1 00:00:01,720 --> 00:00:04,880 Speaker 1: Cool Media. 2 00:00:05,040 --> 00:00:08,560 Speaker 2: Welcome back to it could happen here a podcast about it, which, 3 00:00:08,640 --> 00:00:12,440 Speaker 2: in this week's case is the Consumer Electronics Show is 4 00:00:12,520 --> 00:00:15,720 Speaker 2: happening here, and yeah, we're here to talk about things 5 00:00:15,720 --> 00:00:18,960 Speaker 2: falling apart, and again, in this case that's the tech industry, 6 00:00:19,079 --> 00:00:21,720 Speaker 2: because the story this CES, as it has been for 7 00:00:21,720 --> 00:00:24,880 Speaker 2: the last several cees, is is that the continuing degradation 8 00:00:25,480 --> 00:00:28,840 Speaker 2: of big tech as it seeks more places to get 9 00:00:28,880 --> 00:00:31,800 Speaker 2: money from while providing less and less utility to the 10 00:00:31,800 --> 00:00:34,520 Speaker 2: people that it needs to give it money. And every 11 00:00:34,560 --> 00:00:37,160 Speaker 2: CEES at some point I find myself face to face 12 00:00:37,200 --> 00:00:39,879 Speaker 2: with something that makes me say I've now seen the 13 00:00:40,040 --> 00:00:43,880 Speaker 2: silliest thing I've ever seen. And this year that experience 14 00:00:43,920 --> 00:00:46,720 Speaker 2: happened for the first time within thirty minutes of the 15 00:00:46,720 --> 00:00:49,239 Speaker 2: first half day. And I'm going to talk about that 16 00:00:49,520 --> 00:00:53,479 Speaker 2: and show some videos to my panelists here, which of 17 00:00:53,479 --> 00:00:56,400 Speaker 2: course are the great ed Zeitron, It's me, I'm here, 18 00:00:56,840 --> 00:00:58,280 Speaker 2: the pretty good Garrison Davis. 19 00:00:58,320 --> 00:01:03,320 Speaker 3: Okay, okay, all right, all right, Boddy them. 20 00:01:02,680 --> 00:01:05,959 Speaker 2: And the super enumerate supernumerary. I'm sorry I messed up 21 00:01:05,959 --> 00:01:08,080 Speaker 2: the word I was using as a superlative to praise you. 22 00:01:08,720 --> 00:01:12,000 Speaker 2: I'll take you at Longways, So Junior, thanks Ed, thank 23 00:01:12,000 --> 00:01:14,520 Speaker 2: you so much for joining us. Everybody, are you ready 24 00:01:14,520 --> 00:01:17,600 Speaker 2: to see some of the dumbest AI generated videos. 25 00:01:18,120 --> 00:01:19,640 Speaker 4: Were few me with more pleasure. 26 00:01:19,840 --> 00:01:20,840 Speaker 2: Excellent, excellent. 27 00:01:20,920 --> 00:01:22,120 Speaker 1: Nothing fills me with pleasure. 28 00:01:22,600 --> 00:01:24,920 Speaker 2: The first panel I sat down today with at ten 29 00:01:25,000 --> 00:01:28,880 Speaker 2: am in the goddamn morning Jesus, was the Hollywood Trajectory 30 00:01:29,000 --> 00:01:32,080 Speaker 2: Generative AI Timeline twenty twenty five to twenty thirty. 31 00:01:32,200 --> 00:01:34,880 Speaker 3: Oh boy, I am fascinated for what they think will 32 00:01:34,920 --> 00:01:36,000 Speaker 3: happen at twenty thirty. 33 00:01:37,120 --> 00:01:40,440 Speaker 2: Everything's just gonna get better, Garrison. This panel featured a 34 00:01:40,520 --> 00:01:44,319 Speaker 2: number of luminary thinkers, including Mary Hamilton, a managing director 35 00:01:44,360 --> 00:01:47,680 Speaker 2: at a Censure, who announced her company's three billion dollar 36 00:01:47,720 --> 00:01:50,680 Speaker 2: investment in AI by dropping this gin I have a 37 00:01:50,680 --> 00:01:51,600 Speaker 2: digital twin. 38 00:01:51,440 --> 00:01:54,920 Speaker 5: And she's constantly evolving and how she gets used and 39 00:01:54,960 --> 00:01:56,080 Speaker 5: what she says, and. 40 00:01:57,320 --> 00:02:00,720 Speaker 3: There's big educations around that. I think this is a 41 00:02:00,720 --> 00:02:02,440 Speaker 3: really exciting space to. 42 00:02:02,760 --> 00:02:06,160 Speaker 4: Do that, like that she just stole Hurley Herndon's thing. 43 00:02:06,200 --> 00:02:10,840 Speaker 5: But Okay, they probably said that to a doctor. 44 00:02:10,960 --> 00:02:12,280 Speaker 2: They think I had a concussion. 45 00:02:12,440 --> 00:02:16,480 Speaker 3: Sure what this person needs like psychological. 46 00:02:15,960 --> 00:02:19,360 Speaker 2: Yeah, you should be allowed to drive, you need you 47 00:02:19,360 --> 00:02:20,200 Speaker 2: need a Bryan kid. 48 00:02:20,240 --> 00:02:21,680 Speaker 6: Okay, let's get you. 49 00:02:21,840 --> 00:02:22,960 Speaker 1: Let's get you, sit down, and. 50 00:02:23,160 --> 00:02:26,400 Speaker 2: We're taking the phone away from you. Now. I think 51 00:02:26,440 --> 00:02:28,679 Speaker 2: this is very silly because again I think it. Yeah, 52 00:02:28,680 --> 00:02:31,080 Speaker 2: it's just a fundamental mismatch in what people might want 53 00:02:31,200 --> 00:02:33,840 Speaker 2: from an AI agent and like the way in which 54 00:02:33,840 --> 00:02:34,720 Speaker 2: they get talked about. 55 00:02:34,800 --> 00:02:37,000 Speaker 1: But also they use digital twin, which is ce men 56 00:02:37,080 --> 00:02:38,160 Speaker 1: to prise software ship. 57 00:02:38,280 --> 00:02:39,799 Speaker 6: Yeah, oh my god. 58 00:02:39,680 --> 00:02:42,000 Speaker 2: Yeah, it's it's it's I'm excited to go see some 59 00:02:42,080 --> 00:02:44,440 Speaker 2: digital twin technology that I'm sure we'll make a cheap 60 00:02:44,520 --> 00:02:46,800 Speaker 2: at switching. 61 00:02:47,800 --> 00:02:49,520 Speaker 3: This was this is the first thing I reported on 62 00:02:49,560 --> 00:02:52,440 Speaker 3: at CES was there was the digital twin. Like back 63 00:02:52,480 --> 00:02:55,360 Speaker 3: in like twenty twenty two, twenty one, there was like 64 00:02:55,480 --> 00:02:57,480 Speaker 3: one single company and all of c Yes, I was 65 00:02:57,520 --> 00:02:59,800 Speaker 3: promising like a digital twin, and now it's like every 66 00:02:59,800 --> 00:03:00,840 Speaker 3: other company. Ye. 67 00:03:00,960 --> 00:03:02,480 Speaker 6: It means so many different things. 68 00:03:02,560 --> 00:03:05,360 Speaker 5: It means literally a digital representation of anything. It doesn't 69 00:03:05,360 --> 00:03:07,240 Speaker 5: even mean an II agent. The fact that they're using 70 00:03:07,280 --> 00:03:08,960 Speaker 5: it in the wrong place is very annoying to me. 71 00:03:09,120 --> 00:03:09,320 Speaker 4: Yeah. 72 00:03:09,320 --> 00:03:11,480 Speaker 2: I keep saying like they can now make an AI 73 00:03:11,680 --> 00:03:14,520 Speaker 2: chatbot trained off of your social media presence. That's eighty 74 00:03:14,560 --> 00:03:16,200 Speaker 2: five percent accurate. 75 00:03:17,800 --> 00:03:21,320 Speaker 7: As all twins are and I want to know they can't. 76 00:03:21,360 --> 00:03:23,640 Speaker 2: But then you talk to the average person at CEES 77 00:03:23,800 --> 00:03:27,160 Speaker 2: or the average panelist on this particular panel, I'm like, yes, 78 00:03:27,200 --> 00:03:29,320 Speaker 2: I do believe. In fact, everyone on that panel you 79 00:03:29,360 --> 00:03:32,560 Speaker 2: could accurately, you could accurately get eighty five percent of 80 00:03:32,600 --> 00:03:37,960 Speaker 2: their personality with the chatbot for a bit, maybe a 81 00:03:37,960 --> 00:03:41,880 Speaker 2: lot higher improvement. Yeah, yeah, so I will say, like, 82 00:03:41,960 --> 00:03:44,800 Speaker 2: that was silly. That's not the silliest thing I saw. 83 00:03:45,440 --> 00:03:48,000 Speaker 2: The silliest thing I saw came courtesy of another panelist, 84 00:03:48,160 --> 00:03:52,760 Speaker 2: Jason Zada, founder of Secret Level and COO of the company. 85 00:03:53,120 --> 00:03:56,040 Speaker 2: The videos that Jason came to CEES to brag about 86 00:03:56,200 --> 00:03:59,520 Speaker 2: were a collection of the laziest AI slop ever to 87 00:03:59,600 --> 00:04:03,000 Speaker 2: stain him and eyeballs. His most recent big success that 88 00:04:03,080 --> 00:04:05,480 Speaker 2: you could just see radiating off of him how proud 89 00:04:05,520 --> 00:04:08,760 Speaker 2: he was of this was Coca Cola's annual Christmas ad, 90 00:04:09,160 --> 00:04:12,640 Speaker 2: which last year was produced for the first time entirely 91 00:04:12,680 --> 00:04:15,680 Speaker 2: with AI. And I'm just gonna if you haven't seen this, 92 00:04:15,720 --> 00:04:20,000 Speaker 2: who hears seen Coca Cola's AIS? 93 00:04:20,160 --> 00:04:22,720 Speaker 3: I guess yeah, I've seen pictures. I think I may 94 00:04:22,760 --> 00:04:23,480 Speaker 3: have watched the one. 95 00:04:24,279 --> 00:04:29,520 Speaker 8: Okay, well, let's let's let a few times the amounts. 96 00:04:28,680 --> 00:04:30,880 Speaker 2: We're gonna play. There's three different versions of this, so 97 00:04:31,000 --> 00:04:32,120 Speaker 2: why we're just gonna I. 98 00:04:32,120 --> 00:04:34,240 Speaker 5: Mean, that's that's what it's about out Oh my god. 99 00:04:34,240 --> 00:04:36,240 Speaker 5: If there's three different versions that that that's just they 100 00:04:36,279 --> 00:04:49,840 Speaker 5: saved the pro Everyone is the same length of show. 101 00:04:54,040 --> 00:04:57,840 Speaker 2: Can you believe this song's AI generated? I can't believe 102 00:04:58,560 --> 00:05:01,000 Speaker 2: the cow Could they teach a computer to write the lyrics? 103 00:05:01,000 --> 00:05:01,960 Speaker 2: Holidays are? 104 00:05:03,440 --> 00:05:05,520 Speaker 5: I just can't believe we finally have the technology to 105 00:05:05,560 --> 00:05:07,560 Speaker 5: have three trucks driving somewhere and. 106 00:05:08,240 --> 00:05:11,240 Speaker 2: Wagging its tail with dead eye. It's all these too 107 00:05:11,320 --> 00:05:18,880 Speaker 2: horrible girls, trucks with Coca cola and them driving down 108 00:05:19,000 --> 00:05:21,120 Speaker 2: not a street, raccoons. 109 00:05:23,200 --> 00:05:24,800 Speaker 5: Why is there a satellite O They're gonna drop the 110 00:05:24,800 --> 00:05:31,480 Speaker 5: ion can on them, the pola bes. 111 00:05:32,640 --> 00:05:36,520 Speaker 2: It's all clearly AI. It's all glowing like the city 112 00:05:36,560 --> 00:05:41,000 Speaker 2: shots of like snow colored villages with that, as we're 113 00:05:41,000 --> 00:05:43,599 Speaker 2: going to see in later videos. A. I loves putting 114 00:05:43,640 --> 00:05:46,760 Speaker 2: smoke and random fires where there should not be smoking. 115 00:05:46,880 --> 00:05:48,200 Speaker 2: Random fire, Chris. 116 00:05:48,040 --> 00:05:51,119 Speaker 3: Kringle, that's such a bad omen for four more years 117 00:05:51,120 --> 00:05:52,080 Speaker 3: of a trunk presidence. 118 00:05:52,240 --> 00:05:54,080 Speaker 2: It's a bleak we have like. 119 00:05:54,480 --> 00:05:58,280 Speaker 3: Even uglier Thomas Kincaid esque artwork. That's all. 120 00:05:58,400 --> 00:06:01,080 Speaker 2: Every frame looks like a tomly animated. 121 00:06:01,320 --> 00:06:03,919 Speaker 3: Yeah, it's like they just generated a Thomas Kincaid like 122 00:06:04,000 --> 00:06:05,839 Speaker 3: frame and then like badly animated. 123 00:06:05,960 --> 00:06:08,359 Speaker 5: And the way that they move is very weird, like 124 00:06:08,440 --> 00:06:10,480 Speaker 5: it looks kind of right, but kind of right, looks 125 00:06:10,560 --> 00:06:11,080 Speaker 5: very strange. 126 00:06:11,120 --> 00:06:12,800 Speaker 2: He does that all of the scenes because it's like 127 00:06:12,839 --> 00:06:14,840 Speaker 2: showing you a bunch of you see like a polar bear. 128 00:06:14,920 --> 00:06:17,200 Speaker 2: Obviously it's a Coca Cola Christmas ad. You see like 129 00:06:17,240 --> 00:06:20,280 Speaker 2: a fucking reindeer, you see squirrels, you see a dog. 130 00:06:20,520 --> 00:06:23,000 Speaker 2: But it always is like this very ai shot where 131 00:06:23,000 --> 00:06:26,400 Speaker 2: it just pans across the animal and it's like glowing 132 00:06:26,480 --> 00:06:30,000 Speaker 2: and kind of glossy and steering too much. 133 00:06:31,000 --> 00:06:33,120 Speaker 5: But they're not going anywhere with the movement. It's just 134 00:06:33,200 --> 00:06:34,960 Speaker 5: like they are doing something and that's it. 135 00:06:35,200 --> 00:06:35,479 Speaker 2: Yeah. 136 00:06:35,600 --> 00:06:37,640 Speaker 1: You think in ten years they're still gonna have these commercials? 137 00:06:37,760 --> 00:06:39,200 Speaker 6: No, No, because where's the snow. 138 00:06:39,440 --> 00:06:41,000 Speaker 1: It's just a polar bez walking around like. 139 00:06:41,960 --> 00:06:45,320 Speaker 2: System one, which tests emotional responses to ads, claims that 140 00:06:45,360 --> 00:06:48,640 Speaker 2: the initial response to their Christmas ad was overwhelmingly positive. 141 00:06:48,760 --> 00:06:51,440 Speaker 3: I don't think they're lying about that. I think if 142 00:06:51,440 --> 00:06:53,680 Speaker 3: you walk up to someone like randomly on the street 143 00:06:53,680 --> 00:06:55,920 Speaker 3: and showed them this, I think they'd be like, oh, yeah, 144 00:06:56,120 --> 00:06:56,680 Speaker 3: it looks fine. 145 00:06:58,279 --> 00:06:58,559 Speaker 2: Yeah. 146 00:06:58,800 --> 00:07:01,880 Speaker 5: No one's watching a Coca coat the right and being like, yeah, wow, 147 00:07:02,440 --> 00:07:03,800 Speaker 5: I've never had one of these before. 148 00:07:03,960 --> 00:07:06,360 Speaker 6: Yeah, it's not it's never a new experience. 149 00:07:06,480 --> 00:07:08,240 Speaker 4: Not yet. We need an ad man. 150 00:07:08,400 --> 00:07:11,040 Speaker 6: You need an adman for the coke holdouts. 151 00:07:11,080 --> 00:07:12,520 Speaker 4: We need an ai Don Draper. 152 00:07:12,760 --> 00:07:14,679 Speaker 3: Yeah, well, do not give them ideas. 153 00:07:16,360 --> 00:07:17,000 Speaker 2: What if a. 154 00:07:16,840 --> 00:07:19,080 Speaker 4: Company lost five billion dollars, It's just an ad that 155 00:07:19,080 --> 00:07:20,920 Speaker 4: doesn't work. Instead of going to the movies like Don 156 00:07:20,960 --> 00:07:22,200 Speaker 4: Draper does throughout all. 157 00:07:22,040 --> 00:07:24,200 Speaker 7: Of Mad Man, it just doesn't work and respond to 158 00:07:24,200 --> 00:07:25,040 Speaker 7: any of your queries. 159 00:07:25,200 --> 00:07:29,360 Speaker 2: Just Don Draper spending hours watching that looping Christmas video. 160 00:07:30,000 --> 00:07:31,080 Speaker 1: Staring into nothing. 161 00:07:31,600 --> 00:07:34,920 Speaker 2: Yeah. So there was like an immediate, pretty immediate backlash 162 00:07:35,000 --> 00:07:37,080 Speaker 2: to this, like all of the responses, if you like, 163 00:07:37,160 --> 00:07:39,679 Speaker 2: go to any of like where these things live on YouTube. 164 00:07:39,680 --> 00:07:43,440 Speaker 2: It's just people shitting on them, which he did acknowledge 165 00:07:43,520 --> 00:07:46,200 Speaker 2: Jason by saying the video was very debated. 166 00:07:46,680 --> 00:07:48,680 Speaker 6: Yes, classic thing with commercials. 167 00:07:49,200 --> 00:07:52,280 Speaker 3: Debating commercials, many things they're very debated these days. 168 00:07:52,400 --> 00:07:54,280 Speaker 2: A lot of people are saying and then He showed 169 00:07:54,360 --> 00:07:57,440 Speaker 2: us next an AI generated video The Heist, which was 170 00:07:57,720 --> 00:08:01,040 Speaker 2: entirely made by a text script that itself was mostly 171 00:08:01,080 --> 00:08:04,200 Speaker 2: written by chatch Ept And here's how Jason describes the 172 00:08:04,240 --> 00:08:07,400 Speaker 2: workflow for what you're about to see. It took thousands 173 00:08:07,400 --> 00:08:10,240 Speaker 2: of generations to get the final film that. I'm absolutely 174 00:08:10,280 --> 00:08:14,680 Speaker 2: blown away by the quality, the consistency, and adherence to 175 00:08:14,760 --> 00:08:17,960 Speaker 2: the original prompt when I described gritty New York City 176 00:08:18,000 --> 00:08:22,280 Speaker 2: in the eighties, it delivered in spades, consistently. Well, this 177 00:08:22,440 --> 00:08:26,280 Speaker 2: is not perfect, it is hands down the best video 178 00:08:26,400 --> 00:08:30,360 Speaker 2: generation model out there by a long shot. Additionally, it's 179 00:08:30,400 --> 00:08:34,400 Speaker 2: important no VFX, no cleanup, no color correction has been added. 180 00:08:34,720 --> 00:08:38,000 Speaker 2: Everything is straight out of VO two Google Deep Mind. 181 00:08:38,200 --> 00:08:40,920 Speaker 2: So what is the model VO two Google deep Mind? 182 00:08:40,920 --> 00:08:41,719 Speaker 2: I think is what he's saying. 183 00:08:41,760 --> 00:08:43,600 Speaker 1: It is one. 184 00:08:43,679 --> 00:08:45,680 Speaker 5: By the way, I'm sure what you're about to show 185 00:08:45,720 --> 00:08:46,920 Speaker 5: me looks like a dog's awsome. 186 00:08:47,000 --> 00:08:49,200 Speaker 2: It looks like, yeah, New York, exactly like New York 187 00:08:49,200 --> 00:08:54,079 Speaker 2: at Juliani right before he came in clean it. Uh huh. 188 00:08:54,240 --> 00:08:57,080 Speaker 3: So this is like the competitor to SORA. I guess 189 00:08:57,080 --> 00:09:00,959 Speaker 3: that is the other big like video generation knew I don't. 190 00:09:00,679 --> 00:09:03,760 Speaker 2: Buy for a and I'm not impressed, but we'll see 191 00:09:03,800 --> 00:09:05,840 Speaker 2: what you guys think. I don't want to poison your roots. 192 00:09:06,400 --> 00:09:10,920 Speaker 6: I wouldn't. Oh god, okay, there. 193 00:09:10,800 --> 00:09:11,520 Speaker 3: Is fire in this. 194 00:09:11,640 --> 00:09:13,520 Speaker 2: The last time you're going to see the sack full 195 00:09:13,520 --> 00:09:15,480 Speaker 2: of money. It does not show out again. 196 00:09:15,559 --> 00:09:17,200 Speaker 3: It's a lot of a lot of fires, a lot 197 00:09:17,280 --> 00:09:18,200 Speaker 3: of random fire and. 198 00:09:19,120 --> 00:09:22,199 Speaker 6: Go backwards when they're driving forwards, wheel. 199 00:09:23,360 --> 00:09:24,760 Speaker 2: Again, another straight fire. 200 00:09:25,840 --> 00:09:27,520 Speaker 6: I would love to do freeze frames on this. 201 00:09:28,000 --> 00:09:32,640 Speaker 2: Actually it's in gossip. Why is there so many fires? 202 00:09:32,720 --> 00:09:35,240 Speaker 2: Just all right, let's take a shot every time, Oh 203 00:09:35,320 --> 00:09:38,040 Speaker 2: my god, and also take a shut every time. He 204 00:09:38,160 --> 00:09:40,959 Speaker 2: is wearing different clothing and has a clearly different face. 205 00:09:41,000 --> 00:09:41,760 Speaker 3: The car has changed. 206 00:09:41,760 --> 00:09:44,440 Speaker 2: Column he's praising the consistency and it is a he 207 00:09:44,559 --> 00:09:46,960 Speaker 2: is dressed completely differently every scene. 208 00:09:48,480 --> 00:09:51,640 Speaker 3: His jacket has has has changed you since the last one? Yeah? 209 00:09:51,720 --> 00:09:54,760 Speaker 2: Yeah, and again the cop car the cars. When it 210 00:09:54,840 --> 00:09:57,320 Speaker 2: shows the cars dragging across the screen, they're kind of 211 00:09:57,360 --> 00:09:59,840 Speaker 2: doing the same thing usually that the animals do in 212 00:09:59,840 --> 00:10:00,480 Speaker 2: the coke. 213 00:10:00,360 --> 00:10:03,560 Speaker 4: And minimal motion at the best. 214 00:10:04,440 --> 00:10:08,600 Speaker 2: Yeah, I also love this. Can you believe this music? 215 00:10:10,160 --> 00:10:12,240 Speaker 5: You also want to just say when it swivet hit 216 00:10:12,320 --> 00:10:14,599 Speaker 5: that thing he was driving like half a mile on it. 217 00:10:14,920 --> 00:10:17,360 Speaker 6: Yeah, that's how I run. 218 00:10:17,559 --> 00:10:20,160 Speaker 2: Yeah, look an obviously different man. 219 00:10:20,240 --> 00:10:25,800 Speaker 6: That's the way he runs. Was like he had his 220 00:10:25,920 --> 00:10:26,360 Speaker 6: arms out. 221 00:10:26,440 --> 00:10:29,560 Speaker 2: Two cops three Actually, look they run. 222 00:10:31,200 --> 00:10:32,240 Speaker 3: Running is very funny. 223 00:10:32,280 --> 00:10:34,440 Speaker 2: Yeah, this is different. 224 00:10:34,559 --> 00:10:37,119 Speaker 6: Okay, what is going on with his feet. 225 00:10:36,880 --> 00:10:41,120 Speaker 3: And different levels of facial hair, different different jackets, he's 226 00:10:41,200 --> 00:10:46,640 Speaker 3: wearing different colors jackets, vague definiteness in this just move? 227 00:10:46,720 --> 00:10:47,640 Speaker 6: What the fuck is going on? 228 00:10:48,200 --> 00:10:52,400 Speaker 2: Oh my gosh, I got me directed by Jason's got 229 00:10:52,440 --> 00:10:56,000 Speaker 2: in big flaming words because again, the AI only knows 230 00:10:56,000 --> 00:10:57,559 Speaker 2: how to put random fires on. 231 00:10:57,679 --> 00:10:59,839 Speaker 5: Wow, I'm so glad that we have the technology that 232 00:11:00,080 --> 00:11:01,720 Speaker 5: a thing where a guy gets chased by the police. 233 00:11:01,920 --> 00:11:04,400 Speaker 2: Yeah, we couldn't. This would have been impossible before. 234 00:11:04,200 --> 00:11:06,560 Speaker 5: Because he runs at anywhere from one to one hundred 235 00:11:06,600 --> 00:11:07,160 Speaker 5: miles an hour. 236 00:11:07,360 --> 00:11:10,160 Speaker 3: I assume they just trained they like this was specifically 237 00:11:10,160 --> 00:11:13,120 Speaker 3: like pulling on like Scorsese movies a lot. 238 00:11:13,520 --> 00:11:15,800 Speaker 5: I just want to know about these thousands of generations 239 00:11:15,800 --> 00:11:16,839 Speaker 5: of script because. 240 00:11:16,600 --> 00:11:17,280 Speaker 3: That is interesting. 241 00:11:17,360 --> 00:11:19,680 Speaker 5: I am very curious because I just don't believe that 242 00:11:19,720 --> 00:11:23,040 Speaker 5: for did he just uh read there? 243 00:11:23,120 --> 00:11:25,000 Speaker 7: Yeah no, that's the opening crawl to just like some 244 00:11:25,320 --> 00:11:29,560 Speaker 7: uh generated star Wars. 245 00:11:30,240 --> 00:11:32,480 Speaker 3: It seems like shot by shot, right, each each shot 246 00:11:32,600 --> 00:11:34,640 Speaker 3: is going to require a lot of like iterations. The 247 00:11:34,800 --> 00:11:38,760 Speaker 3: script it's just yeah, I mean again like it unpacking. 248 00:11:38,800 --> 00:11:41,320 Speaker 3: What he actually is saying is unclear because I. 249 00:11:41,559 --> 00:11:44,280 Speaker 2: Went to the YouTube video for this and the first 250 00:11:44,440 --> 00:11:47,120 Speaker 2: five or four comments are looks like we found the 251 00:11:47,160 --> 00:11:49,600 Speaker 2: new king of video. Jesus Christ, give it a rest 252 00:11:50,240 --> 00:11:53,680 Speaker 2: close change in every shot. Four to six year old boys, 253 00:11:53,679 --> 00:11:57,480 Speaker 2: you're gonna love it and still acts character and vehicle consistency. 254 00:11:57,679 --> 00:12:01,640 Speaker 2: But we're getting close, which. 255 00:12:01,480 --> 00:12:02,160 Speaker 3: Is which is the. 256 00:12:04,320 --> 00:12:07,120 Speaker 2: By thirty you're going to make a man wear the 257 00:12:07,200 --> 00:12:09,240 Speaker 2: same clothes for an entire video. 258 00:12:09,360 --> 00:12:11,880 Speaker 5: Oh this is This has happened before with SRA when 259 00:12:11,920 --> 00:12:15,080 Speaker 5: they put Sora out there, like check out airhead on 260 00:12:15,120 --> 00:12:18,920 Speaker 5: your man God, and the balloon changes every single shot. 261 00:12:19,000 --> 00:12:21,440 Speaker 6: It's a different size and color each time. 262 00:12:21,640 --> 00:12:23,600 Speaker 1: There are just people running in the background. 263 00:12:23,679 --> 00:12:25,000 Speaker 6: Sometimes and then they. 264 00:12:24,840 --> 00:12:26,600 Speaker 5: Made a new one. You're like, oh, this is gonna 265 00:12:26,640 --> 00:12:29,920 Speaker 5: be good. It was worse and less consistent, and this 266 00:12:30,040 --> 00:12:32,000 Speaker 5: is what they think of us. They're like, these pigs 267 00:12:32,000 --> 00:12:33,440 Speaker 5: will slop up anything. 268 00:12:33,200 --> 00:12:37,160 Speaker 2: And you can't expect technology to do something as complicated 269 00:12:37,240 --> 00:12:39,480 Speaker 2: as dress a man in clothing and have him stay 270 00:12:39,480 --> 00:12:42,480 Speaker 2: in that same clothing over multiple scenes. Hollywood never figured 271 00:12:42,480 --> 00:12:43,319 Speaker 2: it out so cool. 272 00:12:43,120 --> 00:12:46,560 Speaker 1: That this costs like so much money as well just burning. 273 00:12:46,720 --> 00:12:47,840 Speaker 6: There was some fucking. 274 00:12:47,559 --> 00:12:50,760 Speaker 1: GPU melting and in a data enter in Arizona. 275 00:12:50,800 --> 00:12:55,240 Speaker 7: The strain Carol it is also there's gonna be like 276 00:12:55,400 --> 00:12:58,960 Speaker 7: thirty forty companies trying to recreate the same misshapen wheel, 277 00:12:59,480 --> 00:13:00,720 Speaker 7: you know, for the Max five to. 278 00:13:00,920 --> 00:13:04,079 Speaker 5: Also the little pigs that watch Star Wars, including myself, 279 00:13:04,280 --> 00:13:07,240 Speaker 5: we'll notice every minor inconsistency. Do you think that they're 280 00:13:07,240 --> 00:13:10,520 Speaker 5: gonna tolerate Luke Skywalker's and Watteau and all. 281 00:13:10,400 --> 00:13:13,080 Speaker 7: Their favorite characters they're gonna Do you think that they're 282 00:13:13,080 --> 00:13:14,920 Speaker 7: gonna be happy office with a cyber truck? 283 00:13:15,280 --> 00:13:17,040 Speaker 6: That's gonna be a cyber truck situation? 284 00:13:17,640 --> 00:13:19,920 Speaker 2: You I think the issues are twofold, which is like 285 00:13:20,040 --> 00:13:22,440 Speaker 2: number one. In order to make this shiit sell to 286 00:13:22,480 --> 00:13:25,480 Speaker 2: the people who watch movies, you have to dramatically reduce 287 00:13:25,559 --> 00:13:28,599 Speaker 2: the average intelligence of people watching movies. You have to 288 00:13:28,640 --> 00:13:31,760 Speaker 2: give everyone brain damage, which except they are working in 289 00:13:31,920 --> 00:13:34,640 Speaker 2: Giant And the other thing is the models have to 290 00:13:34,679 --> 00:13:36,800 Speaker 2: get much better. And Jason made a point that like, 291 00:13:36,840 --> 00:13:39,160 Speaker 2: look every time people would like talk about the criticism 292 00:13:39,160 --> 00:13:41,920 Speaker 2: and be like, look, this is the worst it's gonna look, guys, 293 00:13:42,559 --> 00:13:45,720 Speaker 2: And I was just looking into it. GPT four took 294 00:13:45,840 --> 00:13:49,400 Speaker 2: fifty times as many resources in like fifty times as 295 00:13:49,480 --> 00:13:53,360 Speaker 2: much energy to train as GPT three did. So this 296 00:13:53,440 --> 00:13:56,320 Speaker 2: is the These are the kind of like exponential increases 297 00:13:56,360 --> 00:13:58,480 Speaker 2: that we're looking at. So like, if it took them 298 00:13:58,800 --> 00:14:03,080 Speaker 2: so many billions of dollars of investment to get to 299 00:14:03,080 --> 00:14:05,200 Speaker 2: the point where they can make this shitty video, to 300 00:14:05,280 --> 00:14:08,760 Speaker 2: make anything close to watchable, you're talking about again just 301 00:14:08,760 --> 00:14:12,720 Speaker 2: like lighting on fire, billions of dollars to do what 302 00:14:12,920 --> 00:14:15,360 Speaker 2: to make a scene that you could already get like 303 00:14:15,400 --> 00:14:18,200 Speaker 2: a twenty six year old dude who grew up watching 304 00:14:18,240 --> 00:14:22,160 Speaker 2: fucking Quentin Tarantino movies and taking cocaine, and you can 305 00:14:22,200 --> 00:14:24,560 Speaker 2: give him sixty thousand dollars and he'll film that shit 306 00:14:24,640 --> 00:14:26,120 Speaker 2: for you with an old car, Like. 307 00:14:26,200 --> 00:14:28,720 Speaker 3: Yeah, I mean you could, you could even like animate it. 308 00:14:29,240 --> 00:14:31,240 Speaker 7: M I mean, look, you give me a PS four 309 00:14:31,440 --> 00:14:33,840 Speaker 7: and somebody's grandmother and I will make them think that 310 00:14:33,880 --> 00:14:34,640 Speaker 7: they're watching that. 311 00:14:34,840 --> 00:14:36,960 Speaker 4: No, seriously, seriously, dott six. 312 00:14:37,240 --> 00:14:39,160 Speaker 5: But also this I just want to read out some 313 00:14:39,240 --> 00:14:41,840 Speaker 5: of the fucking people that use this model. We started 314 00:14:41,840 --> 00:14:44,640 Speaker 5: working with creatives like Donald Glover, who I said was 315 00:14:44,640 --> 00:14:46,880 Speaker 5: washed ten years ago, fucking sick of people. 316 00:14:46,960 --> 00:14:49,359 Speaker 4: And My Love was a was a good album. 317 00:14:49,960 --> 00:14:53,120 Speaker 6: America is an objectively bad song. It's a bad song 318 00:14:53,160 --> 00:14:55,960 Speaker 6: with a great video. Yeah, I thought he's like kind 319 00:14:56,000 --> 00:14:56,400 Speaker 6: of barn and Bee. 320 00:14:56,400 --> 00:14:59,480 Speaker 5: Stuff's very interesting anyway, moving by, and of course the 321 00:14:59,480 --> 00:15:00,560 Speaker 5: week in it. 322 00:15:00,280 --> 00:15:01,840 Speaker 6: Sorry weekend and some. 323 00:15:03,400 --> 00:15:07,440 Speaker 5: Great I'll work with creators on VO one in form 324 00:15:07,480 --> 00:15:09,200 Speaker 5: the development of VO two, and we look forward to 325 00:15:09,240 --> 00:15:11,600 Speaker 5: working with trusted testers and creators to get feedback on 326 00:15:11,640 --> 00:15:12,240 Speaker 5: this new model. 327 00:15:12,320 --> 00:15:13,880 Speaker 1: How long are you gonna get fucking feedback? 328 00:15:13,880 --> 00:15:14,480 Speaker 6: It stinks. 329 00:15:14,760 --> 00:15:17,840 Speaker 2: We've got some feedback from Yeah, I got thoughts. 330 00:15:17,880 --> 00:15:20,760 Speaker 3: Hopefully those people are are just getting paid to tell 331 00:15:20,800 --> 00:15:23,080 Speaker 3: them words and be like yeah, sure, I'll take your money, 332 00:15:23,120 --> 00:15:24,600 Speaker 3: but if they could be. 333 00:15:24,560 --> 00:15:27,840 Speaker 2: Twenty million dollars, I'm flipping the hope like just yeah, no, 334 00:15:27,880 --> 00:15:30,360 Speaker 2: I will turn on a dime. Speaking of turning on 335 00:15:30,400 --> 00:15:45,600 Speaker 2: a dime for money, here's the ads. Ah, we're back. 336 00:15:46,640 --> 00:15:50,160 Speaker 2: So the next video that our friend I now feel 337 00:15:50,160 --> 00:15:52,440 Speaker 2: he is like a brother to me Jason puts On 338 00:15:52,760 --> 00:15:56,240 Speaker 2: was of an AI generated fictional elderly rock star talking 339 00:15:56,240 --> 00:15:56,920 Speaker 2: about death. 340 00:15:57,080 --> 00:15:59,200 Speaker 3: Oh I'm excited. 341 00:16:00,040 --> 00:16:03,040 Speaker 2: You have to do this plastic and incapable of dynamic 342 00:16:03,080 --> 00:16:05,960 Speaker 2: expression as he guzzles randomly from bottles of liquor that 343 00:16:06,000 --> 00:16:08,760 Speaker 2: flash in and out of existence. Sometimes he lies on 344 00:16:08,840 --> 00:16:11,360 Speaker 2: his back in empty streets while talking about all of 345 00:16:11,400 --> 00:16:14,280 Speaker 2: the all of the cgi featureless women that he has 346 00:16:14,320 --> 00:16:18,200 Speaker 2: loved in his exciting life. Other Times he plays stadium 347 00:16:18,240 --> 00:16:21,400 Speaker 2: shows while obvious GPT written dialogue about aging and death 348 00:16:21,480 --> 00:16:25,240 Speaker 2: drones on. When the video ends, everybody in the room claps, 349 00:16:25,280 --> 00:16:27,960 Speaker 2: And as you watch this, I need to imagine seeing 350 00:16:28,000 --> 00:16:30,200 Speaker 2: the thing that I'm about to show you all and 351 00:16:30,240 --> 00:16:32,640 Speaker 2: a room with like two hundred people in it, all 352 00:16:32,680 --> 00:16:38,120 Speaker 2: clapping enthusiastically. I don't think I did. I did it. 353 00:16:38,280 --> 00:16:41,320 Speaker 2: I did. I said, come the fuck on as loud 354 00:16:41,320 --> 00:16:42,200 Speaker 2: as I could. 355 00:16:43,840 --> 00:16:44,480 Speaker 6: Skywalk up. 356 00:16:44,640 --> 00:16:49,680 Speaker 2: Yeah. So here's fade out and an old man. Ye, 357 00:16:49,720 --> 00:16:51,920 Speaker 2: it looks a little bit like George car It's the. 358 00:16:51,960 --> 00:16:57,840 Speaker 9: End O three, Like the world's just god damn big, 359 00:16:57,880 --> 00:17:03,320 Speaker 9: and you're just to go passing through them. 360 00:17:00,480 --> 00:17:04,000 Speaker 3: M what's he doing? 361 00:17:04,280 --> 00:17:06,280 Speaker 2: Carried my heart concerts? 362 00:17:06,280 --> 00:17:08,320 Speaker 6: Granddad, calm down, scattered. 363 00:17:08,600 --> 00:17:11,280 Speaker 5: I love these slash cuts, said the fast cuts, these 364 00:17:11,320 --> 00:17:13,399 Speaker 5: false cuts because the next frame was unusable. 365 00:17:14,480 --> 00:17:17,080 Speaker 2: Yes actually yes, like that he drank and the bottle 366 00:17:17,240 --> 00:17:19,359 Speaker 2: changed in his hand. You could see it starting to happen. 367 00:17:20,520 --> 00:17:22,120 Speaker 2: What is just anonymous? 368 00:17:22,160 --> 00:17:25,439 Speaker 4: Women destroyed it just in a beautiful music. 369 00:17:25,640 --> 00:17:28,680 Speaker 2: Listen to that lived it to the could you believe 370 00:17:29,640 --> 00:17:32,320 Speaker 2: generated by firing a candle? 371 00:17:34,760 --> 00:17:35,000 Speaker 3: I like? 372 00:17:35,280 --> 00:17:38,320 Speaker 8: Also, the old man does look very different each time, 373 00:17:38,720 --> 00:17:39,600 Speaker 8: very different old man. 374 00:17:40,119 --> 00:17:41,840 Speaker 3: That's a different that's different guy. 375 00:17:42,400 --> 00:17:45,280 Speaker 2: Yeah, that's the Emperor from the first Gladiator. 376 00:17:45,920 --> 00:17:50,919 Speaker 9: Shows and trotting running away from this the way this 377 00:17:51,080 --> 00:17:52,480 Speaker 9: model generates running. 378 00:17:52,320 --> 00:17:58,720 Speaker 2: Die There he is drinking on the fire, old rock star, 379 00:17:58,840 --> 00:18:03,119 Speaker 2: drinking in front of a flame ming house the a 380 00:18:03,280 --> 00:18:05,280 Speaker 2: I loves burning build What. 381 00:18:05,240 --> 00:18:07,720 Speaker 3: Is this voice? I would love to track his tattoos 382 00:18:07,720 --> 00:18:08,520 Speaker 3: from Praying for three. 383 00:18:09,000 --> 00:18:11,600 Speaker 5: We'll say he's about to eat the micro different, I've 384 00:18:11,600 --> 00:18:14,440 Speaker 5: done it, yum. 385 00:18:14,520 --> 00:18:20,560 Speaker 2: Now he's sleeping in a in a broken Mustang, the 386 00:18:20,600 --> 00:18:25,080 Speaker 2: classic Ferrari Mustang mustangs in like a pool in front 387 00:18:25,080 --> 00:18:27,000 Speaker 2: of a mansion. But he clearly isn't questioned to it. 388 00:18:27,000 --> 00:18:31,720 Speaker 2: The car is hovering slightly over the pool like I love. 389 00:18:31,600 --> 00:18:32,399 Speaker 6: This, I love this. 390 00:18:32,480 --> 00:18:35,000 Speaker 2: I love him, And he tells us, He tells us 391 00:18:35,080 --> 00:18:37,080 Speaker 2: during this as if we're supposed to be impressed that 392 00:18:37,320 --> 00:18:40,240 Speaker 2: chatchypt wrote seventy five percent of that's. 393 00:18:40,520 --> 00:18:47,119 Speaker 3: Hell, I can't believe that. 394 00:18:47,520 --> 00:18:50,119 Speaker 10: As a bartender, I regret walking into the room to 395 00:18:50,160 --> 00:18:51,240 Speaker 10: see if people want drinks. 396 00:18:51,280 --> 00:18:55,320 Speaker 6: This is atender. I apologize. I apologize that you had 397 00:18:55,359 --> 00:18:56,200 Speaker 6: to hear a drink. 398 00:18:56,359 --> 00:18:58,480 Speaker 3: I also would like, actually, can I have a drink too? 399 00:18:58,640 --> 00:18:59,680 Speaker 6: We are in the that's. 400 00:18:59,560 --> 00:19:02,240 Speaker 5: Off line, see yes, sweet, and we're all drinking because 401 00:19:02,240 --> 00:19:04,920 Speaker 5: I just want to say I'm fucking disassociating off that. 402 00:19:05,080 --> 00:19:08,600 Speaker 5: I'm so fucking saying every a year of doing this nonsense. 403 00:19:08,840 --> 00:19:11,080 Speaker 5: And I look at these chit eaters and they show 404 00:19:11,160 --> 00:19:13,639 Speaker 5: us that and they like slop down the slop Oh my. 405 00:19:13,720 --> 00:19:17,080 Speaker 2: God, it's it's it's hitting the easiest. 406 00:19:16,640 --> 00:19:19,000 Speaker 1: Things to find an old man that drinks. 407 00:19:18,640 --> 00:19:20,600 Speaker 2: For an idea of like how real this company is. 408 00:19:20,640 --> 00:19:22,440 Speaker 2: Obviously they were one of the companies. They were not 409 00:19:22,520 --> 00:19:24,280 Speaker 2: the only people who made that Coca Cola ad. They 410 00:19:24,280 --> 00:19:26,120 Speaker 2: were one of like three or four companies. 411 00:19:26,160 --> 00:19:30,919 Speaker 3: It takes four companies take companies to make that. 412 00:19:31,400 --> 00:19:33,679 Speaker 2: They have six hundred and twenty two followers on Twitter, 413 00:19:33,720 --> 00:19:36,959 Speaker 2: Hell yes or not? Twitter, YouTube and YouTube on YouTube 414 00:19:36,960 --> 00:19:41,000 Speaker 2: on Youtube't I post this karaoke song and this this 415 00:19:41,200 --> 00:19:43,520 Speaker 2: fade out is their or Sorry The Heist is their 416 00:19:43,520 --> 00:19:46,800 Speaker 2: most successful video with fifty six thousand views. Fade Out, 417 00:19:46,800 --> 00:19:49,600 Speaker 2: which we just watched, has less than five thousand views. 418 00:19:49,800 --> 00:19:52,720 Speaker 2: They're not ready, so they're not They're not quite. 419 00:19:52,800 --> 00:19:54,040 Speaker 3: It's only going to get better. 420 00:19:54,200 --> 00:19:55,400 Speaker 2: Yeah, it's only going to get better. 421 00:19:55,760 --> 00:19:56,760 Speaker 4: That's gonna get better. 422 00:19:56,880 --> 00:19:59,560 Speaker 3: Previously, things will only get round floor. 423 00:19:59,680 --> 00:20:01,840 Speaker 4: Yeah, a small price of one billion dollars. 424 00:20:01,920 --> 00:20:04,119 Speaker 1: This is like one hundred thousand dollars to compute. 425 00:20:04,560 --> 00:20:06,280 Speaker 4: Yeah, imagine how good it would be. 426 00:20:06,680 --> 00:20:09,399 Speaker 3: Much of a billion will only get worth more. 427 00:20:10,600 --> 00:20:12,920 Speaker 2: I mean, I get now, Garrison, I do think you 428 00:20:12,920 --> 00:20:14,280 Speaker 2: should invest all of your salary. 429 00:20:14,320 --> 00:20:16,679 Speaker 6: I just did a sixteenth minute about talking about this. 430 00:20:17,320 --> 00:20:19,879 Speaker 5: I think I would rather hook To has a more 431 00:20:20,240 --> 00:20:23,240 Speaker 5: obvious use case than this shit. Hey, do you want 432 00:20:23,240 --> 00:20:25,360 Speaker 5: to spend way more money to get something way worse? 433 00:20:25,440 --> 00:20:28,560 Speaker 5: I actually can't get over the seventy five percent check GPTWO, 434 00:20:29,119 --> 00:20:33,560 Speaker 5: Like that should be twenty No, it should be theoretically 435 00:20:33,640 --> 00:20:34,800 Speaker 5: it should be it should be one hundred. 436 00:20:34,840 --> 00:20:36,879 Speaker 3: Should be one hundred percent, yeah, not seventy. 437 00:20:36,680 --> 00:20:39,000 Speaker 5: Which means that a quarter a quarter of it was 438 00:20:39,080 --> 00:20:40,400 Speaker 5: just fucking unusable. 439 00:20:40,480 --> 00:20:43,800 Speaker 3: No. Absolutely, they're generating like individual shots that they're like 440 00:20:43,840 --> 00:20:46,719 Speaker 3: stitching together and like who knows how how long it 441 00:20:46,760 --> 00:20:48,760 Speaker 3: takes to like get like the prompt right for that 442 00:20:48,840 --> 00:20:49,600 Speaker 3: shot to work. 443 00:20:50,000 --> 00:20:52,480 Speaker 2: However long it takes. It was too long because it 444 00:20:52,480 --> 00:20:54,600 Speaker 2: looks like, shit, we're gonna watch a video I haven't 445 00:20:54,640 --> 00:20:56,640 Speaker 2: seen yet, or at least of course, because it's five minutes, 446 00:20:56,640 --> 00:20:57,479 Speaker 2: so we're not watching all this. 447 00:20:57,520 --> 00:20:58,120 Speaker 3: Oh my god. 448 00:20:58,160 --> 00:21:02,240 Speaker 2: It's fifty views and came out a week ago. It's 449 00:21:02,280 --> 00:21:03,520 Speaker 2: called minimonade. 450 00:21:04,359 --> 00:21:08,119 Speaker 3: What it's a word? 451 00:21:08,280 --> 00:21:10,560 Speaker 5: Now it's like when you find your cat's vomited on 452 00:21:10,600 --> 00:21:11,359 Speaker 5: the floor again. 453 00:21:11,920 --> 00:21:14,760 Speaker 2: So first we see a diner called Manimonade that appears 454 00:21:14,760 --> 00:21:17,359 Speaker 2: to be both on the fire light runner, Yeah, light runner. 455 00:21:17,440 --> 00:21:17,880 Speaker 5: Oh god. 456 00:21:18,040 --> 00:21:20,399 Speaker 2: When an old lady Rice is up out of a 457 00:21:20,440 --> 00:21:21,880 Speaker 2: pile of ashes. 458 00:21:22,280 --> 00:21:23,200 Speaker 6: That's how mouths work. 459 00:21:25,400 --> 00:21:26,320 Speaker 3: Where am I. 460 00:21:27,840 --> 00:21:28,840 Speaker 2: Great? AI voice? 461 00:21:28,960 --> 00:21:32,119 Speaker 6: What is this phantasmicgoria? All voice acting? 462 00:21:32,880 --> 00:21:34,240 Speaker 2: It's me, Harrison Ford? 463 00:21:38,600 --> 00:21:39,600 Speaker 1: What the fuck is going on? 464 00:21:40,600 --> 00:21:43,560 Speaker 2: What I think? This is death? This old lady's dead. 465 00:21:44,200 --> 00:21:51,720 Speaker 2: That's how I now. She's tripping on tomatoes. The decaying 466 00:21:52,040 --> 00:21:56,000 Speaker 2: sandy diner that exploded has turned into a lively fifties diner. 467 00:21:56,119 --> 00:21:59,399 Speaker 5: Off Dennis Villain News. 468 00:21:59,440 --> 00:22:00,639 Speaker 4: This is second get a Diner. 469 00:22:02,400 --> 00:22:09,159 Speaker 2: I yeah, ye know. There's a little Indian book he is. 470 00:22:09,240 --> 00:22:13,480 Speaker 2: He is the help though. Yeah, m hm. 471 00:22:15,760 --> 00:22:16,560 Speaker 3: Oh, that's not. 472 00:22:16,720 --> 00:22:18,359 Speaker 2: The little kid just fell down, and the way it 473 00:22:18,400 --> 00:22:22,399 Speaker 2: shows falling is that he just sort of deflays and 474 00:22:22,560 --> 00:22:27,320 Speaker 2: he's up again and he's staring at well, that's terrible. 475 00:22:27,600 --> 00:22:30,000 Speaker 2: We don't need to watch it anymore of that. No one, 476 00:22:30,119 --> 00:22:31,800 Speaker 2: no one, no one want to watch watch. 477 00:22:31,600 --> 00:22:32,760 Speaker 6: This and have a positive reaction. 478 00:22:32,840 --> 00:22:34,800 Speaker 1: They should, they should keep you in a holding cell. 479 00:22:34,880 --> 00:22:38,160 Speaker 2: Ye, I'm deeply unhappy at the time we already spent watching. 480 00:22:38,240 --> 00:22:40,040 Speaker 6: Yeah, like, we don't know what you're gonna do next. 481 00:22:40,400 --> 00:22:41,879 Speaker 4: We're building a facility for you. 482 00:22:43,359 --> 00:22:46,359 Speaker 2: The Phreeze reality distortion field gets used a lot when 483 00:22:46,400 --> 00:22:49,080 Speaker 2: we talk about text, but I really tasted it in 484 00:22:49,160 --> 00:22:51,879 Speaker 2: that room because all anyone on stage could talk about 485 00:22:52,040 --> 00:22:54,520 Speaker 2: is how good it looks. In every one of these videos, 486 00:22:54,800 --> 00:22:59,159 Speaker 2: people are like clapping, They're like, wow, this is amazing. 487 00:22:59,359 --> 00:23:01,920 Speaker 4: What do you think They think? It looks good? 488 00:23:02,480 --> 00:23:04,120 Speaker 6: It looks better than an Xbox. 489 00:23:04,680 --> 00:23:07,119 Speaker 5: Yeah, And the idea is you talk to fing In 490 00:23:07,160 --> 00:23:08,960 Speaker 5: and now a thing came out and that's magical. 491 00:23:09,040 --> 00:23:11,640 Speaker 7: So by virtue of not having humans work on it, 492 00:23:11,640 --> 00:23:14,760 Speaker 7: it's so it's better than you'd have Yeah, Okay. 493 00:23:14,800 --> 00:23:17,600 Speaker 2: There was a moment after this where Jason like joked 494 00:23:17,640 --> 00:23:20,440 Speaker 2: about how like I don't like obviously I don't want 495 00:23:20,480 --> 00:23:26,399 Speaker 2: to replace actors yet yeah, yeah, and another Panels was like, 496 00:23:26,440 --> 00:23:28,159 Speaker 2: I think we're gonna have to make some decision. You 497 00:23:28,280 --> 00:23:30,960 Speaker 2: have to see how some decisions go as to fair use, 498 00:23:31,000 --> 00:23:33,840 Speaker 2: because obviously this is cribbing from a bunch of fucking 499 00:23:33,880 --> 00:23:39,040 Speaker 2: Scorsese like kind of looked like yeah, and Thomas Can and. 500 00:23:38,960 --> 00:23:41,920 Speaker 3: Later On twenty four nine and Denny Villanu in general, 501 00:23:42,000 --> 00:23:43,679 Speaker 3: like all of his films have been like a massive 502 00:23:43,720 --> 00:23:48,880 Speaker 3: source for for these emotion and still generations, so much 503 00:23:48,920 --> 00:23:50,800 Speaker 3: so that like I think like Later On twenty forty 504 00:23:50,880 --> 00:23:52,840 Speaker 3: nine is like one of the easiest films to like 505 00:23:52,840 --> 00:23:56,359 Speaker 3: like replicate film stills almost exactly for based on like 506 00:23:56,400 --> 00:23:58,920 Speaker 3: how like how like load bearing that film has been 507 00:23:58,960 --> 00:24:01,320 Speaker 3: for a whole bunch of these model that could be 508 00:24:01,359 --> 00:24:02,440 Speaker 3: due to a number of factors. 509 00:24:02,680 --> 00:24:05,080 Speaker 2: Now, I know what you're wondering, how soon until we 510 00:24:05,080 --> 00:24:07,639 Speaker 2: can get a full ninety minute movie that looks like this? 511 00:24:07,840 --> 00:24:09,440 Speaker 3: Oh, I'm guessing days away. 512 00:24:09,880 --> 00:24:12,199 Speaker 2: No, no, Jason said, probably not at least for a 513 00:24:12,240 --> 00:24:12,960 Speaker 2: decade or so. 514 00:24:13,160 --> 00:24:16,120 Speaker 3: Really, Okay, that's interesting. 515 00:24:16,400 --> 00:24:17,560 Speaker 6: I don't want to wait that long. 516 00:24:17,640 --> 00:24:18,800 Speaker 2: What a worthwhile endeffort? 517 00:24:18,920 --> 00:24:22,080 Speaker 3: Because he could have said shorter, that actually is interesting. 518 00:24:22,119 --> 00:24:25,399 Speaker 8: He could have said anything those um I think it 519 00:24:25,560 --> 00:24:28,520 Speaker 8: is like he did have to spend probably hundreds of 520 00:24:28,560 --> 00:24:32,960 Speaker 8: hours of his precious one human life stitching those those. 521 00:24:32,720 --> 00:24:35,960 Speaker 2: Turts together, and he's like, it's nowhere near ready. There's 522 00:24:35,960 --> 00:24:37,720 Speaker 2: no way it could make a nice he's giving himself 523 00:24:37,720 --> 00:24:39,720 Speaker 2: a yeah. 524 00:24:39,760 --> 00:24:44,760 Speaker 10: Because I've only really seen one interesting genitive video thing. 525 00:24:44,760 --> 00:24:48,760 Speaker 10: But it wasn't a generative video thing. It was they filmed, uh, Brian, 526 00:24:48,840 --> 00:24:52,399 Speaker 10: you know, filmed a documentary and they created, you know, 527 00:24:52,480 --> 00:24:56,399 Speaker 10: some backhand software so that they would be able to 528 00:24:57,359 --> 00:25:01,439 Speaker 10: do cut of existing footage and try to total on 529 00:25:01,840 --> 00:25:06,199 Speaker 10: parts of the documentary. But I never ever see anything 530 00:25:06,280 --> 00:25:09,520 Speaker 10: interested in like constructing narratives or it's a like you 531 00:25:09,600 --> 00:25:13,639 Speaker 10: can't teasing other aspects of the creative process. It's only 532 00:25:13,960 --> 00:25:16,520 Speaker 10: let's try to replace, right, Let's try to so you. 533 00:25:16,520 --> 00:25:17,679 Speaker 6: Can't do narrative with it. 534 00:25:17,760 --> 00:25:18,320 Speaker 3: And that's the thing. 535 00:25:18,400 --> 00:25:20,920 Speaker 2: If if I'd sat down there, because I was sitting 536 00:25:20,920 --> 00:25:22,440 Speaker 2: I said this. I was sitting next to a guy 537 00:25:22,440 --> 00:25:24,080 Speaker 2: from usc who was one of the only people in 538 00:25:24,119 --> 00:25:27,200 Speaker 2: the room who was like similarly critical to me of 539 00:25:27,280 --> 00:25:29,440 Speaker 2: what we were seeing on stage. It was like, look 540 00:25:29,560 --> 00:25:31,080 Speaker 2: if they had come down and been like, look, this 541 00:25:31,119 --> 00:25:32,720 Speaker 2: is how we can plug a script in and it 542 00:25:32,760 --> 00:25:35,399 Speaker 2: can create a storyboard and you can like kind of 543 00:25:35,440 --> 00:25:38,040 Speaker 2: see like a crude CGI animation of how the shots 544 00:25:38,040 --> 00:25:39,600 Speaker 2: will look, and that can help you like plan out, 545 00:25:39,720 --> 00:25:42,879 Speaker 2: like like that's legitimately useful. That's the thing that adds 546 00:25:43,000 --> 00:25:45,720 Speaker 2: value and can cut costs in a meaningful way to 547 00:25:45,840 --> 00:25:49,320 Speaker 2: like the production of good TV and movies. But that's 548 00:25:49,400 --> 00:25:52,399 Speaker 2: not as sexy as like I'm and they were all talking. 549 00:25:52,400 --> 00:25:56,800 Speaker 2: There was this this like very weird moment where one 550 00:25:56,800 --> 00:26:00,000 Speaker 2: of the panels Leslie Shannon, who's head of innovation for Nochio, 551 00:26:00,400 --> 00:26:02,240 Speaker 2: a company that used to make phones and now makes 552 00:26:02,240 --> 00:26:04,560 Speaker 2: panelists who pretend to be entertained by awkward. 553 00:26:04,440 --> 00:26:06,720 Speaker 3: They also like make cameras and. 554 00:26:07,000 --> 00:26:08,760 Speaker 2: They make a lot of stuff. I was just shitting 555 00:26:08,760 --> 00:26:12,120 Speaker 2: on Nokia. She's like, can we use neuroscience to see 556 00:26:12,200 --> 00:26:15,520 Speaker 2: how people are reacting to AI generated videos and then 557 00:26:15,560 --> 00:26:18,800 Speaker 2: adjust the ending to be like, you know, let's make 558 00:26:18,800 --> 00:26:21,440 Speaker 2: this resonation of a night. That way, we're helping the creative. 559 00:26:21,480 --> 00:26:23,400 Speaker 2: And I was like, are you out of your fucking mind? 560 00:26:23,520 --> 00:26:26,800 Speaker 6: We attach electrodes to people skulls? 561 00:26:27,200 --> 00:26:30,520 Speaker 2: I would I would have supported electrodes in their skulls. Yes, 562 00:26:30,880 --> 00:26:33,479 Speaker 2: Jesus Christ, we should do the monkeyin thing. 563 00:26:33,800 --> 00:26:38,639 Speaker 4: Perhaps a pair of calipers, some skulls. 564 00:26:38,680 --> 00:26:41,800 Speaker 2: I am fascinating the skull shapes of that fucking so to. 565 00:26:41,640 --> 00:26:44,320 Speaker 5: Say that is there's so many things that you've said 566 00:26:44,320 --> 00:26:46,720 Speaker 5: that just they wouldn't survive at that position. 567 00:26:47,200 --> 00:26:49,879 Speaker 2: Speaking of things that wouldn't survive a deposition the sponsors 568 00:26:49,880 --> 00:27:04,600 Speaker 2: to this podcast. Okay, so that first panel was a 569 00:27:04,640 --> 00:27:07,560 Speaker 2: real moment for me. I went through a couple of more, 570 00:27:07,800 --> 00:27:10,320 Speaker 2: one of which was on like advertising and AI and 571 00:27:10,520 --> 00:27:15,600 Speaker 2: was mostly mostly pretty boring. The third panel I went through, though, 572 00:27:16,080 --> 00:27:20,080 Speaker 2: was called AI Cinematics, Spatial and XR and I just 573 00:27:20,119 --> 00:27:22,080 Speaker 2: want to actually play you guys, you'll have to cluster around. 574 00:27:22,080 --> 00:27:24,920 Speaker 5: I would actually believe that was generated with chat GPT 575 00:27:25,359 --> 00:27:26,800 Speaker 5: from a GPT two point zero. 576 00:27:27,400 --> 00:27:28,919 Speaker 2: So let's start with this one. 577 00:27:29,119 --> 00:27:31,960 Speaker 3: AI will be more impactful than the Internet. 578 00:27:36,200 --> 00:27:43,680 Speaker 2: Maybe, Yes, it's a trick question because it is the Internet. 579 00:27:43,960 --> 00:27:47,240 Speaker 1: That was that was the Internet, so not although it 580 00:27:47,320 --> 00:27:48,439 Speaker 1: can wrong without the Internet. 581 00:27:48,760 --> 00:27:50,440 Speaker 3: So I'm like, oh, yeah, there you go. 582 00:27:50,520 --> 00:27:54,680 Speaker 2: All right, what's what when you impact AI? 583 00:27:55,280 --> 00:27:59,919 Speaker 3: AI is going to resolve in astronomical job losses? 584 00:28:02,960 --> 00:28:06,440 Speaker 4: Uh, there will be an evolution of job long. 585 00:28:08,280 --> 00:28:16,159 Speaker 2: Next, that was the scene I wanted you to hear. 586 00:28:16,280 --> 00:28:17,720 Speaker 2: They're like, we don't want to say it out loud, 587 00:28:17,720 --> 00:28:18,960 Speaker 2: and then everyone chuckles. 588 00:28:19,040 --> 00:28:20,840 Speaker 1: These people are too fucking smug. 589 00:28:21,160 --> 00:28:23,840 Speaker 5: Yeah, these people sound too confident and too chummy and 590 00:28:23,880 --> 00:28:25,359 Speaker 5: too happy to say things about this. 591 00:28:26,080 --> 00:28:26,680 Speaker 6: That's not good. 592 00:28:26,680 --> 00:28:28,480 Speaker 1: I don't a lot of these people laughing about people 593 00:28:28,480 --> 00:28:29,119 Speaker 1: losing jobs. 594 00:28:29,200 --> 00:28:30,600 Speaker 2: No, I shouldn't have jobs. 595 00:28:30,680 --> 00:28:32,200 Speaker 6: That's that's a good place to stop. 596 00:28:32,400 --> 00:28:35,360 Speaker 2: Yeah, I don't like that either. And the people you're 597 00:28:35,359 --> 00:28:36,879 Speaker 2: hearing from. Let me let me tell you who's in 598 00:28:36,880 --> 00:28:41,000 Speaker 2: this fucking panel who was just laughing about, like, well, 599 00:28:41,120 --> 00:28:45,720 Speaker 2: there will be a un evolution law. 600 00:28:46,480 --> 00:28:47,120 Speaker 4: Yeah. 601 00:28:47,200 --> 00:28:49,840 Speaker 2: So the motherfuckers who were all that panel laughing about 602 00:28:49,840 --> 00:28:54,840 Speaker 2: people losing their jobs. Ted Shillowitz literally his name is Shilowitz, 603 00:28:55,320 --> 00:28:57,720 Speaker 2: futurist at Cinemersion, Inc. 604 00:28:58,160 --> 00:28:59,840 Speaker 3: That's like a jame. 605 00:29:04,000 --> 00:29:08,280 Speaker 2: Rebecca Barkin, co founder and CEO laman O One, Aaron 606 00:29:08,440 --> 00:29:18,800 Speaker 2: Luber Director Partnerships at Google IPG Media Lab, Mayla Emir Sadegi, 607 00:29:19,000 --> 00:29:23,960 Speaker 2: Principal program Manager at Engineering Microsoft, and Katie Henson, s 608 00:29:24,080 --> 00:29:27,520 Speaker 2: VP post Production as Fear Studios. So those are the people. 609 00:29:27,240 --> 00:29:30,680 Speaker 3: Who are sore, sad, all laughing and like it's like 610 00:29:31,120 --> 00:29:34,400 Speaker 3: generative AI is like good at like one thing creatively, 611 00:29:34,840 --> 00:29:39,320 Speaker 3: it's good at like streamlining VFX, like workflow to the 612 00:29:39,360 --> 00:29:42,960 Speaker 3: workflow of of how to do like it is it 613 00:29:43,040 --> 00:29:46,000 Speaker 3: is like there's aspect. Famously, the only useful thing it's 614 00:29:46,040 --> 00:29:48,720 Speaker 3: been used for is making people's eyes blue in Dune 615 00:29:48,720 --> 00:29:49,120 Speaker 3: Part two. 616 00:29:49,160 --> 00:29:51,280 Speaker 2: It's not one hundred billion dollars. 617 00:29:51,600 --> 00:29:54,520 Speaker 3: And like it is applicable for like changing objects into 618 00:29:54,600 --> 00:29:57,040 Speaker 3: other objects on screen. It can produce really like kind 619 00:29:57,080 --> 00:29:59,800 Speaker 3: of odd like uncanny effects that could be utilized by 620 00:29:59,840 --> 00:30:03,080 Speaker 3: a team of human artists. Really well, what it can't 621 00:30:03,160 --> 00:30:05,640 Speaker 3: do is generate a short film that is in any 622 00:30:05,640 --> 00:30:11,400 Speaker 3: way compelling. Is well that is anyway compelling as a 623 00:30:11,400 --> 00:30:13,520 Speaker 3: piece of art, okay, And the fact that they're like 624 00:30:13,600 --> 00:30:15,840 Speaker 3: laughing at how much how much of. 625 00:30:16,280 --> 00:30:21,120 Speaker 5: Lost enough jobs they have not that had structures full 626 00:30:21,240 --> 00:30:24,000 Speaker 5: to the beauty of the flame, right. 627 00:30:24,120 --> 00:30:27,520 Speaker 2: Although the AI keeps keeps foreboding coming for them. 628 00:30:27,880 --> 00:30:30,320 Speaker 4: It wants something fames. 629 00:30:30,560 --> 00:30:32,240 Speaker 2: I'm going to end on a happy note because the 630 00:30:32,320 --> 00:30:35,360 Speaker 2: last panel I went to was actually really cool. It 631 00:30:35,400 --> 00:30:38,400 Speaker 2: was AI and the Crisis of Creative Rights, Deep Fakes, 632 00:30:38,440 --> 00:30:41,960 Speaker 2: ethics and the Law, and it featured the first intelligent 633 00:30:42,000 --> 00:30:44,960 Speaker 2: person that I've seen at CEES this year, Moya McTeer, 634 00:30:45,040 --> 00:30:47,800 Speaker 2: who is a folklorist and senior advisor at the Human 635 00:30:47,880 --> 00:30:52,720 Speaker 2: Artistry Campaign. It also featured Duncan Crabtree Ireland, who's the 636 00:30:52,800 --> 00:30:55,920 Speaker 2: national executive director and chief negotiator of sag Aftra. There 637 00:30:55,960 --> 00:30:58,480 Speaker 2: we go, There we go, and this was no bullshit. 638 00:30:58,560 --> 00:31:00,400 Speaker 2: It was talking about all of the different law suits 639 00:31:00,400 --> 00:31:02,160 Speaker 2: that are going on right now, all of the litigation 640 00:31:02,280 --> 00:31:06,320 Speaker 2: around AI, and like the actual strategy for litigating, and 641 00:31:06,360 --> 00:31:08,120 Speaker 2: like there was a couple of points where like Duncan 642 00:31:08,200 --> 00:31:10,440 Speaker 2: was like a lot is going to hinge on some 643 00:31:10,640 --> 00:31:14,280 Speaker 2: very brave, very famous people choosing to throw down some 644 00:31:14,400 --> 00:31:17,520 Speaker 2: big dollar lawsuits, Like that's what we need right now. 645 00:31:17,840 --> 00:31:20,040 Speaker 2: They did talk about the No Fakes Act, which has 646 00:31:20,040 --> 00:31:22,840 Speaker 2: bipartisan support and gives some legal force to allow people 647 00:31:22,840 --> 00:31:25,640 Speaker 2: to push for AI copies of themselves to be taken down. 648 00:31:26,160 --> 00:31:28,719 Speaker 2: And they think there's also some bipartisan possibility to get 649 00:31:28,760 --> 00:31:31,400 Speaker 2: AI labeling like legislation. 650 00:31:31,600 --> 00:31:33,200 Speaker 5: The thing is, any of these things would be fucking 651 00:31:33,200 --> 00:31:36,360 Speaker 5: fail because if what you have to remove something from a. 652 00:31:36,400 --> 00:31:37,920 Speaker 1: Model, how the fuck do we do that? 653 00:31:38,040 --> 00:31:40,560 Speaker 4: Yet we don't know you have the entire module, you. 654 00:31:40,520 --> 00:31:42,760 Speaker 1: Have to retrain like it, there's no way around it. 655 00:31:43,240 --> 00:31:46,280 Speaker 2: Yeah, And there was a really good point where kind 656 00:31:46,320 --> 00:31:47,640 Speaker 2: of at the end of this part of what I 657 00:31:47,680 --> 00:31:49,880 Speaker 2: appreciate is again there was no bullshit, Like Moya at 658 00:31:49,920 --> 00:31:52,080 Speaker 2: one point was like, I think it is absolutely it 659 00:31:52,240 --> 00:31:55,320 Speaker 2: being generative AI is absolutely a net negative for the 660 00:31:55,400 --> 00:31:57,880 Speaker 2: artistic community. The point is, the point is not to 661 00:31:57,880 --> 00:32:00,080 Speaker 2: get something out as quick as possible to like make art. 662 00:32:00,440 --> 00:32:02,840 Speaker 3: And this has to be like one of maybe five 663 00:32:02,880 --> 00:32:04,760 Speaker 3: people who are doing panels at the CEES who's like 664 00:32:04,800 --> 00:32:06,120 Speaker 3: willing to say that yes. 665 00:32:06,160 --> 00:32:08,000 Speaker 2: And Duncan got I was like, look, you can't stop 666 00:32:08,000 --> 00:32:10,520 Speaker 2: the technology from being invented, so the best path forward 667 00:32:10,560 --> 00:32:12,880 Speaker 2: is to like try and channel this into a direction 668 00:32:13,040 --> 00:32:16,040 Speaker 2: that like is at least better for artists. Like they were, 669 00:32:16,080 --> 00:32:18,080 Speaker 2: there was very little for most of the people on 670 00:32:18,120 --> 00:32:20,959 Speaker 2: the panel, very little bullshit. There was some bullshit from 671 00:32:21,000 --> 00:32:24,120 Speaker 2: one person on the panel, Jenny Katzman, Senior director of 672 00:32:24,120 --> 00:32:29,800 Speaker 2: Government Affairs from Microsoft. That was fun. So after there's 673 00:32:29,800 --> 00:32:31,760 Speaker 2: this whole point where like everyone else on the panels like, yeah, 674 00:32:31,760 --> 00:32:33,720 Speaker 2: I think it's probably in that negative for artists on 675 00:32:33,800 --> 00:32:37,160 Speaker 2: the whole, and Jenny comes on she's like, actually, I 676 00:32:37,160 --> 00:32:40,400 Speaker 2: think it's a net positive and her example of this is, well, 677 00:32:40,520 --> 00:32:41,920 Speaker 2: you know, think there's a lot of stuff that you 678 00:32:41,960 --> 00:32:44,160 Speaker 2: couldn't do before that. Thanks to AI, you could do 679 00:32:44,640 --> 00:32:48,120 Speaker 2: like d aging Harrison Ford for the Indiana Jones movie. 680 00:32:48,600 --> 00:32:55,600 Speaker 3: Something everyone everyone loved and great creative. 681 00:32:56,320 --> 00:32:58,080 Speaker 5: This is the fucking problem with all of this, on 682 00:32:58,120 --> 00:33:00,200 Speaker 5: top of house ship it is and how expensive it is. 683 00:33:00,760 --> 00:33:03,120 Speaker 5: Which kind of AI are we talking about? That dipshit 684 00:33:03,240 --> 00:33:06,000 Speaker 5: that's not generative AI. That's not what that fucking was. 685 00:33:06,040 --> 00:33:09,120 Speaker 3: And they still suck, And it also steals us from 686 00:33:09,160 --> 00:33:13,440 Speaker 3: being able to cast a young River Phoenix explain lovely. 687 00:33:13,960 --> 00:33:17,160 Speaker 2: The only things getting cast in more stuff, Garrett, I'm 688 00:33:17,640 --> 00:33:18,360 Speaker 2: very unfair. 689 00:33:19,120 --> 00:33:21,000 Speaker 3: Well, luckily with the power of AI. 690 00:33:21,760 --> 00:33:23,000 Speaker 6: Look, I can put. 691 00:33:23,480 --> 00:33:27,239 Speaker 2: Into every newspaper sequentially starting in eighteen thirty four, so 692 00:33:27,280 --> 00:33:29,680 Speaker 2: I've not gotten to the end of Phoenix. It would 693 00:33:29,720 --> 00:33:31,000 Speaker 2: be a really long career. 694 00:33:31,440 --> 00:33:32,240 Speaker 3: It would be really. 695 00:33:32,040 --> 00:33:35,560 Speaker 2: Cool sleeping guy. I think he's got the bold ideas. 696 00:33:36,320 --> 00:33:38,920 Speaker 2: This is gonna work out really well for Germany. 697 00:33:39,120 --> 00:33:41,000 Speaker 3: It would be really cool that instead of just doing 698 00:33:41,040 --> 00:33:43,520 Speaker 3: Young Harrison for they just do a River Phoenix deep 699 00:33:43,560 --> 00:33:44,320 Speaker 3: fake for. 700 00:33:46,600 --> 00:33:48,160 Speaker 2: You generate him. 701 00:33:49,160 --> 00:33:50,560 Speaker 3: Look it's canonical. 702 00:33:50,760 --> 00:33:52,320 Speaker 2: Yeah great. 703 00:33:52,560 --> 00:33:55,000 Speaker 5: Oh I love the movies in the future of them too. 704 00:33:55,160 --> 00:33:56,920 Speaker 5: This is so good. This is so bad. 705 00:33:57,000 --> 00:33:57,840 Speaker 3: James mangled. 706 00:33:57,880 --> 00:34:00,640 Speaker 2: You're a hackenel So I gotta say it was very 707 00:34:00,640 --> 00:34:04,120 Speaker 2: funny because she also suggests Jenny, there's we could use 708 00:34:04,160 --> 00:34:06,520 Speaker 2: animals without causing harm thanks to AI, a thing that 709 00:34:06,560 --> 00:34:08,840 Speaker 2: no one had figured out how to do before. Nobody 710 00:34:08,880 --> 00:34:11,480 Speaker 2: had ever figured out how just like not hurt animals 711 00:34:11,480 --> 00:34:13,880 Speaker 2: in movies that didn't exist before AI. 712 00:34:13,840 --> 00:34:17,320 Speaker 3: I thank god, thankfully AI will never do any harmed 713 00:34:17,800 --> 00:34:19,000 Speaker 3: animals or the environment. 714 00:34:19,880 --> 00:34:22,640 Speaker 7: Nobody asked the lobbyist for Microsoft, what else the company 715 00:34:22,719 --> 00:34:26,279 Speaker 7: is doing with AI, right, with police deployments or with 716 00:34:26,760 --> 00:34:27,960 Speaker 7: fossil fuel companies? 717 00:34:28,080 --> 00:34:29,800 Speaker 2: Yeah? Is that bad for animals? 718 00:34:30,160 --> 00:34:30,359 Speaker 9: No? 719 00:34:30,440 --> 00:34:35,520 Speaker 4: Actually, it's really good. They they need it. They they 720 00:34:35,640 --> 00:34:36,600 Speaker 4: yearned for the moment. 721 00:34:36,880 --> 00:34:41,839 Speaker 2: They love data. Great for their habitats. She said, there's 722 00:34:41,880 --> 00:34:44,759 Speaker 2: issues with employment, but there's lots of issues that fall 723 00:34:44,800 --> 00:34:47,280 Speaker 2: around that, and I do think you need a balance. 724 00:34:47,600 --> 00:34:49,319 Speaker 2: And at the end of it, the guy running the 725 00:34:49,320 --> 00:34:53,560 Speaker 2: panel just says, Okay. 726 00:34:52,600 --> 00:34:54,279 Speaker 7: That sounds like you guys are saying a bunch of 727 00:34:54,360 --> 00:34:55,960 Speaker 7: woke shit on this panel. 728 00:34:56,000 --> 00:34:59,800 Speaker 2: Alright, all right, Microsoft, once. 729 00:35:00,120 --> 00:35:02,080 Speaker 6: On the panel, someone to go and say, what the 730 00:35:02,200 --> 00:35:03,200 Speaker 6: fuck do you mean? 731 00:35:03,760 --> 00:35:06,520 Speaker 2: What do you mean closest to that that you were 732 00:35:06,560 --> 00:35:07,040 Speaker 2: going to get. 733 00:35:07,120 --> 00:35:08,960 Speaker 3: I think we do need a balance of some people 734 00:35:09,000 --> 00:35:11,920 Speaker 3: being fired like these people, and other people keeping their 735 00:35:11,960 --> 00:35:15,240 Speaker 3: jobs like everyone like moya give somebody. 736 00:35:14,960 --> 00:35:17,640 Speaker 7: Has to lose, and somebody has to exactly that's their 737 00:35:17,760 --> 00:35:21,160 Speaker 7: entire Somebody has the guns, somebody doesn't. 738 00:35:21,280 --> 00:35:23,120 Speaker 6: Somebody knows the way the maze works, and. 739 00:35:23,080 --> 00:35:25,440 Speaker 4: Something as gonna what we shouldn't have guns. 740 00:35:25,520 --> 00:35:27,719 Speaker 5: We shouldn't have a man and one of them knows 741 00:35:27,719 --> 00:35:28,520 Speaker 5: the maze and have a gun. 742 00:35:28,719 --> 00:35:32,719 Speaker 2: We should have a gun maze you talking about the gun? Now? Look, 743 00:35:32,760 --> 00:35:35,200 Speaker 2: we all like keeping a couple of people in a 744 00:35:35,280 --> 00:35:38,799 Speaker 2: maze beneath our house, right, Yeah, there's nothing wrong with this. 745 00:35:38,600 --> 00:35:40,840 Speaker 3: This is just the dormant next this we just we 746 00:35:41,000 --> 00:35:41,640 Speaker 3: keep doing it. 747 00:35:43,760 --> 00:35:47,040 Speaker 2: It's it's a nice maze under my house. 748 00:35:47,640 --> 00:35:47,880 Speaker 5: They have. 749 00:35:48,200 --> 00:35:49,520 Speaker 6: It's nice to run some of them. 750 00:35:51,239 --> 00:35:52,360 Speaker 4: Through one of the corners. 751 00:35:52,360 --> 00:35:58,560 Speaker 5: The Minota gets them only sometimes I'm the Minotta. Anyway, 752 00:35:58,600 --> 00:36:01,760 Speaker 5: the gun maze isn't real, So most of their arguments 753 00:36:01,760 --> 00:36:04,520 Speaker 5: can mostly just come down to, well, you can't make 754 00:36:04,560 --> 00:36:07,000 Speaker 5: an omelet without breaking like you have to fucking make people. 755 00:36:07,040 --> 00:36:10,400 Speaker 2: You have to break the human drive to create art, obviously, 756 00:36:10,480 --> 00:36:12,919 Speaker 2: to make an does not taste good. 757 00:36:13,040 --> 00:36:16,120 Speaker 6: Yeah, an omelet esque food. 758 00:36:16,800 --> 00:36:19,360 Speaker 2: It's a piss omelet, Like there's piss in the omelet, 759 00:36:19,800 --> 00:36:21,680 Speaker 2: and we had to we had to burn down this 760 00:36:21,840 --> 00:36:23,800 Speaker 2: esteemed chapel to make the piss armlet. 761 00:36:23,920 --> 00:36:27,480 Speaker 1: Computer made it, though, Yeah, go on clap for the computer. 762 00:36:28,880 --> 00:36:32,279 Speaker 2: We did firebomb the Louverra. But look look at this, 763 00:36:32,560 --> 00:36:35,320 Speaker 2: Look at this rock star? 764 00:36:37,160 --> 00:36:37,680 Speaker 3: Oh god? 765 00:36:38,239 --> 00:36:40,560 Speaker 2: All right, well that's the episode. That's all I got, folks. 766 00:36:40,560 --> 00:36:43,440 Speaker 2: That was my first day at CES twenty twenty five. Huzzah. 767 00:36:43,760 --> 00:36:45,960 Speaker 1: Yeah, this is just my first day. Better offlines here 768 00:36:46,000 --> 00:36:46,440 Speaker 1: all week. 769 00:36:46,840 --> 00:36:48,840 Speaker 5: I'm gonna hear about stuff like this all week, and 770 00:36:48,880 --> 00:36:50,920 Speaker 5: I think I'm gonna be fully jokeified. 771 00:36:50,920 --> 00:36:53,160 Speaker 6: I'm gonna wake up in the clown makeup on Friday. 772 00:36:53,200 --> 00:36:55,680 Speaker 4: I'm gonna find the funnest thing to bring back for you. 773 00:36:55,920 --> 00:36:58,640 Speaker 5: I'm gonna find a an artist to put me in 774 00:36:58,680 --> 00:36:59,080 Speaker 5: full joke. 775 00:36:59,160 --> 00:37:02,600 Speaker 2: Now, I'm gonna try to steal that AI enhanced grill 776 00:37:04,239 --> 00:37:07,399 Speaker 2: grill that you can I just like move this around. 777 00:37:07,440 --> 00:37:10,239 Speaker 2: I just want to test that would roll open the door, 778 00:37:10,280 --> 00:37:10,959 Speaker 2: open the door. 779 00:37:11,239 --> 00:37:13,600 Speaker 5: As someone who's done a lot of like grilling, done 780 00:37:13,640 --> 00:37:16,239 Speaker 5: a lot of spoken barbecue, I don't know what I 781 00:37:16,520 --> 00:37:16,840 Speaker 5: would do. 782 00:37:16,920 --> 00:37:17,840 Speaker 6: Is it gonna talk to me in the. 783 00:37:18,440 --> 00:37:20,879 Speaker 2: Wait till you are you? Are you trying to tell 784 00:37:21,000 --> 00:37:24,319 Speaker 2: us here at z tray that you have grilled meat 785 00:37:24,480 --> 00:37:27,440 Speaker 2: without a robot texting you about it? Because I just 786 00:37:27,440 --> 00:37:27,960 Speaker 2: don't believe. 787 00:37:28,040 --> 00:37:31,080 Speaker 1: I don't know how I did it, but I did it. 788 00:37:31,320 --> 00:37:34,239 Speaker 2: You're never is always dreamed of knowing how to cook 789 00:37:35,840 --> 00:37:37,520 Speaker 2: the robots. It was impossible. 790 00:37:38,000 --> 00:37:40,040 Speaker 6: Oh god, we're at the death of innovation. 791 00:37:40,360 --> 00:37:42,719 Speaker 2: Yeah, at the end of a lot of things maybe 792 00:37:42,960 --> 00:37:44,719 Speaker 2: and the end of the episode. Yeah, and the end 793 00:37:44,760 --> 00:37:48,600 Speaker 2: of the episode, thank god. You know, everyone else be 794 00:37:48,719 --> 00:37:57,279 Speaker 2: the cyber truck in the It could happened here is 795 00:37:57,320 --> 00:37:58,840 Speaker 2: a production of cool Zone Media. 796 00:37:59,040 --> 00:38:02,879 Speaker 3: For what podcast cool Zone Media, visit our website Coolzonmedia 797 00:38:02,920 --> 00:38:05,719 Speaker 3: dot com, or check us out on the iHeartRadio app, 798 00:38:05,800 --> 00:38:09,359 Speaker 3: Apple Podcasts, or wherever you listen to podcasts. You can 799 00:38:09,400 --> 00:38:11,760 Speaker 3: now find sources for it could happen here, listed directly 800 00:38:11,760 --> 00:38:13,040 Speaker 3: in episode descriptions. 801 00:38:13,239 --> 00:38:14,080 Speaker 4: Thanks for listening.