before it was 333 00:17:10,480 --> 00:17:13,080 Speaker 1: so commonplace, also use autotune. 334 00:17:15,640 --> 00:17:28,600 Speaker 2: Let's take a quick break at our back. 335 00:17:30,200 --> 00:17:33,639 Speaker 1: So I knew that auto tune was like notoriously controversial 336 00:17:33,680 --> 00:17:35,879 Speaker 1: in the music industry when I was first doing the 337 00:17:35,920 --> 00:17:39,280 Speaker 1: research for this episode, I thought, oh, people just find 338 00:17:39,320 --> 00:17:41,520 Speaker 1: it a little bit lazy, or a little bit gimmicky, 339 00:17:41,560 --> 00:17:43,840 Speaker 1: or they just have a negative perception of it. What 340 00:17:43,920 --> 00:17:46,440 Speaker 1: I did not realize is how much of an organized 341 00:17:46,600 --> 00:17:51,960 Speaker 1: negative publicity campaign surrounded autotune in the two thousands. At 342 00:17:51,960 --> 00:17:54,240 Speaker 1: the Grammys in two thousand and nine, the band Deathcab 343 00:17:54,280 --> 00:17:57,880 Speaker 1: for Quti showed up wearing blue ribbons on their lapels 344 00:17:57,920 --> 00:18:00,600 Speaker 1: to protest the use of autotune in the muse industry. 345 00:18:01,040 --> 00:18:04,200 Speaker 1: This sounds kind of funny, but it was very serious. 346 00:18:04,359 --> 00:18:07,720 Speaker 1: There's an MTV dot com article about it titled Deathcab 347 00:18:07,760 --> 00:18:11,399 Speaker 1: for Cutie raise Awareness about autotune abuse. Enough is Enough? 348 00:18:11,560 --> 00:18:13,720 Speaker 1: And in the article, Ben Gibberd, the lead of Death 349 00:18:13,720 --> 00:18:16,480 Speaker 1: Caab for Cuti, says, we're here to raise awareness about 350 00:18:16,480 --> 00:18:19,080 Speaker 1: auto tune abuse. I think it's over the last ten 351 00:18:19,160 --> 00:18:22,120 Speaker 1: years we've seen good musicians being affected by this new 352 00:18:22,160 --> 00:18:24,920 Speaker 1: found digital manipulation of the human voice, and we feel 353 00:18:25,080 --> 00:18:28,359 Speaker 1: enough is enough. Let's raise awareness, let's stop this, let's 354 00:18:28,359 --> 00:18:30,199 Speaker 1: bring back the blue note, and let's really try to 355 00:18:30,200 --> 00:18:32,720 Speaker 1: get music back to its roots of having actual people 356 00:18:32,920 --> 00:18:36,359 Speaker 1: who sound like actual human beings. I read a quote 357 00:18:36,400 --> 00:18:39,760 Speaker 1: from the singer Nico Case, who I know you've really like, 358 00:18:39,840 --> 00:18:40,720 Speaker 1: Nicoka's right. 359 00:18:41,119 --> 00:18:45,000 Speaker 3: That's true, I do, and I to my knowledge, I've 360 00:18:45,000 --> 00:18:47,880 Speaker 3: never heard her use AutoTunes. So I'm a little concerned 361 00:18:47,920 --> 00:18:48,920 Speaker 3: where you're going with this. 362 00:18:49,560 --> 00:18:52,320 Speaker 1: Well, Nicokease does not use autotune, and in two thousand 363 00:18:52,320 --> 00:18:55,159 Speaker 1: and six she told Pitchfork quote, I'm not a perfect 364 00:18:55,240 --> 00:18:57,159 Speaker 1: note hitter either, but I'm not going to cover it 365 00:18:57,240 --> 00:18:59,920 Speaker 1: up with auto tune. Everybody uses it too, I want. 366 00:19:00,000 --> 00:19:02,280 Speaker 1: I asked the studio guy in Toronto how many people 367 00:19:02,280 --> 00:19:05,240 Speaker 1: don't use autotune and he said, you and Nellie Fortado 368 00:19:05,320 --> 00:19:07,440 Speaker 1: are the only two people who've never used it in here. 369 00:19:07,880 --> 00:19:10,119 Speaker 1: Even though I'm not into Nelly Fortato, it kind of 370 00:19:10,160 --> 00:19:12,679 Speaker 1: made me respect her. It's cool that she has some integrity. 371 00:19:13,320 --> 00:19:15,560 Speaker 1: And so I think what we really are seeing here 372 00:19:15,720 --> 00:19:20,400 Speaker 1: is like auto tune being connected to like a lack 373 00:19:20,440 --> 00:19:23,679 Speaker 1: of integrity or a lack of authenticity. This was the 374 00:19:23,720 --> 00:19:26,600 Speaker 1: two thousands at a time where I think that there 375 00:19:26,680 --> 00:19:32,160 Speaker 1: was a real binary between quote, real musicians, real artists 376 00:19:32,200 --> 00:19:36,960 Speaker 1: and like pop poppy, bubblegummy kind of like gimmicks. And 377 00:19:37,000 --> 00:19:40,720 Speaker 1: so I think part of the backlash was about this 378 00:19:41,040 --> 00:19:45,160 Speaker 1: was a response to people being like, oh, now, these 379 00:19:45,200 --> 00:19:48,760 Speaker 1: new fake musicians they're not real musicians. They're using auto tune. 380 00:19:48,800 --> 00:19:52,600 Speaker 1: They're fakers. Real music is like real music people using 381 00:19:52,600 --> 00:19:59,560 Speaker 1: their real voice. And I can understand that sentiment, but honestly, 382 00:19:59,600 --> 00:20:02,840 Speaker 1: there's nothing natural about the human singing voice. 383 00:20:02,920 --> 00:20:05,680 Speaker 2: Like your voice is a tool, it's an instrument. 384 00:20:05,720 --> 00:20:09,520 Speaker 1: And so this idea that any manipulation of that means 385 00:20:09,520 --> 00:20:12,000 Speaker 1: that you're like not a real artist, I find sort 386 00:20:12,000 --> 00:20:12,560 Speaker 1: of interesting. 387 00:20:13,000 --> 00:20:16,760 Speaker 3: Yeah, that adjective reel comes up on the show a lot, right, 388 00:20:16,920 --> 00:20:21,159 Speaker 3: just to separate us from them pretty often kind of 389 00:20:21,200 --> 00:20:23,440 Speaker 3: makes you wonder, wh who's the us and who's the 390 00:20:23,440 --> 00:20:24,560 Speaker 3: them in that scenario. 391 00:20:24,840 --> 00:20:26,760 Speaker 1: So in two thousand and nine, the same year that 392 00:20:26,840 --> 00:20:29,879 Speaker 1: Death Cab for Cut shows up wearing those blue lapel 393 00:20:29,960 --> 00:20:33,280 Speaker 1: pens to protest autotune, Jay Z released the lead single 394 00:20:33,440 --> 00:20:37,720 Speaker 1: of his album, The Blueprint three Da Death of auto Tune, 395 00:20:38,440 --> 00:20:40,280 Speaker 1: and he said that he thought that autotune was like 396 00:20:40,280 --> 00:20:43,520 Speaker 1: an overused gimmick. In that song, he calls tea pain 397 00:20:43,640 --> 00:20:47,359 Speaker 1: out by name. Also, in twenty ten, Time magazine includes 398 00:20:47,400 --> 00:20:51,040 Speaker 1: autotune in their list of the fifty worst inventions. On 399 00:20:51,080 --> 00:20:55,320 Speaker 1: the same list with Agent Orange and subprime mortgages. 400 00:20:55,680 --> 00:21:00,879 Speaker 3: Not agent Orange, that pretty harsh. 401 00:21:00,040 --> 00:21:05,320 Speaker 2: I feel like Agent Orange has been responsible for more 402 00:21:05,400 --> 00:21:08,520 Speaker 2: harm in the world than AUDI. 403 00:21:08,600 --> 00:21:12,040 Speaker 3: Yeah, like it literally killed a lot of people horrifically 404 00:21:12,520 --> 00:21:18,520 Speaker 3: and caused a lot of cancer. I mean, I don't 405 00:21:18,520 --> 00:21:21,840 Speaker 3: think AutoTunes killed anybody time. 406 00:21:23,480 --> 00:21:26,119 Speaker 1: So just like in conversations that we're having around AI 407 00:21:26,200 --> 00:21:28,360 Speaker 1: and whether or not images that have been manipulated using 408 00:21:28,400 --> 00:21:30,520 Speaker 1: AI should be labeled, which, by the way, we think 409 00:21:30,560 --> 00:21:32,480 Speaker 1: they should. There was even a call to have it 410 00:21:32,560 --> 00:21:36,200 Speaker 1: be labeled whatever a live performance used auto tune. Singer 411 00:21:36,240 --> 00:21:39,639 Speaker 1: songwriter David Mendel started the live means Live campaign and 412 00:21:39,680 --> 00:21:41,640 Speaker 1: he wanted there to be a logo that said live 413 00:21:41,720 --> 00:21:44,240 Speaker 1: means live to let the audience know that when that 414 00:21:44,280 --> 00:21:47,159 Speaker 1: person is performing, no auto tune is being used, no 415 00:21:47,240 --> 00:21:50,040 Speaker 1: backing track is being used. It is one hundred percent live. 416 00:21:50,720 --> 00:21:52,840 Speaker 1: And I think that goes back to what we were 417 00:21:52,880 --> 00:21:55,960 Speaker 1: just talking about. Like in the two thousands, there was this, 418 00:21:57,040 --> 00:22:01,280 Speaker 1: I would argue, false dichotomy between author identic musicians and 419 00:22:01,320 --> 00:22:04,720 Speaker 1: inauthentic musicians. You know, you had a lot of rappers 420 00:22:04,760 --> 00:22:09,399 Speaker 1: calling other rappers quote ringtone rappers, which was like a 421 00:22:10,400 --> 00:22:14,160 Speaker 1: negative term for a rapper that was like too poppy, 422 00:22:14,280 --> 00:22:17,359 Speaker 1: too bubblegum, just trying to get money as opposed to 423 00:22:17,440 --> 00:22:19,840 Speaker 1: like real rappers who are. 424 00:22:20,080 --> 00:22:21,360 Speaker 2: Doing something else entirely. 425 00:22:21,800 --> 00:22:23,600 Speaker 1: You have a lot of artists being called out for 426 00:22:23,720 --> 00:22:27,159 Speaker 1: using you know, backup vocals tracks and they're in their 427 00:22:27,359 --> 00:22:31,879 Speaker 1: live performances. In twenty thirteen, Beyonce famously was lip syncing 428 00:22:31,920 --> 00:22:35,359 Speaker 1: to a vocal track when she performed at Obama's inauguration, 429 00:22:36,000 --> 00:22:38,120 Speaker 1: and then she had to do a press conference where 430 00:22:38,160 --> 00:22:41,720 Speaker 1: she belts out the national anthem a cappella and is 431 00:22:41,800 --> 00:22:44,360 Speaker 1: like any questions, just to prove that she actually can 432 00:22:44,440 --> 00:22:48,160 Speaker 1: do it. Which, by the way, I've often found conversations 433 00:22:48,200 --> 00:22:52,160 Speaker 1: around you know, having a backtrack going for a live 434 00:22:52,200 --> 00:22:56,439 Speaker 1: performance like that inauguration. I was at that inauguration and 435 00:22:56,480 --> 00:22:59,280 Speaker 1: it was a very cold day. The weather does stuff 436 00:22:59,320 --> 00:23:01,320 Speaker 1: to your voice, Like I don't know. I feel like 437 00:23:01,400 --> 00:23:06,120 Speaker 1: if you're performing an outdoor important inauguration, there's no shame 438 00:23:06,160 --> 00:23:08,359 Speaker 1: in using a backtrack for a big moment like an 439 00:23:08,359 --> 00:23:10,879 Speaker 1: inauguration to just need to sound perfect, and if you 440 00:23:11,119 --> 00:23:13,360 Speaker 1: use a backtrack to accomplish that, I don't. 441 00:23:13,160 --> 00:23:13,960 Speaker 2: Think it's the end of the world. 442 00:23:14,000 --> 00:23:17,600 Speaker 1: Like I think it's sometimes the accusations are a little much, 443 00:23:18,200 --> 00:23:21,200 Speaker 1: and I do think that some of this climate around 444 00:23:21,720 --> 00:23:26,080 Speaker 1: inauthentic and authentic musicians does to me to seem like 445 00:23:26,119 --> 00:23:30,679 Speaker 1: a way to criticize black musicians and poppy women musicians. 446 00:23:31,080 --> 00:23:34,240 Speaker 1: Like you can't help but see people who see themselves 447 00:23:34,280 --> 00:23:38,920 Speaker 1: as like real authentic rockers calling out people who use 448 00:23:39,600 --> 00:23:43,760 Speaker 1: auto tune. And it's curious to me who got caught 449 00:23:43,880 --> 00:23:47,960 Speaker 1: up in the auto tune criticism, because like in radioheads 450 00:23:47,960 --> 00:23:51,080 Speaker 1: two thousand and one album Amnisiak, they use auto tune. 451 00:23:51,760 --> 00:23:53,879 Speaker 1: Tom Yorke described the use of auto tune on that 452 00:23:53,960 --> 00:23:57,480 Speaker 1: album as quote, auto tune desperately tries to search for 453 00:23:57,520 --> 00:24:00,200 Speaker 1: the music in your speech and produces noeset random. 454 00:24:00,359 --> 00:24:02,920 Speaker 2: If you've assigned it a key, you've got music right. 455 00:24:02,960 --> 00:24:06,840 Speaker 1: And so nobody was calling out when all this auto 456 00:24:06,920 --> 00:24:10,439 Speaker 1: tune criticism was happening. Nobody was calling out Radiohead for 457 00:24:10,560 --> 00:24:12,840 Speaker 1: using it. It was interesting to me who got called 458 00:24:12,840 --> 00:24:16,080 Speaker 1: out publicly and who didn't. In that really good pitchwork 459 00:24:16,080 --> 00:24:18,679 Speaker 1: piece I mentioned, they put it this way. Much of 460 00:24:18,720 --> 00:24:22,200 Speaker 1: this anti autotune sentiment presented the idea that the technology 461 00:24:22,280 --> 00:24:26,280 Speaker 1: is a dehumanizing deception foisted upon the public. Attempting to 462 00:24:26,280 --> 00:24:29,919 Speaker 1: deflect this angle of attack, Hildbrand once offered up an 463 00:24:29,960 --> 00:24:33,400 Speaker 1: analogy with a generally accepted form of everyday artifice, asking 464 00:24:33,760 --> 00:24:37,280 Speaker 1: my wife wears makeup, does that make her evil? Perhaps 465 00:24:37,320 --> 00:24:40,120 Speaker 1: because of Shar's involvement in Autotune's debut on the world 466 00:24:40,119 --> 00:24:44,440 Speaker 1: pop stage, critics have often connected pitch correction and cosmetic surgery, 467 00:24:44,480 --> 00:24:48,080 Speaker 1: comparing the effects to botox, face peels, college and injections 468 00:24:48,119 --> 00:24:51,600 Speaker 1: and the rest in the video for believe Share actually 469 00:24:51,600 --> 00:24:54,679 Speaker 1: looks how auto Tune sounds. The combination of three levels 470 00:24:54,680 --> 00:24:58,440 Speaker 1: of enhancement, surgery, makeup, and that old trick of bright 471 00:24:58,520 --> 00:25:01,360 Speaker 1: lights that flatten the skin surface into a blank dazzle 472 00:25:01,840 --> 00:25:03,840 Speaker 1: means that her face and her voice seemed to be 473 00:25:03,880 --> 00:25:07,080 Speaker 1: made out of the same immaterial substance. If the believed 474 00:25:07,119 --> 00:25:10,440 Speaker 1: promo was produced today, a fourth level of falsification would 475 00:25:10,440 --> 00:25:14,920 Speaker 1: be routinely applied digital post production procedures like motion retouching 476 00:25:15,200 --> 00:25:18,160 Speaker 1: or colorizing that operate at the levels of pixels rather 477 00:25:18,200 --> 00:25:22,000 Speaker 1: than poores, fundamentally altering the integrity of the image. The 478 00:25:22,080 --> 00:25:24,920 Speaker 1: taste for these effects and the revulsion against them are 479 00:25:24,960 --> 00:25:28,840 Speaker 1: part of the same syndrome, reflecting a deeply conflicted confusion 480 00:25:28,920 --> 00:25:32,720 Speaker 1: in our desires. Simultaneously craving the real and the true, 481 00:25:33,000 --> 00:25:36,399 Speaker 1: while continuing to be seduced by digital's perfection and the 482 00:25:36,400 --> 00:25:39,640 Speaker 1: facility and flexibility of use that it offers. That's why 483 00:25:39,680 --> 00:25:43,240 Speaker 1: young hipsters buy overpriced vinyl for the aura of authenticity 484 00:25:43,240 --> 00:25:47,200 Speaker 1: and analog warmth, but in practice use the download codes 485 00:25:47,240 --> 00:25:49,639 Speaker 1: to listen to the music on an everyday level. So 486 00:25:49,800 --> 00:25:52,520 Speaker 1: I definitely agree with that take, and I think that 487 00:25:52,600 --> 00:25:55,440 Speaker 1: our response to autotune and the rise of auto tune 488 00:25:55,440 --> 00:25:58,760 Speaker 1: culturally does say something about us that we want it 489 00:25:58,840 --> 00:25:59,479 Speaker 1: both ways. 490 00:25:59,720 --> 00:26:02,520 Speaker 2: We want the slickness. 491 00:26:01,840 --> 00:26:05,280 Speaker 1: Of something that is clearly digitized, while also being able 492 00:26:05,320 --> 00:26:08,920 Speaker 1: to call it out for being inauthentic or being repulsed 493 00:26:08,960 --> 00:26:09,280 Speaker 1: by it. 494 00:26:09,280 --> 00:26:11,280 Speaker 2: It's like this real duality of life. 495 00:26:11,280 --> 00:26:13,560 Speaker 1: We want this, we create this, we have been trained 496 00:26:13,560 --> 00:26:16,639 Speaker 1: as audiences to like this and respond to this, but 497 00:26:16,800 --> 00:26:21,040 Speaker 1: also we find that very dynamic to be repulsive. But 498 00:26:21,160 --> 00:26:25,320 Speaker 1: here's my thing, because Sharon T Pain or innovators, today, 499 00:26:25,440 --> 00:26:28,399 Speaker 1: autotune is so commonplace that I would be willing to 500 00:26:28,440 --> 00:26:30,919 Speaker 1: bet that most people don't even know that they're hearing 501 00:26:31,000 --> 00:26:34,200 Speaker 1: songs that have been pitch corrected by autotune when they 502 00:26:34,200 --> 00:26:37,800 Speaker 1: hear it. It is commonplace across all genres of music. 503 00:26:37,880 --> 00:26:40,800 Speaker 1: Even though we associate it more heavily with hip hop, 504 00:26:41,080 --> 00:26:44,200 Speaker 1: it is in all different kinds of music, and autotune 505 00:26:44,240 --> 00:26:47,040 Speaker 1: is not only used for things like pitch correction or 506 00:26:47,040 --> 00:26:50,280 Speaker 1: making a singer sound better. Artists also use autotune in 507 00:26:50,320 --> 00:26:53,119 Speaker 1: all kinds of unique ways. One of my favorites is 508 00:26:53,160 --> 00:26:56,160 Speaker 1: the Kate Busch song A Deeper Understanding, which was originally 509 00:26:56,240 --> 00:26:58,960 Speaker 1: released back in nineteen ninety eight. Kate Bush has said 510 00:26:58,960 --> 00:27:01,000 Speaker 1: that the song is a critics seek about our relationship 511 00:27:01,040 --> 00:27:05,000 Speaker 1: with technology, saying this is about people, well, about the 512 00:27:05,040 --> 00:27:07,440 Speaker 1: modern situation where more and where people are having less 513 00:27:07,440 --> 00:27:10,280 Speaker 1: and less contact with human beings. We spend all day 514 00:27:10,320 --> 00:27:13,320 Speaker 1: with machines, all night with machines. You know, all day 515 00:27:13,320 --> 00:27:14,919 Speaker 1: you're on the phone, and all night you're watching the 516 00:27:14,920 --> 00:27:18,240 Speaker 1: telly press a button. And this happens, and this idea 517 00:27:18,280 --> 00:27:20,399 Speaker 1: of someone who spends all their time with the computer, 518 00:27:20,680 --> 00:27:22,680 Speaker 1: and like a lot of people, they spend an obsessive 519 00:27:22,720 --> 00:27:25,679 Speaker 1: amount of time with their computers. People really build up 520 00:27:25,720 --> 00:27:29,640 Speaker 1: heavy relationships with their computers. So the song initially had 521 00:27:29,720 --> 00:27:32,919 Speaker 1: this computer voice on the track. In the original version, 522 00:27:33,000 --> 00:27:35,600 Speaker 1: it's voiced by her son, but when she revisited that 523 00:27:35,680 --> 00:27:38,879 Speaker 1: song in twenty eleven. Pitchwork reports that she used auto 524 00:27:38,920 --> 00:27:41,360 Speaker 1: tune to make the serie like voice of the computer 525 00:27:41,800 --> 00:27:46,120 Speaker 1: sound like a guardian angel offering saragate solace and counterfeit company, 526 00:27:46,400 --> 00:27:49,840 Speaker 1: saying hello, I know that you're unhappy. I bring you 527 00:27:49,880 --> 00:27:54,520 Speaker 1: love and deeper understanding. So she used a heavily criticized 528 00:27:54,560 --> 00:28:00,159 Speaker 1: technology to critique our relationship with technology brilliant. So I 529 00:28:00,200 --> 00:28:02,560 Speaker 1: think there's always going to be artists who find a 530 00:28:02,600 --> 00:28:05,400 Speaker 1: way to use new technology in ways that are creative, 531 00:28:05,440 --> 00:28:07,480 Speaker 1: because that's what creative people. 532 00:28:07,400 --> 00:28:08,240 Speaker 2: Have always done. 533 00:28:08,800 --> 00:28:11,720 Speaker 1: I saw this really interesting point in Traptal, a newsletter 534 00:28:11,720 --> 00:28:14,199 Speaker 1: that covers the business of hip hop by Dan Runzi. 535 00:28:14,760 --> 00:28:18,080 Speaker 1: So Dan points out that venture capitalist Chris Dixon has 536 00:28:18,160 --> 00:28:23,040 Speaker 1: this concept about disruptive technologies that disruptive technologies always start 537 00:28:23,080 --> 00:28:25,640 Speaker 1: out by looking like a toy. So when they come 538 00:28:25,680 --> 00:28:28,320 Speaker 1: out like the iPhone, I remember someone saying I don't 539 00:28:28,320 --> 00:28:30,120 Speaker 1: want to get an iPhone. It looks like a toy. 540 00:28:30,119 --> 00:28:32,720 Speaker 1: It looks like a phone that like a child would use. 541 00:28:33,160 --> 00:28:37,080 Speaker 1: And I think there's something to this idea that technologies 542 00:28:37,200 --> 00:28:39,200 Speaker 1: that are going to be disruptive and really take off 543 00:28:39,200 --> 00:28:41,920 Speaker 1: as the next big thing. They do often start out 544 00:28:42,040 --> 00:28:45,880 Speaker 1: by looking like toys, and so when something looks like 545 00:28:45,920 --> 00:28:49,120 Speaker 1: a toy, it can be dismissed while also being phased 546 00:28:49,160 --> 00:28:51,560 Speaker 1: in as something that's going to be disruptive. And so 547 00:28:51,640 --> 00:28:54,920 Speaker 1: I think that auto tune was dismissed as this gimmick, right, 548 00:28:55,000 --> 00:28:58,720 Speaker 1: this joke, and now it's everywhere, even though so many 549 00:28:58,760 --> 00:28:59,920 Speaker 1: people initially dismissed it. 550 00:29:00,960 --> 00:29:05,280 Speaker 3: Yeah, that's a really interesting point, and it does really 551 00:29:05,280 --> 00:29:08,160 Speaker 3: connect to the analogy you were making to AI earlier. 552 00:29:08,600 --> 00:29:13,320 Speaker 3: Right when AI first became a big thing, we were 553 00:29:13,360 --> 00:29:17,320 Speaker 3: all using DALLI to create images that were like sort 554 00:29:17,360 --> 00:29:20,280 Speaker 3: of jokey, and it was like and like similar with 555 00:29:20,360 --> 00:29:25,800 Speaker 3: chat GPT, just asking it questions for novelty, for funzies, 556 00:29:26,280 --> 00:29:28,440 Speaker 3: and so it really does have this quality of feeling 557 00:29:28,920 --> 00:29:34,560 Speaker 3: a little bit like a toy. But I suppose, you know, quietly, 558 00:29:35,320 --> 00:29:37,960 Speaker 3: on laptops around the world, people are using it for 559 00:29:38,040 --> 00:29:39,480 Speaker 3: much more serious purposes. 560 00:29:40,280 --> 00:29:40,520 Speaker 2: Yeah. 561 00:29:40,560 --> 00:29:45,000 Speaker 1: I really think the comparison between auto tune and AI 562 00:29:45,040 --> 00:29:49,000 Speaker 1: as technologies inso much that they will transform these creative industries. 563 00:29:49,160 --> 00:29:51,480 Speaker 1: And I think the reason why I connect those two 564 00:29:51,520 --> 00:29:55,600 Speaker 1: conversations in my head is because as we're having these 565 00:29:55,640 --> 00:29:59,360 Speaker 1: conversations about how AI is going to transform the creative 566 00:29:59,400 --> 00:30:04,440 Speaker 1: spaces hip hop, music, the arts, film, television. A lot 567 00:30:04,440 --> 00:30:08,400 Speaker 1: of those conversations have been really pessimistic, and I totally 568 00:30:08,480 --> 00:30:12,600 Speaker 1: get it right, Like the striking screenwriters and actors are 569 00:30:12,640 --> 00:30:16,720 Speaker 1: not wrong to be really concerned about the way that 570 00:30:16,760 --> 00:30:21,520 Speaker 1: AI will impact their industry. Those concerns are absolutely fair 571 00:30:21,600 --> 00:30:26,200 Speaker 1: and grounded. However, I think that there's always going to 572 00:30:26,240 --> 00:30:30,800 Speaker 1: be people who find ways to use technology to create 573 00:30:30,840 --> 00:30:33,239 Speaker 1: something better, and so I think that we're at this 574 00:30:33,280 --> 00:30:37,240 Speaker 1: pivotal point in the conversation around AI and how it's 575 00:30:37,280 --> 00:30:41,480 Speaker 1: going to shape those fields where the question is do 576 00:30:41,520 --> 00:30:44,320 Speaker 1: we want this technology to be used to exploit or 577 00:30:44,320 --> 00:30:47,640 Speaker 1: to innovate. I think the screenwriters are saying, let's see 578 00:30:47,680 --> 00:30:50,840 Speaker 1: how this technology can be used to innovate. If AI 579 00:30:50,920 --> 00:30:53,360 Speaker 1: is going to help me punch up a script, that's great, 580 00:30:54,160 --> 00:30:58,480 Speaker 1: the powers that be should not bake my exploitation into 581 00:30:58,520 --> 00:31:01,000 Speaker 1: that model. I think that that's where and so this 582 00:31:01,080 --> 00:31:04,960 Speaker 1: might surprise people, but I am kind of positive, kind 583 00:31:04,960 --> 00:31:08,240 Speaker 1: of cautiously hopeful about how we see this play out, 584 00:31:08,520 --> 00:31:10,840 Speaker 1: Because if there's anybody who can find a way to 585 00:31:10,880 --> 00:31:14,200 Speaker 1: innovate with new technologies, it's creatives, it's hip hop artists, 586 00:31:14,240 --> 00:31:17,440 Speaker 1: it's people like tea paid and people like share, it's innovators, 587 00:31:17,640 --> 00:31:19,920 Speaker 1: and so I really want those people to be the 588 00:31:19,920 --> 00:31:22,880 Speaker 1: ones who are leading the way in terms of how 589 00:31:23,000 --> 00:31:26,120 Speaker 1: these how new technologies do shape creative fields, Like that's 590 00:31:26,160 --> 00:31:28,080 Speaker 1: what I want to see. How can they be used 591 00:31:28,120 --> 00:31:31,080 Speaker 1: to innovate, not exploit. And it really comes down to 592 00:31:31,120 --> 00:31:34,120 Speaker 1: something that tech historian Claire Evan said in the very 593 00:31:34,160 --> 00:31:37,600 Speaker 1: first ever episode of their No Girls on the Internet. 594 00:31:41,160 --> 00:31:43,200 Speaker 4: There have been many instances in the history of music 595 00:31:43,240 --> 00:31:46,040 Speaker 4: when a new technology has come along that extensibly is 596 00:31:46,080 --> 00:31:49,240 Speaker 4: there to displace the musician. For example, the drum machine 597 00:31:49,360 --> 00:31:51,920 Speaker 4: or the synthesizer. You know, these are tools that we're 598 00:31:51,960 --> 00:31:56,400 Speaker 4: designed to replace session musicians with an easier, cheaper version 599 00:31:56,560 --> 00:31:58,960 Speaker 4: kind of automation of their labor. In fact, even in 600 00:31:59,000 --> 00:32:02,560 Speaker 4: the eighties, like the Britishmusicians Union tried to ban synthesizers. 601 00:32:02,560 --> 00:32:05,840 Speaker 4: But what artists and musicians did was instead of allowing 602 00:32:05,840 --> 00:32:08,560 Speaker 4: those tools to replace them, they took control of them. 603 00:32:09,000 --> 00:32:11,760 Speaker 4: And you know, they took drum machines, and they took synthesizers, 604 00:32:11,760 --> 00:32:14,600 Speaker 4: and they invented Detroit Techno, and they invented new waves, 605 00:32:14,680 --> 00:32:17,680 Speaker 4: and they invented hip hop, and they invented you know, 606 00:32:17,920 --> 00:32:21,440 Speaker 4: electronic music as it exists today, and as many manifestations, 607 00:32:21,680 --> 00:32:23,840 Speaker 4: they kind of took the thing that was threatening them 608 00:32:23,840 --> 00:32:26,840 Speaker 4: with displacement and incorporated it into what they were doing 609 00:32:26,880 --> 00:32:29,200 Speaker 4: and made it essential to who they were and used 610 00:32:29,200 --> 00:32:31,720 Speaker 4: it to invent something new that they were integrally as 611 00:32:31,800 --> 00:32:34,880 Speaker 4: human beings involved with. And I think that that act 612 00:32:34,920 --> 00:32:37,520 Speaker 4: of kind of like I don't know, like like jumping 613 00:32:37,560 --> 00:32:40,560 Speaker 4: on the grenade or something, is like a really beautiful 614 00:32:40,720 --> 00:32:44,880 Speaker 4: thing that artists always do, willingly or unwillingly when they 615 00:32:44,920 --> 00:32:47,520 Speaker 4: are faced with new technology. And I think when new 616 00:32:47,520 --> 00:32:49,960 Speaker 4: technology comes along, you always have that choice. Are you 617 00:32:50,000 --> 00:32:51,920 Speaker 4: going to let it displace you or are you going 618 00:32:51,960 --> 00:32:53,480 Speaker 4: to let it intimidate you? Or are you going to 619 00:32:53,520 --> 00:32:57,120 Speaker 4: take it, you know, jump on it, find some new 620 00:32:57,200 --> 00:32:58,800 Speaker 4: use for it, and make it part of who you are, 621 00:32:59,120 --> 00:33:00,760 Speaker 4: and give it back to the world in a new form. 622 00:33:01,240 --> 00:33:03,560 Speaker 4: That is all that choice is always present, and I 623 00:33:03,600 --> 00:33:05,080 Speaker 4: think that's what I try to do in my work 624 00:33:05,120 --> 00:33:07,720 Speaker 4: across the board, and I think it's the only way 625 00:33:07,760 --> 00:33:10,680 Speaker 4: that we're going to kind of keep on top of 626 00:33:10,720 --> 00:33:13,800 Speaker 4: all of this technology. And I think it's also very human. 627 00:33:13,920 --> 00:33:18,800 Speaker 4: I think it's what people always do. We are always 628 00:33:18,880 --> 00:33:22,880 Speaker 4: trying to create systems of meaning and beauty out of 629 00:33:23,120 --> 00:33:25,840 Speaker 4: what is coming up ahead. And I think that will 630 00:33:25,840 --> 00:33:26,560 Speaker 4: never change. 631 00:33:27,080 --> 00:33:30,200 Speaker 1: Claire really put that so beautifully, and that is a 632 00:33:30,240 --> 00:33:32,360 Speaker 1: sentiment that is echoed by one of my favorite bands, 633 00:33:32,400 --> 00:33:35,320 Speaker 1: daff Punk, who used auto tune in their song one 634 00:33:35,360 --> 00:33:38,320 Speaker 1: More Time. They said a lot of people complain about 635 00:33:38,320 --> 00:33:40,440 Speaker 1: the musicians using auto tune. It reminds me of the 636 00:33:40,480 --> 00:33:43,400 Speaker 1: late seventies when musicians in France tried to ban the synthesizer. 637 00:33:43,680 --> 00:33:45,440 Speaker 1: What they didn't see was that you could use those 638 00:33:45,480 --> 00:33:47,840 Speaker 1: tools in a new way instead of just replacing the 639 00:33:47,880 --> 00:33:49,080 Speaker 1: instruments that came before. 640 00:33:49,680 --> 00:33:52,280 Speaker 2: And so I think that's where we are right now. 641 00:33:52,800 --> 00:33:55,680 Speaker 1: We are at the crossroads with so many technologies of 642 00:33:56,520 --> 00:33:58,560 Speaker 1: are we going to use this to innovate or are 643 00:33:58,600 --> 00:34:01,080 Speaker 1: we going to use this to exploit? And that is 644 00:34:01,120 --> 00:34:03,960 Speaker 1: the question that we are asking right now about new technologies, 645 00:34:04,240 --> 00:34:06,840 Speaker 1: and that is a question that innovators like Share and 646 00:34:06,960 --> 00:34:14,959 Speaker 1: Tea Pain answered with a resounding innovator. Got a story 647 00:34:15,000 --> 00:34:16,880 Speaker 1: about an interesting thing in tech, or just want to 648 00:34:16,880 --> 00:34:19,280 Speaker 1: say hi, You can reach us at Hello at tegody 649 00:34:19,280 --> 00:34:21,840 Speaker 1: dot com. You can also find transcripts for today's episode 650 00:34:21,880 --> 00:34:24,080 Speaker 1: at tenggody dot com. There Are No Girls on the 651 00:34:24,080 --> 00:34:26,720 Speaker 1: Internet was created by me Bridget Tod. It's a production 652 00:34:26,760 --> 00:34:30,560 Speaker 1: of iHeartRadio and Unbossed Creative. Jonathan Strickland is our executive producer. 653 00:34:30,880 --> 00:34:34,120 Speaker 1: Tari Harrison is our producer and sound engineer. Michael Almado 654 00:34:34,200 --> 00:34:37,719 Speaker 1: is our contributing producer. I'm your host, Bridget Tod. If 655 00:34:37,760 --> 00:34:39,480 Speaker 1: you want to help us grow, rate and review us 656 00:34:39,480 --> 00:34:43,120 Speaker 1: on Apple Podcasts. For more podcasts from iHeartRadio, check out 657 00:34:43,120 --> 00:34:57,239 Speaker 1: the iHeartRadio app, Apple podcast, or wherever you get your podcasts.