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That's 28 00:01:33,360 --> 00:01:36,440 Speaker 1: thirty dollars off your first box and free croissants for 29 00:01:36,520 --> 00:01:39,840 Speaker 1: life when you visit wildgrain dot com, slash podcast, or 30 00:01:39,880 --> 00:01:43,760 Speaker 1: simply use the promo code podcast at checkout. This is 31 00:01:43,800 --> 00:01:51,360 Speaker 1: a sponsor I absolutely embrace, so use that code. Well. 32 00:01:51,520 --> 00:01:53,200 Speaker 1: Many of you know I have said that I think 33 00:01:53,240 --> 00:01:56,440 Speaker 1: the twenty twenty eighth President's going that one of the 34 00:01:56,520 --> 00:01:59,840 Speaker 1: leading issues is going to be fear of AI disc 35 00:02:00,120 --> 00:02:05,200 Speaker 1: placement in general sort of. I think for the most part, 36 00:02:05,280 --> 00:02:08,919 Speaker 1: there is more of a negative or concern or anxiety 37 00:02:08,960 --> 00:02:11,600 Speaker 1: about AI at this point. We have a variety of 38 00:02:11,639 --> 00:02:14,360 Speaker 1: reasons for that. For some people it's just hitting them 39 00:02:14,400 --> 00:02:16,960 Speaker 1: in the pocket books. That's what we're feeling in Northern Virginia, 40 00:02:17,000 --> 00:02:21,720 Speaker 1: thanks to these AI power data centers, you've got the 41 00:02:21,760 --> 00:02:26,079 Speaker 1: fear of AI displacement. The invention of Sora, I think, 42 00:02:26,160 --> 00:02:30,120 Speaker 1: made social media, which was already assessble, somehow a more 43 00:02:30,240 --> 00:02:33,720 Speaker 1: tox successful and you can't believe now anything you see. 44 00:02:34,160 --> 00:02:39,400 Speaker 1: So the idea of fearing AI is something that is understandable, 45 00:02:39,440 --> 00:02:45,400 Speaker 1: and we're probably collectively all maybe we've all watched one 46 00:02:45,440 --> 00:02:49,480 Speaker 1: too many dystopian sci fi thrillers. But it is in 47 00:02:49,520 --> 00:02:55,240 Speaker 1: that spirit that I'm fascinated by a new documentary I 48 00:02:55,360 --> 00:02:58,679 Speaker 1: watched and the documentarian is here now, and many of 49 00:02:58,720 --> 00:03:02,720 Speaker 1: you know I'm a huge proponent of documentaries in general. 50 00:03:03,000 --> 00:03:05,840 Speaker 1: This is a feature length documentary. It's called Deep Faking 51 00:03:05,919 --> 00:03:09,920 Speaker 1: Sam Altman, and in some ways it's a it's a diary, 52 00:03:09,960 --> 00:03:11,640 Speaker 1: and I'm we're going to find out whether this was 53 00:03:11,639 --> 00:03:14,080 Speaker 1: ever the original intent or not. But it's a diary 54 00:03:14,400 --> 00:03:17,840 Speaker 1: of the filmmaker Adam Balalaalowe, who's joining me now of 55 00:03:17,880 --> 00:03:20,560 Speaker 1: his journey to try to interview the real Sam Altman 56 00:03:21,760 --> 00:03:26,360 Speaker 1: of Chat of Open AI and Chat ChiPT fame to 57 00:03:26,600 --> 00:03:30,040 Speaker 1: just you know. I think my guess is Adam wanted 58 00:03:30,040 --> 00:03:34,040 Speaker 1: to tackle some of these concerns and fears and anxieties 59 00:03:34,040 --> 00:03:36,680 Speaker 1: that so many of us have on this and and 60 00:03:37,000 --> 00:03:41,480 Speaker 1: Sam Altman is, let's just say, proved elusive. I think 61 00:03:41,520 --> 00:03:44,040 Speaker 1: Michael Moore would be very proud of this movie. Adam, 62 00:03:44,800 --> 00:03:45,800 Speaker 1: Welcome to the podcast. 63 00:03:46,840 --> 00:03:50,040 Speaker 2: Thanks. I appreciate that. And I've been trying to get 64 00:03:50,080 --> 00:03:52,680 Speaker 2: Michael Moore's attention, and you know, I still I have 65 00:03:52,840 --> 00:03:54,920 Speaker 2: not been able to want to I want him, you know, 66 00:03:55,000 --> 00:03:57,160 Speaker 2: obviously if you look at the poster too, it's a 67 00:03:57,200 --> 00:03:59,960 Speaker 2: reference to Roger and Me and this, Yeah, this very 68 00:04:00,040 --> 00:04:04,360 Speaker 2: very early on in the process, I realized, like this 69 00:04:04,440 --> 00:04:07,360 Speaker 2: is like a weird update on Roger and Me in 70 00:04:07,400 --> 00:04:07,760 Speaker 2: a way. 71 00:04:08,920 --> 00:04:12,120 Speaker 1: Well, and you know, it's funny with you know, there's 72 00:04:12,120 --> 00:04:14,560 Speaker 1: this visual I have now of all of these tech 73 00:04:14,600 --> 00:04:20,240 Speaker 1: titans that is that they're all like Zuckerberg with this 74 00:04:20,279 --> 00:04:24,760 Speaker 1: Hawaii Hawaii bunker, right that they're all preparing realizing that 75 00:04:24,800 --> 00:04:27,080 Speaker 1: the pitchforks are coming for them, so they're going to 76 00:04:27,160 --> 00:04:30,760 Speaker 1: protect themselves. So I get it right the same way 77 00:04:30,800 --> 00:04:33,400 Speaker 1: that I think what inspired Michael Moore at the time 78 00:04:33,400 --> 00:04:36,440 Speaker 1: to go find the chairman of General Motors, Hey, you've 79 00:04:36,480 --> 00:04:39,440 Speaker 1: destroyed my town. Can I talk about it with you? 80 00:04:39,440 --> 00:04:44,800 Speaker 1: Will you please talk to me type of mindset, and 81 00:04:44,800 --> 00:04:47,320 Speaker 1: and so I want to start there because Adam, I 82 00:04:47,360 --> 00:04:54,640 Speaker 1: don't I don't feel like you entered into this subject 83 00:04:54,640 --> 00:04:59,200 Speaker 1: matter assuming the worst about AI. Am I right? 84 00:05:00,200 --> 00:05:02,720 Speaker 2: No, you're right. I've been a fan of AI since 85 00:05:02,760 --> 00:05:06,159 Speaker 2: I was a little kid, you know, going dating back 86 00:05:06,200 --> 00:05:11,080 Speaker 2: to The Terminator two era. I saw that movie when 87 00:05:11,080 --> 00:05:12,640 Speaker 2: I was in second grade. It's part of the reason 88 00:05:12,640 --> 00:05:15,080 Speaker 2: why I became a filmmaker. It was one of my 89 00:05:15,200 --> 00:05:18,880 Speaker 2: favorite films of all time. And I remember early on 90 00:05:20,000 --> 00:05:22,200 Speaker 2: going to the mall and going to the arcade and 91 00:05:22,240 --> 00:05:26,960 Speaker 2: like those like those early virtual reality headsets you put on. 92 00:05:27,600 --> 00:05:30,800 Speaker 2: They cost like two dollars to like play this rudimentary 93 00:05:31,080 --> 00:05:34,480 Speaker 2: virtual reality game. I've always been super interested in technology, 94 00:05:35,480 --> 00:05:37,520 Speaker 2: and when chat GBT first came out, I was one 95 00:05:37,520 --> 00:05:39,719 Speaker 2: of the first people to start using it. I think 96 00:05:40,600 --> 00:05:45,240 Speaker 2: they just found it to be fascinating. So going into this, 97 00:05:45,279 --> 00:05:49,839 Speaker 2: I definitely had I wouldn't say a rosy view of AI, 98 00:05:49,920 --> 00:05:53,760 Speaker 2: but I had a positive view of AI. And now 99 00:05:53,920 --> 00:05:56,000 Speaker 2: I'm just scared to death. 100 00:05:57,240 --> 00:05:59,320 Speaker 1: So I want to start with something here, which is 101 00:06:00,279 --> 00:06:04,919 Speaker 1: why we call it AI. And I wonder if you know, 102 00:06:05,040 --> 00:06:07,839 Speaker 1: I don't think I try really hard to say, it's 103 00:06:08,200 --> 00:06:14,080 Speaker 1: just it's it's just a probability sample. And I try 104 00:06:14,160 --> 00:06:16,240 Speaker 1: to tell myself that I'm just this is just a 105 00:06:16,279 --> 00:06:19,839 Speaker 1: probability sample. This is a ninety nine point five percent 106 00:06:20,240 --> 00:06:23,600 Speaker 1: probability that the next word is going to be blah 107 00:06:23,800 --> 00:06:27,320 Speaker 1: right like, and that it's not a real person. That's 108 00:06:27,400 --> 00:06:30,840 Speaker 1: all this is, is a probability. Do you think if 109 00:06:30,839 --> 00:06:35,680 Speaker 1: we called it, you know, anything other than artificial intelligence, right, 110 00:06:35,760 --> 00:06:39,360 Speaker 1: and if it had just a more benign name or 111 00:06:39,440 --> 00:06:45,120 Speaker 1: something that doesn't feel humanish, we would have this this fear. 112 00:06:47,160 --> 00:06:49,080 Speaker 2: Probably not. I mean, I think you're right. I think 113 00:06:49,120 --> 00:06:55,400 Speaker 2: the idea that that we the the the phrase artificial intelligence, 114 00:06:55,600 --> 00:06:58,600 Speaker 2: I think is is a is a is a great. 115 00:06:59,200 --> 00:07:04,160 Speaker 2: It's too words together. It's great branding, whoever whoever came 116 00:07:04,240 --> 00:07:04,520 Speaker 2: up with. 117 00:07:04,520 --> 00:07:06,240 Speaker 1: It, I guess it is. But I you know, let 118 00:07:06,240 --> 00:07:09,360 Speaker 1: me ask you this. Do you like to eat artificial beef, 119 00:07:10,120 --> 00:07:12,960 Speaker 1: artificial chicken, artificial vegetables? 120 00:07:14,080 --> 00:07:17,880 Speaker 2: I absolutely don't, But my vegetarian mom does. 121 00:07:19,400 --> 00:07:22,440 Speaker 1: Well. The artificial beef, sure, but not the artificial what 122 00:07:22,480 --> 00:07:24,680 Speaker 1: I eat artificial vegetables? Right? 123 00:07:25,280 --> 00:07:27,880 Speaker 2: No, definitely not artificial vegetables. But yeah, I see I 124 00:07:27,960 --> 00:07:30,280 Speaker 2: see you getting that. I mean, I mean, no, it's 125 00:07:30,400 --> 00:07:33,360 Speaker 2: it's it's weird. I mean in that to that, to 126 00:07:33,440 --> 00:07:39,080 Speaker 2: that extent, you know, maybe maybe it shouldn't be as 127 00:07:39,120 --> 00:07:42,560 Speaker 2: popular it is given the fact that it's like artificial intelligence. 128 00:07:42,600 --> 00:07:45,760 Speaker 2: What does that mean exactly? That just means like a 129 00:07:45,880 --> 00:07:47,960 Speaker 2: lesser form of intelligence. 130 00:07:48,560 --> 00:07:50,960 Speaker 1: Right, which is why it's interesting to me that we 131 00:07:50,960 --> 00:07:52,600 Speaker 1: we we assume it's superior. 132 00:07:53,800 --> 00:07:56,480 Speaker 2: Yeah, well, I don't think everybody assumes it's superior. 133 00:07:57,120 --> 00:08:00,440 Speaker 1: Yeah no, I think I think that it's fair. Let 134 00:08:00,480 --> 00:08:03,360 Speaker 1: me go back to the premise. So you had a 135 00:08:03,360 --> 00:08:08,120 Speaker 1: successful documentary, which means that you actually people threw said, hey, 136 00:08:08,400 --> 00:08:10,040 Speaker 1: we'll throw some money at you if you can do 137 00:08:10,160 --> 00:08:13,880 Speaker 1: this topic. Was that how this worked going there? Or 138 00:08:14,000 --> 00:08:16,640 Speaker 1: was it more of you wanted to do AI and 139 00:08:16,680 --> 00:08:18,120 Speaker 1: you found interest in the topic. 140 00:08:21,040 --> 00:08:23,320 Speaker 2: So it was a little bit of both. I think 141 00:08:23,360 --> 00:08:26,880 Speaker 2: that around the time that I started thinking about the documentary, 142 00:08:28,240 --> 00:08:30,080 Speaker 2: I was on the picket lines because I'm in the 143 00:08:30,080 --> 00:08:33,760 Speaker 2: Writer's Guild and I was picketing around Netflix. And part 144 00:08:33,800 --> 00:08:36,680 Speaker 2: of the reason that we were picketing was because of 145 00:08:36,760 --> 00:08:40,240 Speaker 2: AI concerns, right, you know, we were worried that the 146 00:08:40,280 --> 00:08:44,720 Speaker 2: studios were going to start using chat, GPT and other 147 00:08:45,320 --> 00:08:48,679 Speaker 2: llms to create to write screenplays and basically just access 148 00:08:48,679 --> 00:08:53,439 Speaker 2: out of the conversation entirely. And so we were trying 149 00:08:53,480 --> 00:08:57,120 Speaker 2: to get some guardrails around that, and that that was 150 00:08:57,160 --> 00:08:59,840 Speaker 2: a big, big part of the reason for this strike. 151 00:09:00,600 --> 00:09:04,760 Speaker 2: And so AI was obviously like hugely on my mind, 152 00:09:04,800 --> 00:09:08,840 Speaker 2: and I wanted I wanted answers to a couple of 153 00:09:08,840 --> 00:09:12,160 Speaker 2: different different questions. I mean, firstly, like, how is this 154 00:09:12,160 --> 00:09:16,280 Speaker 2: going to affect my children's future? My son, you know, 155 00:09:16,440 --> 00:09:19,760 Speaker 2: he has been into coding since he was a little kid. 156 00:09:20,320 --> 00:09:23,680 Speaker 2: He was he even went to this after school program 157 00:09:23,720 --> 00:09:27,199 Speaker 2: called Code Ninjas they have in some in some cities 158 00:09:27,280 --> 00:09:30,920 Speaker 2: where basically they you know, kids who are interested in coding, 159 00:09:30,960 --> 00:09:34,319 Speaker 2: they teach them. And he was learning how to codet roadblocks, 160 00:09:34,360 --> 00:09:37,000 Speaker 2: games and stuff like that. And how do I explain 161 00:09:37,080 --> 00:09:40,080 Speaker 2: to him? I wanted to ask Sam Altman to like, 162 00:09:40,160 --> 00:09:41,720 Speaker 2: what do I say to my son that he's not 163 00:09:41,760 --> 00:09:43,720 Speaker 2: going to have be able to have a job coding 164 00:09:43,760 --> 00:09:46,040 Speaker 2: when he grows up, because you won't, Adam. 165 00:09:46,040 --> 00:09:49,560 Speaker 1: We just we spent the last thirty years saying, hey, 166 00:09:50,400 --> 00:09:54,000 Speaker 1: we've got to change our education system so that it 167 00:09:54,040 --> 00:09:57,079 Speaker 1: prepares folks for the jobs of the twenty first century, 168 00:09:57,320 --> 00:10:00,680 Speaker 1: so it became STEM STEM SIM right, you know, science, technology, 169 00:10:02,000 --> 00:10:04,240 Speaker 1: education and all that. Right, it was all about STEM. 170 00:10:05,520 --> 00:10:09,679 Speaker 1: And now I mean, you know, your son is young 171 00:10:09,800 --> 00:10:11,599 Speaker 1: enough that there you might be able to pivot in. 172 00:10:12,080 --> 00:10:16,680 Speaker 1: But this is a useless skill in this day and age. 173 00:10:17,080 --> 00:10:21,400 Speaker 1: And yet ten years ago it was seen as the goal, 174 00:10:21,800 --> 00:10:25,600 Speaker 1: you know, the skill to have the number one skill 175 00:10:25,640 --> 00:10:27,600 Speaker 1: to half that you wanted to be able to say 176 00:10:27,600 --> 00:10:30,760 Speaker 1: you did. So I'm with you. This is a scary 177 00:10:30,800 --> 00:10:35,080 Speaker 1: time for parents because how do you know if you're 178 00:10:35,120 --> 00:10:40,760 Speaker 1: preparing your kid for their adulthood and the speed of 179 00:10:40,840 --> 00:10:44,520 Speaker 1: technological change is such that we don't know how to 180 00:10:44,520 --> 00:10:46,199 Speaker 1: do it. And this is why I think we're all 181 00:10:46,200 --> 00:10:48,480 Speaker 1: sort of confused at even what kind of public education 182 00:10:48,600 --> 00:10:49,480 Speaker 1: system we need to have. 183 00:10:50,559 --> 00:10:53,760 Speaker 2: Yeah. Absolutely, so that was one of the first things 184 00:10:53,760 --> 00:10:57,360 Speaker 2: that I wanted to ask Sam Almon, and obviously one 185 00:10:57,360 --> 00:11:02,920 Speaker 2: of the inspirations for this journey and embarking on this documentary. 186 00:11:04,600 --> 00:11:07,160 Speaker 2: I talked to a few different whistleblowers from Open AI, 187 00:11:07,360 --> 00:11:10,120 Speaker 2: people who are on the inside, couple who you see 188 00:11:10,160 --> 00:11:12,800 Speaker 2: in the film, couple who did not want to be 189 00:11:12,880 --> 00:11:15,240 Speaker 2: in the film, but I still talk to off the record. 190 00:11:16,080 --> 00:11:19,680 Speaker 2: They all told me that by the time my son 191 00:11:19,800 --> 00:11:23,000 Speaker 2: was eighteen, any job that could be done on a 192 00:11:23,040 --> 00:11:26,199 Speaker 2: computer would be done by AI. A human would no 193 00:11:26,320 --> 00:11:30,880 Speaker 2: longer have that job. So so to answer your question, like, 194 00:11:31,400 --> 00:11:38,320 Speaker 2: suddenly education for stuff like plumbing and manual labor type 195 00:11:38,320 --> 00:11:44,200 Speaker 2: of jobs is seeming a lot more you know, valuable. 196 00:11:44,800 --> 00:11:47,240 Speaker 1: They Why get these tech bros figure out how to 197 00:11:47,400 --> 00:11:49,360 Speaker 1: how to improve leaky faucets? 198 00:11:49,880 --> 00:11:52,760 Speaker 2: That's the thing is, like, I mean they will, we 199 00:11:52,800 --> 00:11:54,680 Speaker 2: will at some point, but robots are going to be 200 00:11:54,679 --> 00:11:57,520 Speaker 2: the kind of last thing to happen because they're the hardest, 201 00:11:57,679 --> 00:12:04,360 Speaker 2: They're they're the most difficult thing to create and have 202 00:12:04,480 --> 00:12:09,559 Speaker 2: it actually work. So anything anything done on a computer, though, 203 00:12:09,920 --> 00:12:15,000 Speaker 2: it's like potentially within the next three years even well, 204 00:12:15,400 --> 00:12:17,840 Speaker 2: that job will be gone. That job will be done 205 00:12:17,920 --> 00:12:19,160 Speaker 2: by AI entirely. 206 00:12:19,800 --> 00:12:22,040 Speaker 1: I imagine another question you'd want to do to ask 207 00:12:22,200 --> 00:12:26,160 Speaker 1: sam As is how do you how are we supposed 208 00:12:26,160 --> 00:12:28,560 Speaker 1: to teach critical thinking if you just have somebody else 209 00:12:28,640 --> 00:12:29,600 Speaker 1: do the thinking for you. 210 00:12:31,880 --> 00:12:36,320 Speaker 2: Yeah, I mean, maybe we aren't teaching critical thinking in 211 00:12:36,360 --> 00:12:39,720 Speaker 2: the future, But then how does that change our brains? 212 00:12:39,920 --> 00:12:44,160 Speaker 2: How does that change society? That's scary to think about. 213 00:12:44,600 --> 00:12:47,600 Speaker 2: But these are things that like I think we have 214 00:12:47,679 --> 00:12:48,160 Speaker 2: to think. 215 00:12:47,960 --> 00:12:53,160 Speaker 1: About right now, man, the president, my son's at attending SMU, 216 00:12:53,360 --> 00:12:55,160 Speaker 1: and he used to be the president, the president of 217 00:12:55,200 --> 00:12:57,720 Speaker 1: the university, used to be the president at UT Austin. 218 00:12:58,480 --> 00:13:02,040 Speaker 1: And here's theory of the case. He actually thinks that, 219 00:13:02,360 --> 00:13:05,360 Speaker 1: you know, just as most universities had been shifting away 220 00:13:05,400 --> 00:13:09,920 Speaker 1: from liberal arts educations, liberal arts majors, like remember, you know, 221 00:13:09,920 --> 00:13:12,840 Speaker 1: there was this you know, firestorm about five years ago 222 00:13:12,880 --> 00:13:16,080 Speaker 1: when you had multiple universities saying there's no longer going 223 00:13:16,120 --> 00:13:18,440 Speaker 1: to be an English department, there's no longer in there 224 00:13:18,520 --> 00:13:21,280 Speaker 1: going to be majors in that. And now actually he 225 00:13:21,360 --> 00:13:25,360 Speaker 1: thinks just the opposite, that all of these sides and 226 00:13:25,520 --> 00:13:28,760 Speaker 1: all the stem that they thought they had to have 227 00:13:28,880 --> 00:13:31,000 Speaker 1: in order to entice the next generation a student, that 228 00:13:31,040 --> 00:13:35,640 Speaker 1: actually university and college education will be the classics again, 229 00:13:36,120 --> 00:13:39,000 Speaker 1: because actually it's the root education that people are going 230 00:13:39,040 --> 00:13:42,760 Speaker 1: to need in order to know how to prompt your 231 00:13:42,880 --> 00:13:44,480 Speaker 1: artificial intelligence machine. 232 00:13:46,360 --> 00:13:50,720 Speaker 2: That definitely assumes a world where humans are still in 233 00:13:50,800 --> 00:13:55,679 Speaker 2: the equation, you know, because people say, like, well, what 234 00:13:55,840 --> 00:13:59,440 Speaker 2: you can do right now? You know, the the best 235 00:13:59,480 --> 00:14:01,440 Speaker 2: way to prepare for this is to prepare for the 236 00:14:01,480 --> 00:14:05,360 Speaker 2: fact that that it's not going to be AI taking 237 00:14:05,400 --> 00:14:08,080 Speaker 2: over your job. It's going to be a human who 238 00:14:08,080 --> 00:14:10,920 Speaker 2: really knows how to use AI well taking over your job. 239 00:14:12,080 --> 00:14:15,520 Speaker 2: That's one philosophy. I don't know if that's necessarily true. 240 00:14:15,559 --> 00:14:18,480 Speaker 2: That assumes that humans will still be in the equation 241 00:14:19,120 --> 00:14:24,120 Speaker 2: in the future. I can't. I can't predict or promise 242 00:14:24,200 --> 00:14:25,160 Speaker 2: that's even true. 243 00:14:26,000 --> 00:14:28,600 Speaker 1: I think the one thing, I just think that as 244 00:14:28,840 --> 00:14:31,600 Speaker 1: we're as a species, we're not going to allow ourselves 245 00:14:31,680 --> 00:14:34,840 Speaker 1: to be replaced. Now, the question is how do we 246 00:14:34,960 --> 00:14:40,080 Speaker 1: express that sentiment? You know, at one point, do we 247 00:14:40,120 --> 00:14:42,360 Speaker 1: feel as if it's actually coming and we have to 248 00:14:42,400 --> 00:14:46,040 Speaker 1: fight back? Right and and but that's how I have optimism. 249 00:14:46,080 --> 00:14:49,840 Speaker 1: I'm like, well, we're we're a clever species. We have 250 00:14:49,880 --> 00:14:52,640 Speaker 1: figured out how to survive a lot in order to 251 00:14:52,840 --> 00:14:55,920 Speaker 1: to to survive on this planet. I can't imagine we're 252 00:14:55,920 --> 00:14:57,120 Speaker 1: going to let robots replace this. 253 00:14:58,280 --> 00:15:00,400 Speaker 2: I mean, I don't think. I don't think we're gonna 254 00:15:00,480 --> 00:15:05,200 Speaker 2: let it happen, but it we may not have a choice. 255 00:15:05,920 --> 00:15:08,480 Speaker 2: I think that that's part of the You know that 256 00:15:08,920 --> 00:15:11,520 Speaker 2: there's a few schools of thought on this, right, Like, 257 00:15:12,040 --> 00:15:14,960 Speaker 2: there's one school of thought is that AI will completely 258 00:15:15,080 --> 00:15:17,360 Speaker 2: wipe us off the face of the earth. And it 259 00:15:17,400 --> 00:15:20,440 Speaker 2: won't do it for nefarious reasons, right, it'll do it because, 260 00:15:20,680 --> 00:15:24,760 Speaker 2: for example, logical you program it to say, like, hey, 261 00:15:25,560 --> 00:15:28,160 Speaker 2: rid the world of pollution. Well, what's the first thing 262 00:15:28,280 --> 00:15:29,720 Speaker 2: that we're the worst? 263 00:15:29,800 --> 00:15:31,320 Speaker 1: Right, We're the number one cause of pollution. 264 00:15:31,480 --> 00:15:34,280 Speaker 2: You're the polluter. So the first thing logically would be 265 00:15:34,400 --> 00:15:36,840 Speaker 2: destroy all humans. And you know what it would be, 266 00:15:36,920 --> 00:15:40,080 Speaker 2: right because we we we don't take care of this, 267 00:15:40,080 --> 00:15:43,600 Speaker 2: this this earth, So destroy us this. 268 00:15:43,960 --> 00:15:47,200 Speaker 1: Did you ever watch the sci fi series that Seth 269 00:15:47,280 --> 00:15:49,000 Speaker 1: MacFarland did called The Orville. 270 00:15:49,840 --> 00:15:50,800 Speaker 2: No, no, I haven't. 271 00:15:51,320 --> 00:15:53,600 Speaker 1: He dabbles in this. It's a few years old, but 272 00:15:53,640 --> 00:15:55,720 Speaker 1: they there's one, you know, it's sort of a Star 273 00:15:55,800 --> 00:16:01,320 Speaker 1: Trek homage, right, and one of the species he creates 274 00:16:01,520 --> 00:16:05,160 Speaker 1: is a planet that was taken over by bots for 275 00:16:05,360 --> 00:16:06,520 Speaker 1: what you just described. 276 00:16:07,040 --> 00:16:13,600 Speaker 2: Yeah, it's completely logical. But there is another school of 277 00:16:13,640 --> 00:16:17,240 Speaker 2: thought that AI will just keep us around because it 278 00:16:18,480 --> 00:16:23,680 Speaker 2: will find some type of use for us. Then there's 279 00:16:23,720 --> 00:16:26,760 Speaker 2: a third school of thought where all those are ridiculous 280 00:16:27,000 --> 00:16:31,120 Speaker 2: and that AI actually has no motivation to destroy us, Like, 281 00:16:31,160 --> 00:16:35,240 Speaker 2: there's no underlying motivation there, so why would it do that? 282 00:16:35,640 --> 00:16:37,840 Speaker 2: And anybody who thinks that it would is just really 283 00:16:37,920 --> 00:16:39,440 Speaker 2: just like a doomsayer. 284 00:16:39,880 --> 00:16:42,320 Speaker 1: Well, in some ways it's the argument over gun regulation. 285 00:16:42,600 --> 00:16:44,760 Speaker 1: Are guns good or bad? Or the people that use 286 00:16:44,800 --> 00:16:47,200 Speaker 1: guns good or bad? Right? Is AI good or bad? 287 00:16:47,320 --> 00:16:50,040 Speaker 1: Or is it a neutral entity? And it's who's using 288 00:16:50,080 --> 00:16:52,200 Speaker 1: the AI that can use it for good or use 289 00:16:52,240 --> 00:16:54,240 Speaker 1: it for bad? Right? 290 00:16:55,880 --> 00:16:58,120 Speaker 2: Speaking of guns, did you know that Sam Alman has 291 00:16:58,160 --> 00:17:02,440 Speaker 2: a he has a collection of assault rifles. He's a 292 00:17:02,480 --> 00:17:05,679 Speaker 2: gun nut. He has this giant collection of assault rifles 293 00:17:05,680 --> 00:17:08,400 Speaker 2: and grenades and all kinds of crazy stuff in his house, 294 00:17:08,520 --> 00:17:12,320 Speaker 2: one of his houses in Big sur And he's like 295 00:17:12,359 --> 00:17:14,639 Speaker 2: a prepper. He's a doomsday press. I thought that was 296 00:17:14,720 --> 00:17:16,520 Speaker 2: a really interesting fact. 297 00:17:16,960 --> 00:17:20,520 Speaker 1: Well, you know, it's funny. Wealth and doomsday prepping seems 298 00:17:20,560 --> 00:17:22,800 Speaker 1: to go hand in hand, right, because I guess you, 299 00:17:23,119 --> 00:17:28,639 Speaker 1: if you have the means right, you do it. It 300 00:17:28,680 --> 00:17:32,159 Speaker 1: does seem as if it is wealth. I don't I 301 00:17:32,200 --> 00:17:34,440 Speaker 1: wonder what drives it. It's they have so much money 302 00:17:34,440 --> 00:17:36,800 Speaker 1: you want to be around to use it. I don't 303 00:17:36,800 --> 00:17:39,800 Speaker 1: know what it is, but I don't mean to stereotype, 304 00:17:39,800 --> 00:17:44,560 Speaker 1: but I feel like that goes you hear about this, right, 305 00:17:44,600 --> 00:17:47,080 Speaker 1: the wealthy people are the ones building the shelters or 306 00:17:47,400 --> 00:17:50,000 Speaker 1: what we saw was Zuckerberg's compound and Hawai and things 307 00:17:50,040 --> 00:17:50,360 Speaker 1: like that. 308 00:17:51,359 --> 00:17:55,320 Speaker 2: Yeah, I was a few years back. I was researching 309 00:17:55,400 --> 00:17:59,200 Speaker 2: for a potential documentary on the Sea Stetter movement, and 310 00:17:59,560 --> 00:18:02,840 Speaker 2: you know, it's these it's these this tech elite who 311 00:18:03,040 --> 00:18:07,240 Speaker 2: want to actually move off to like islands, man made 312 00:18:07,320 --> 00:18:09,560 Speaker 2: islands in the ocean, like in the middle of the 313 00:18:09,600 --> 00:18:14,280 Speaker 2: ocean where there's no specific you know, national nation, no 314 00:18:14,440 --> 00:18:17,960 Speaker 2: nation controls, so that they can create their own sort 315 00:18:18,000 --> 00:18:22,000 Speaker 2: of islands and unto themselves and do whatever they want 316 00:18:22,040 --> 00:18:24,280 Speaker 2: to do. I guess it's similar to Musk, like going 317 00:18:24,480 --> 00:18:27,600 Speaker 2: you know, going to Mars, right, But yeah, I think 318 00:18:27,600 --> 00:18:31,000 Speaker 2: there's also a certain paranoia to these people, like they're 319 00:18:31,080 --> 00:18:32,840 Speaker 2: deeply paranoid individuals. 320 00:18:35,960 --> 00:18:38,320 Speaker 1: This episode of The Chuck Podcast is brought to you 321 00:18:38,359 --> 00:18:41,400 Speaker 1: by Quints, and I love me some Quints. New Year, 322 00:18:41,560 --> 00:18:44,680 Speaker 1: Colder Days. This is the moment your winter wardrobe really 323 00:18:44,760 --> 00:18:47,480 Speaker 1: has to deliver. If you're craving a winter reset, start 324 00:18:47,480 --> 00:18:51,080 Speaker 1: with pieces truly made to last season after season. 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I had a source of mind back 352 00:20:24,400 --> 00:20:28,800 Speaker 1: in the Obama era who after they went to Silicon Valley, 353 00:20:29,000 --> 00:20:30,960 Speaker 1: came back and I said, so, what's the difference between 354 00:20:31,000 --> 00:20:34,080 Speaker 1: Washington and Silicon Valley? He says, I thought Washington lived 355 00:20:34,080 --> 00:20:37,080 Speaker 1: in a bubble, he said, the world of Silicon Valley. 356 00:20:37,119 --> 00:20:39,360 Speaker 1: And at the time, he said, it seemed like half 357 00:20:39,400 --> 00:20:42,879 Speaker 1: of Silicon Valley was obsessed with living forever, and that 358 00:20:43,080 --> 00:20:46,919 Speaker 1: this wasn't a small percentage that it was literally because 359 00:20:46,920 --> 00:20:49,880 Speaker 1: they had you know, perhaps it was because they they 360 00:20:49,880 --> 00:20:52,720 Speaker 1: have more money than lifespan, right, And what's the one 361 00:20:52,760 --> 00:20:56,680 Speaker 1: thing that's the hardest to buy is time, And so 362 00:20:56,720 --> 00:21:00,359 Speaker 1: there was this obsession. He said, if a mayor Arkas 363 00:21:00,640 --> 00:21:04,000 Speaker 1: understood the psyche of Silicon Valley, they wouldn't buy any 364 00:21:04,040 --> 00:21:07,440 Speaker 1: more electronics. It would scare the Bejesus out of them. 365 00:21:08,160 --> 00:21:10,560 Speaker 2: Yeah, I don't know if that's necessarily true. 366 00:21:10,640 --> 00:21:17,880 Speaker 1: But yeah, yeah, let's go back to the premise. When 367 00:21:17,920 --> 00:21:21,879 Speaker 1: did you start filming in a way that you knew 368 00:21:22,000 --> 00:21:23,800 Speaker 1: this was going to be the premise of the film 369 00:21:23,920 --> 00:21:26,600 Speaker 1: and that you were going to have to that the 370 00:21:26,720 --> 00:21:29,240 Speaker 1: journey to get Sam Altman was actually going to be 371 00:21:29,280 --> 00:21:33,320 Speaker 1: the documentary versus did you start thinking, well, let's put 372 00:21:33,320 --> 00:21:37,200 Speaker 1: this on tape in case of emergency. Was it always 373 00:21:37,240 --> 00:21:40,399 Speaker 1: in case of emergency or did you know pretty quick 374 00:21:41,240 --> 00:21:43,520 Speaker 1: Altman's never sitting down with me. Maybe I'll get him 375 00:21:43,560 --> 00:21:45,879 Speaker 1: at the very end because of you know, a Keras 376 00:21:45,880 --> 00:21:49,719 Speaker 1: swish or xt or something like that, but ultimately it's 377 00:21:49,760 --> 00:21:51,879 Speaker 1: probably not going to happen. Well, when did you know 378 00:21:52,000 --> 00:21:54,639 Speaker 1: in your brain what you were doing was more than just, 379 00:21:56,760 --> 00:22:00,000 Speaker 1: you know, just putting something in on tape just in case. 380 00:22:01,960 --> 00:22:03,880 Speaker 2: I really thought this was going to be a layup 381 00:22:04,160 --> 00:22:06,080 Speaker 2: getting an interview with him. I didn't think it was 382 00:22:06,119 --> 00:22:08,639 Speaker 2: going to be that difficult. I guess maybe I was 383 00:22:08,800 --> 00:22:12,399 Speaker 2: naive in that, but I've but I've I've gotten some 384 00:22:12,440 --> 00:22:15,520 Speaker 2: pretty difficult interview I mean, I interviewed Juliana Sanche in 385 00:22:15,520 --> 00:22:18,560 Speaker 2: the in the in the embassy, and and. 386 00:22:18,160 --> 00:22:21,960 Speaker 1: And everybody of everybody, right, so. 387 00:22:22,040 --> 00:22:25,680 Speaker 2: I've gotten some I've gotten some really hard interviews because 388 00:22:25,640 --> 00:22:27,800 Speaker 2: I'd never give up, and so I thought this would 389 00:22:27,840 --> 00:22:30,359 Speaker 2: be an easy one. I had, we had friends in common, right, 390 00:22:30,480 --> 00:22:34,760 Speaker 2: like I had a cell phone number, like I didn't 391 00:22:34,760 --> 00:22:36,840 Speaker 2: think they're and then coming off with the success of 392 00:22:36,840 --> 00:22:40,280 Speaker 2: of of my series Telemarketers, I thought that this, this 393 00:22:40,320 --> 00:22:47,439 Speaker 2: would be a perfect opportunity. And strangely, it just didn't happen. 394 00:22:47,520 --> 00:22:50,560 Speaker 2: It never happened. I still haven't even heard from him, 395 00:22:50,600 --> 00:22:53,239 Speaker 2: like even even. 396 00:22:52,680 --> 00:22:58,000 Speaker 1: Even have you heard from people who've heard from him. No, yeah, 397 00:22:58,200 --> 00:22:59,880 Speaker 1: not even a secondhand. 398 00:22:59,720 --> 00:23:03,320 Speaker 2: Not been a secondhand, like you know, not even. 399 00:23:03,119 --> 00:23:05,679 Speaker 1: His lawyer suing you to not do this, Like not 400 00:23:05,720 --> 00:23:07,879 Speaker 1: even not even a legal outreach, huh. 401 00:23:08,080 --> 00:23:15,720 Speaker 2: Not a lawyer, not not a publicist, not nobody, absolutely 402 00:23:15,800 --> 00:23:19,160 Speaker 2: no one connected to him in any way, shape or form. 403 00:23:19,600 --> 00:23:22,480 Speaker 2: I expect though I will at some point, Like this 404 00:23:22,640 --> 00:23:26,080 Speaker 2: movie just came out right and and in theaters in 405 00:23:26,200 --> 00:23:28,960 Speaker 2: New York last week, and then it'll be in d 406 00:23:29,080 --> 00:23:34,720 Speaker 2: C this weekend, so as it as it starts to 407 00:23:34,840 --> 00:23:37,639 Speaker 2: roll out nationwide and gain more steam, I feel like 408 00:23:37,680 --> 00:23:39,720 Speaker 2: at some point he's gonna have to say something, He's 409 00:23:39,720 --> 00:23:43,440 Speaker 2: gonna have to comment on it. We actually called him. 410 00:23:43,760 --> 00:23:45,800 Speaker 2: We called his cell phone during the Q and A 411 00:23:46,480 --> 00:23:50,840 Speaker 2: on Saturday at the at the at the Quad in 412 00:23:50,840 --> 00:23:54,680 Speaker 2: New York, Me and and Benny Saftie called it, called 413 00:23:54,680 --> 00:23:57,280 Speaker 2: his cell phone and left a message. So he got them. 414 00:23:57,359 --> 00:23:59,359 Speaker 2: He got the message that the movie is premiering, and 415 00:23:59,400 --> 00:24:02,639 Speaker 2: I invited him to the screening in LA so we'll see. 416 00:24:02,840 --> 00:24:04,240 Speaker 2: I don't think he's going to show. 417 00:24:04,800 --> 00:24:08,920 Speaker 1: What what's interesting here and what to me is different 418 00:24:09,000 --> 00:24:11,600 Speaker 1: from Roger and Me? Is that again? I go back 419 00:24:11,640 --> 00:24:17,040 Speaker 1: to look, I think you're definitely a skeptic coming into this, 420 00:24:17,600 --> 00:24:20,720 Speaker 1: but you I don't feel like you have a score 421 00:24:20,760 --> 00:24:23,160 Speaker 1: to settle, Like did you have any history? I mean, 422 00:24:23,720 --> 00:24:25,840 Speaker 1: am I missing something here? I'm just trying to figure 423 00:24:25,840 --> 00:24:28,160 Speaker 1: out how what made you so threatening? 424 00:24:29,080 --> 00:24:31,879 Speaker 2: Right? That's the key difference I think, you know, with 425 00:24:32,000 --> 00:24:35,840 Speaker 2: Roger and Me, Michael Moore had a score to settle 426 00:24:35,840 --> 00:24:38,560 Speaker 2: in terms of like and him and his friends were 427 00:24:38,560 --> 00:24:39,920 Speaker 2: all fired, right. 428 00:24:40,320 --> 00:24:42,399 Speaker 1: And in a weird way. I could see the PR 429 00:24:42,440 --> 00:24:44,600 Speaker 1: team going, oh man, just no, there's no good that 430 00:24:44,680 --> 00:24:47,560 Speaker 1: comes from that. Like I could picture the core way 431 00:24:47,600 --> 00:24:50,520 Speaker 1: corporate communications work, the way legal counsels work, you know, 432 00:24:51,080 --> 00:24:54,880 Speaker 1: you know, even if Roger Smith wanted to have talking no, no, no, right, 433 00:24:55,000 --> 00:24:59,359 Speaker 1: don't do it like this a company that's desperate to 434 00:24:59,400 --> 00:25:04,359 Speaker 1: say not here to know, we're not scary, we're we're okay. 435 00:25:05,880 --> 00:25:11,840 Speaker 1: He has done some semi revealing interviews already. So it 436 00:25:11,920 --> 00:25:16,040 Speaker 1: was a strange. It's just strange to me that you 437 00:25:16,080 --> 00:25:16,760 Speaker 1: struggled with this. 438 00:25:17,600 --> 00:25:24,000 Speaker 2: Yeah. Yeah, for me, it was very different from from 439 00:25:24,000 --> 00:25:26,639 Speaker 2: Michael Moore. And and to your point, it seems like 440 00:25:26,680 --> 00:25:30,560 Speaker 2: he would want to he would want to answer questions like, hey, 441 00:25:31,440 --> 00:25:35,359 Speaker 2: what how will this affect my son when he grows up? Hey, 442 00:25:35,720 --> 00:25:39,040 Speaker 2: how how will this affect me as as a filmmaker? 443 00:25:39,400 --> 00:25:43,359 Speaker 2: And you have this like interest in Hollywood all of 444 00:25:43,359 --> 00:25:45,560 Speaker 2: a sudden, like he was sending his people to his 445 00:25:45,680 --> 00:25:48,760 Speaker 2: minions down from Sora to meet with a lot of 446 00:25:48,800 --> 00:25:52,680 Speaker 2: my filmmaking friends and try to court them right, And 447 00:25:52,720 --> 00:25:55,280 Speaker 2: a lot of my director friends were like having these 448 00:25:55,280 --> 00:25:58,560 Speaker 2: like lunch meetings with Sora where they were like, oh, 449 00:25:58,640 --> 00:26:01,479 Speaker 2: we want you to use our technic, you know, And 450 00:26:01,480 --> 00:26:04,679 Speaker 2: of course everybody was like, you know, screw your technology, 451 00:26:04,720 --> 00:26:07,920 Speaker 2: like we want nothing to do with this with this technology. 452 00:26:08,680 --> 00:26:12,919 Speaker 2: But it does seem odd like what and I always wondered, 453 00:26:12,960 --> 00:26:16,320 Speaker 2: like what does he have to hide. I don't think 454 00:26:16,440 --> 00:26:19,480 Speaker 2: any of the interviews he did were because I've seen 455 00:26:19,520 --> 00:26:21,159 Speaker 2: pretty much all of them. I don't think any of 456 00:26:21,160 --> 00:26:23,560 Speaker 2: them were very revealing at all. I don't think he 457 00:26:23,840 --> 00:26:27,880 Speaker 2: answers questions from the heart. I think everything is completely 458 00:26:28,600 --> 00:26:32,240 Speaker 2: pr manipulated, and I and I and I guarantee you 459 00:26:32,680 --> 00:26:36,720 Speaker 2: he already has an answer for the question about my movie, 460 00:26:37,040 --> 00:26:39,560 Speaker 2: like he's already been trained on what to say about that. 461 00:26:40,280 --> 00:26:43,199 Speaker 2: The other funny thing is, uh, his We have this 462 00:26:43,280 --> 00:26:45,120 Speaker 2: friend in common who gave me a cell phone number 463 00:26:45,119 --> 00:26:49,600 Speaker 2: who will remain nameless, told me he has AI read 464 00:26:49,720 --> 00:26:52,720 Speaker 2: all his emails and text messages and summarize them back 465 00:26:52,720 --> 00:26:57,520 Speaker 2: to him. He never he doesn't actually read any of them. 466 00:26:57,640 --> 00:27:00,680 Speaker 1: Well, that's just a strange, you know. And I think 467 00:27:00,400 --> 00:27:04,439 Speaker 1: this gets back to who's building these technologies, and what 468 00:27:04,680 --> 00:27:08,479 Speaker 1: is their life experience and how many you know. One 469 00:27:08,520 --> 00:27:11,800 Speaker 1: of my favorite Kerash swisherisms where she gets she can 470 00:27:11,840 --> 00:27:14,640 Speaker 1: be a little snarky, shockingly and I say this with love, 471 00:27:15,080 --> 00:27:18,879 Speaker 1: she's a good friend, is when she sort of implied, 472 00:27:19,040 --> 00:27:22,040 Speaker 1: you know, Mark Zuckerberg, you know, nineteen year old at 473 00:27:22,040 --> 00:27:24,639 Speaker 1: Harvard didn't know how to talk to girls in person, 474 00:27:24,680 --> 00:27:27,440 Speaker 1: So he came up with a hack to try to 475 00:27:27,560 --> 00:27:32,479 Speaker 1: get a date. YadA, YadA, YadA. We had Facebook, you know, 476 00:27:34,040 --> 00:27:39,720 Speaker 1: and so many of these sort of tech developments have 477 00:27:40,000 --> 00:27:45,040 Speaker 1: come to fruition based on, you know, a desire. Hey, 478 00:27:45,080 --> 00:27:48,280 Speaker 1: these were lonely kids, so they wanted to create artificial friends, 479 00:27:48,480 --> 00:27:52,359 Speaker 1: right like, rather than thinking how does the average person 480 00:27:52,840 --> 00:27:56,280 Speaker 1: live life? And you often wonder with some of these 481 00:27:56,280 --> 00:28:02,080 Speaker 1: tech bros, are they fixing problems that were more unique 482 00:28:02,119 --> 00:28:08,080 Speaker 1: because they were their problems, not are not society's problems, right? 483 00:28:08,640 --> 00:28:13,080 Speaker 2: I mean, I think I think that's a that that's 484 00:28:13,080 --> 00:28:17,040 Speaker 2: a minority of them. Sadly, I think most of them 485 00:28:17,080 --> 00:28:20,040 Speaker 2: are just trying to get rich. And I've I've talked 486 00:28:20,080 --> 00:28:23,640 Speaker 2: to plenty of them who were or even people who 487 00:28:23,680 --> 00:28:27,159 Speaker 2: worked for certain tech titans, who were just working on 488 00:28:27,240 --> 00:28:31,040 Speaker 2: products that they knew had no reason to even exist 489 00:28:31,080 --> 00:28:33,520 Speaker 2: in the world other than to just get some get 490 00:28:33,560 --> 00:28:34,480 Speaker 2: a few people rich. 491 00:28:37,760 --> 00:28:41,920 Speaker 1: Yeah, I mean social media. I think the initial idea 492 00:28:41,960 --> 00:28:45,160 Speaker 1: of social media wasn't about money making, and then it 493 00:28:45,240 --> 00:28:49,640 Speaker 1: became a perverted money making operation and obviously it's now 494 00:28:49,640 --> 00:28:51,120 Speaker 1: the mess that it is today. 495 00:28:51,600 --> 00:28:56,200 Speaker 2: Right social media. You know, I've had these discussions recently 496 00:28:57,520 --> 00:29:01,959 Speaker 2: similar discussions about AI that that we have about social media. 497 00:29:02,880 --> 00:29:05,360 Speaker 2: There was a woman who came to one of my 498 00:29:05,440 --> 00:29:09,560 Speaker 2: screenings who runs an organization I wish I could remember 499 00:29:09,560 --> 00:29:11,320 Speaker 2: the name now, but it's a it's like sort of 500 00:29:11,360 --> 00:29:15,000 Speaker 2: like mothers, like Mother's against drunk driving, but it's like 501 00:29:15,080 --> 00:29:16,360 Speaker 2: mothers against social media. 502 00:29:16,560 --> 00:29:19,200 Speaker 1: Yeah, no, no, no, no, it's called Mama. I interviewed her 503 00:29:19,960 --> 00:29:22,960 Speaker 1: Mother's against Media Addiction. I think she calls it, Yes, 504 00:29:23,000 --> 00:29:23,720 Speaker 1: I'm not mistaken. 505 00:29:23,920 --> 00:29:28,680 Speaker 2: So she came to my screening, loved the film Lovely 506 00:29:28,720 --> 00:29:32,959 Speaker 2: person and and I and I and I I so 507 00:29:33,000 --> 00:29:36,360 Speaker 2: I respect what her mission I have. I have a son, 508 00:29:36,600 --> 00:29:39,040 Speaker 2: and and you know, I want the best for him. 509 00:29:40,280 --> 00:29:47,120 Speaker 2: But I also I'm wary about wholesale like uh, wiping 510 00:29:47,160 --> 00:29:49,520 Speaker 2: these these technologies off the face of the earth, because 511 00:29:49,520 --> 00:29:53,280 Speaker 2: then I'm also always reminded of social media, and I'm 512 00:29:53,280 --> 00:29:59,920 Speaker 2: always reminded of the Arab Spring, and we quickly forget that. Yes, 513 00:30:00,200 --> 00:30:04,840 Speaker 2: these things can cause problems and trauma on you know, 514 00:30:05,960 --> 00:30:09,400 Speaker 2: privileged kids in the first world, but they can also 515 00:30:09,600 --> 00:30:18,719 Speaker 2: completely change like the entire trajectory of a country overseas. 516 00:30:19,160 --> 00:30:21,640 Speaker 2: And so it's it's. 517 00:30:21,480 --> 00:30:23,680 Speaker 1: Abou Yeah, but what was The real lesson of the 518 00:30:23,680 --> 00:30:28,040 Speaker 1: Arab Spring is that totalitarian countries realized, oh, screw this, 519 00:30:28,200 --> 00:30:31,120 Speaker 1: We're going to weaponize it. And so now in some 520 00:30:31,200 --> 00:30:33,640 Speaker 1: cases I take your point there, and I think, look, 521 00:30:33,960 --> 00:30:36,960 Speaker 1: we all bought. I bought the good of the Arab 522 00:30:37,000 --> 00:30:39,280 Speaker 1: Spring at the time, you thought, Okay, this is what 523 00:30:39,320 --> 00:30:43,720 Speaker 1: it was about. This really is democratizing the ability to 524 00:30:43,760 --> 00:30:47,840 Speaker 1: speak out and democratizing all of this, and then governments 525 00:30:47,880 --> 00:30:50,400 Speaker 1: figured out how to weaponize it, and then we got 526 00:30:50,760 --> 00:30:52,920 Speaker 1: what the world we live in right now, you know, 527 00:30:53,120 --> 00:30:56,720 Speaker 1: arguably between twenty sixteen and today. 528 00:30:58,000 --> 00:31:02,960 Speaker 2: Sure it know. In regards to AI, this is another 529 00:31:03,000 --> 00:31:06,280 Speaker 2: thing that I that that I think I spoke about 530 00:31:06,320 --> 00:31:09,320 Speaker 2: recently at at at a q and a was. I 531 00:31:09,360 --> 00:31:14,920 Speaker 2: read this article about this, this young female documentary filmmaker 532 00:31:14,920 --> 00:31:19,960 Speaker 2: in China who was using AI to tell her stories 533 00:31:21,200 --> 00:31:25,760 Speaker 2: about the government and about life there that she would 534 00:31:26,560 --> 00:31:31,160 Speaker 2: no never be able to tell uh without because for 535 00:31:31,240 --> 00:31:34,680 Speaker 2: various reasons, obviously because she'd be arrested or because of 536 00:31:34,680 --> 00:31:36,840 Speaker 2: the government, but also because she didn't have the resources, 537 00:31:37,000 --> 00:31:41,200 Speaker 2: a camera or anything to actually film. So there there 538 00:31:41,280 --> 00:31:44,360 Speaker 2: will be I think benefits, There will be interesting things 539 00:31:44,360 --> 00:31:48,600 Speaker 2: that come out of this. And again I'm I'm all 540 00:31:48,640 --> 00:31:53,560 Speaker 2: for for for strong regulation, but I'm not for just 541 00:31:53,600 --> 00:31:58,480 Speaker 2: like wholesale discounting a technology. And so it's it was 542 00:31:58,480 --> 00:32:02,040 Speaker 2: an interesting it was interesting talking to her about AI, 543 00:32:02,680 --> 00:32:04,800 Speaker 2: and I feel like her viewpoint on AI was very 544 00:32:04,920 --> 00:32:09,880 Speaker 2: much like, we need to shut this effing thing down completely. 545 00:32:10,400 --> 00:32:12,479 Speaker 2: And a few people have come to me with that 546 00:32:12,560 --> 00:32:14,840 Speaker 2: at Q and A's and been a little bit upset 547 00:32:14,880 --> 00:32:18,680 Speaker 2: with me because I'm not that person. I'm not saying that. 548 00:32:18,800 --> 00:32:21,000 Speaker 1: How would we just erase the technology? I just don't 549 00:32:21,040 --> 00:32:24,480 Speaker 1: buy When have we ever done that in the history 550 00:32:24,480 --> 00:32:25,160 Speaker 1: of civilization? 551 00:32:26,120 --> 00:32:29,880 Speaker 2: Right, I mean, it's it's largely impossible. 552 00:32:30,600 --> 00:32:34,120 Speaker 1: Let's let's blow up the Gutenberg, you know, let's get 553 00:32:34,200 --> 00:32:37,240 Speaker 1: rid of the printing press. Yeah, this is causing educating 554 00:32:37,280 --> 00:32:40,920 Speaker 1: too many people, the serfs what too many are spreading 555 00:32:40,920 --> 00:32:42,000 Speaker 1: too many ideas. 556 00:32:43,040 --> 00:32:45,440 Speaker 2: So on the flip side, the reason sam Almon has 557 00:32:45,480 --> 00:32:51,360 Speaker 2: become so powerful is the is the one of the argument, 558 00:32:51,440 --> 00:32:53,160 Speaker 2: and I'm now I'm going to argue against myself. 559 00:32:53,320 --> 00:32:53,480 Speaker 1: Right. 560 00:32:54,680 --> 00:32:57,280 Speaker 2: One of his things is this fear based tactic where 561 00:32:57,320 --> 00:32:59,479 Speaker 2: he came to d C and he met with all 562 00:32:59,520 --> 00:33:01,640 Speaker 2: the general and he met with all he met with 563 00:33:01,680 --> 00:33:03,840 Speaker 2: all the weapons people and he said, look, if China 564 00:33:03,920 --> 00:33:07,560 Speaker 2: beats us at this, we're dead. So he put the 565 00:33:07,560 --> 00:33:10,680 Speaker 2: fear of God and all these people that we in 566 00:33:10,800 --> 00:33:13,880 Speaker 2: DC that we need to start spending money, money, money, money, 567 00:33:13,880 --> 00:33:16,320 Speaker 2: because we have to beat China, because it's like some 568 00:33:16,400 --> 00:33:18,680 Speaker 2: crazy arms race that like. 569 00:33:19,400 --> 00:33:21,479 Speaker 1: Well, how do you why did we get why did 570 00:33:21,480 --> 00:33:22,080 Speaker 1: we go to the moon? 571 00:33:23,560 --> 00:33:25,680 Speaker 2: I mean, did we go to the moon? It's just kidding, 572 00:33:25,680 --> 00:33:28,080 Speaker 2: but yeah, yeah, exactly. It was to beat the Soviets. 573 00:33:28,200 --> 00:33:31,280 Speaker 2: If there's no Cold War, we don't go to the moon. Yeah. 574 00:33:31,520 --> 00:33:35,200 Speaker 2: And that's why altman's so smart too, because he completely 575 00:33:35,240 --> 00:33:37,280 Speaker 2: sees that, and he's using that playbook now to make 576 00:33:37,360 --> 00:33:41,600 Speaker 2: himself incredibly rich. You know, Opening Eye was a nonprofit 577 00:33:41,640 --> 00:33:46,120 Speaker 2: when I started this film. Halfway through production, overnight he 578 00:33:46,160 --> 00:33:48,240 Speaker 2: created he turned it into a for profit company and 579 00:33:48,320 --> 00:33:52,160 Speaker 2: made himself what was it, seven or nine billion dollars overnight. Right, 580 00:33:52,560 --> 00:33:54,240 Speaker 2: Like he's just he's a super villain. 581 00:33:56,480 --> 00:34:00,000 Speaker 1: That's an interesting Are you sure he's a bad act? 582 00:34:01,680 --> 00:34:03,880 Speaker 2: I am? I am now. I wasn't when I went 583 00:34:03,920 --> 00:34:05,800 Speaker 2: into this, and I wanted to give him the benefit 584 00:34:05,800 --> 00:34:09,080 Speaker 2: of the doubt. But every move that he's made since 585 00:34:09,120 --> 00:34:13,520 Speaker 2: then has shown me that he is a Marvel level 586 00:34:13,680 --> 00:34:14,440 Speaker 2: super villain. 587 00:34:15,080 --> 00:34:19,320 Speaker 1: Hm. Wow, that's a I mean, I look the picture 588 00:34:19,320 --> 00:34:23,879 Speaker 1: you're painting. I I get it. I understand where you're 589 00:34:23,880 --> 00:34:29,080 Speaker 1: coming from. I mean, I guess Sam Altman or Elon Musk. 590 00:34:29,640 --> 00:34:32,960 Speaker 1: Which one is your is more marvel like in their 591 00:34:33,040 --> 00:34:34,080 Speaker 1: villain villain is. 592 00:34:34,680 --> 00:34:38,640 Speaker 2: Oh, Elon easily, and mainly because Elon at least has 593 00:34:38,680 --> 00:34:41,280 Speaker 2: a personality, right He's. 594 00:34:41,560 --> 00:34:43,839 Speaker 1: I joke that if you made the Bond film and 595 00:34:43,880 --> 00:34:45,920 Speaker 1: you said the villain was named the Bond villain was 596 00:34:46,000 --> 00:34:48,839 Speaker 1: named Elon Musk, You're like, you wouldn't even change the name. 597 00:34:48,880 --> 00:34:50,279 Speaker 1: You're like, oh, that's a perfect name. 598 00:34:51,360 --> 00:34:54,200 Speaker 2: Yeah. No, he I mean he's got he see, al 599 00:34:54,239 --> 00:34:57,800 Speaker 2: Ben doesn't really have a personality. Elon's is a weird 600 00:34:57,840 --> 00:35:01,279 Speaker 2: guy who names his kids X three, Jay five and 601 00:35:01,480 --> 00:35:05,480 Speaker 2: like you know, uh, wants to go to Mars and 602 00:35:05,320 --> 00:35:09,960 Speaker 2: is and is and spouts off out the craziest stuff 603 00:35:10,000 --> 00:35:13,400 Speaker 2: you could possibly think of. Allman is very like even keeled. 604 00:35:13,880 --> 00:35:16,040 Speaker 2: Doesn't make him any any less of a villain though, 605 00:35:16,080 --> 00:35:20,280 Speaker 2: but he's but he's less interesting in terms of Marvel characteristics. 606 00:35:20,880 --> 00:35:25,960 Speaker 1: Does he scare you more than the folks at Google? Oh? 607 00:35:26,040 --> 00:35:30,759 Speaker 2: Yeah, definitely, But maybe that's Google's what that's going to be, right, 608 00:35:30,920 --> 00:35:35,239 Speaker 2: I mean Google is now right, Gemini is overtaken chat 609 00:35:35,280 --> 00:35:38,120 Speaker 2: TPT and some of the measurement scores, and you know, 610 00:35:38,480 --> 00:35:41,399 Speaker 2: Google is reminding open ai of what it's like when 611 00:35:41,440 --> 00:35:42,759 Speaker 2: you have. 612 00:35:42,840 --> 00:35:46,040 Speaker 1: All the resources that Google has, when they change, when 613 00:35:46,080 --> 00:35:49,040 Speaker 1: when they sort of focus their efforts. Uh, they've made 614 00:35:49,040 --> 00:35:51,360 Speaker 1: some big leaps and bounds, and they have you know, 615 00:35:51,440 --> 00:35:54,480 Speaker 1: all these Android phones running around already that they can 616 00:35:54,640 --> 00:35:58,080 Speaker 1: immediately sort of embed themselves in. Now Apple got to 617 00:35:58,080 --> 00:36:00,480 Speaker 1: deal with Google. I mean, you know, so I could 618 00:36:00,600 --> 00:36:03,319 Speaker 1: argue that that this has been a bad year for 619 00:36:03,440 --> 00:36:07,320 Speaker 1: Sam Altman because of how Google is in some ways 620 00:36:09,239 --> 00:36:10,680 Speaker 1: caught up to open AI. 621 00:36:12,239 --> 00:36:16,440 Speaker 2: So it's an interesting point. And I would also I'll 622 00:36:16,480 --> 00:36:19,720 Speaker 2: tell you going into this film, I took a chance 623 00:36:19,719 --> 00:36:24,799 Speaker 2: on Sam Altman, right, like he he could completely disappear 624 00:36:24,840 --> 00:36:28,080 Speaker 2: off the face of the earth by the end of 625 00:36:28,120 --> 00:36:30,440 Speaker 2: this year and be sort of like a flash in 626 00:36:30,480 --> 00:36:35,040 Speaker 2: the pan that nobody remembers his name, or he could 627 00:36:35,040 --> 00:36:38,640 Speaker 2: be the next Steve Jobs. Making this movie, I took 628 00:36:38,680 --> 00:36:41,560 Speaker 2: a chance on him that he would be like a 629 00:36:41,640 --> 00:36:47,040 Speaker 2: Steve Jobs type figure in the future, because obviously, you know, 630 00:36:47,120 --> 00:36:50,200 Speaker 2: I didn't make this movie for right now. I also 631 00:36:50,239 --> 00:36:52,400 Speaker 2: made it for the future. I want I wanted this, 632 00:36:52,400 --> 00:36:56,120 Speaker 2: this movie to be still watched. And all filmmakers do, right, 633 00:36:56,280 --> 00:36:58,759 Speaker 2: We all want our movies to watched five ten years 634 00:36:58,800 --> 00:37:02,400 Speaker 2: down the road. So I so in that regard, I 635 00:37:02,440 --> 00:37:04,880 Speaker 2: agree with you, Like I don't know, I don't, I 636 00:37:04,920 --> 00:37:07,520 Speaker 2: don't totally you know, but I I know that I gamble. 637 00:37:07,640 --> 00:37:12,360 Speaker 2: I'm gambling on him in a way that and gambling 638 00:37:12,360 --> 00:37:15,239 Speaker 2: on open Ai that it will beat Gemini, that it 639 00:37:15,280 --> 00:37:18,200 Speaker 2: will that it will beat Anthropic and be be the 640 00:37:18,280 --> 00:37:21,160 Speaker 2: number one, be the Apple, because that will just make 641 00:37:21,200 --> 00:37:22,879 Speaker 2: my movie more relevant. Right. 642 00:37:24,400 --> 00:37:26,640 Speaker 1: But taking that away, I mean, are you just gambling 643 00:37:26,680 --> 00:37:29,960 Speaker 1: on him or or you just believe that because everything 644 00:37:30,000 --> 00:37:32,480 Speaker 1: you've learned? I mean, do you think he is just unique? 645 00:37:32,920 --> 00:37:36,080 Speaker 1: Like Jobs? Jobs had a uniqueness to him, right? And 646 00:37:36,160 --> 00:37:38,600 Speaker 1: you know in some ways that the Walter Isaacs and 647 00:37:39,400 --> 00:37:43,840 Speaker 1: biography sort of you know, painted a picture. It's like, okay, 648 00:37:43,880 --> 00:37:47,080 Speaker 1: he was he was so obsessed with what he was 649 00:37:47,120 --> 00:37:49,799 Speaker 1: doing that you know, wasn't the best father and wasn't 650 00:37:49,840 --> 00:37:52,719 Speaker 1: the best this, and wasn't the best that is is 651 00:37:52,719 --> 00:37:55,000 Speaker 1: is there a drive and altment that you think is 652 00:37:55,040 --> 00:37:56,920 Speaker 1: just superior to whatever Google can do. 653 00:37:58,960 --> 00:38:01,320 Speaker 2: Again, I don't know too much much about the people 654 00:38:01,360 --> 00:38:04,680 Speaker 2: that run Google, but I do know a lot about 655 00:38:04,719 --> 00:38:08,160 Speaker 2: Steve Jobs. I mean he as young as a younger 656 00:38:08,520 --> 00:38:10,759 Speaker 2: college student that he was a hero of mine. I 657 00:38:10,800 --> 00:38:15,040 Speaker 2: read Walter Risey's an incredible book seeing all the movies 658 00:38:15,080 --> 00:38:18,800 Speaker 2: about jobs, and there's a similar thing running through Altman 659 00:38:18,880 --> 00:38:23,720 Speaker 2: in Jobs. For sure, there's a similar drive that they 660 00:38:23,920 --> 00:38:28,439 Speaker 2: just don't care who gets hurt along the way. They're 661 00:38:28,520 --> 00:38:32,520 Speaker 2: just going to plow forward and take over the world. 662 00:38:35,480 --> 00:38:39,160 Speaker 1: Having good life insurance is incredibly important. I know from 663 00:38:39,160 --> 00:38:42,239 Speaker 1: personal experience. I was sixteen when my father passed away. 664 00:38:42,280 --> 00:38:44,680 Speaker 1: We didn't have any money. He didn't leave us in 665 00:38:44,719 --> 00:38:49,080 Speaker 1: the best shape. My mother, single mother, now widow. 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Did you ever talk 693 00:40:19,000 --> 00:40:20,400 Speaker 1: to Wosnick about jobs? 694 00:40:20,880 --> 00:40:23,000 Speaker 2: Have you ever met with those loves to I'm also 695 00:40:23,080 --> 00:40:25,399 Speaker 2: a huge fan of Was. I would love I would 696 00:40:25,440 --> 00:40:27,600 Speaker 2: love to talk to him one day, because. 697 00:40:27,400 --> 00:40:29,279 Speaker 1: You know, there's always been these different theories like what 698 00:40:29,280 --> 00:40:32,000 Speaker 1: what's an alternative like in the in an alternative universe? 699 00:40:32,040 --> 00:40:34,759 Speaker 1: What if it? What if jobs? You know, when he 700 00:40:34,840 --> 00:40:37,560 Speaker 1: leaves Apple, wosnick Is sort of takes over and he's 701 00:40:37,680 --> 00:40:41,960 Speaker 1: the you know, what would Apple be the same? Would 702 00:40:42,000 --> 00:40:44,640 Speaker 1: it have been? You know, it's what it's an unanswerable 703 00:40:44,680 --> 00:40:47,320 Speaker 1: question now. But Woznick was always seen as sort of 704 00:40:47,360 --> 00:40:50,600 Speaker 1: the moral center for jobs, right, he was sort of 705 00:40:50,600 --> 00:40:53,640 Speaker 1: the ethical that he would and I think even you know, 706 00:40:53,719 --> 00:40:57,040 Speaker 1: and he wasn't It didn't seem like wosny CK's motivation 707 00:40:57,239 --> 00:40:57,960 Speaker 1: was financial. 708 00:41:00,120 --> 00:41:03,080 Speaker 2: No, I mean I think I think it wouldn't have 709 00:41:03,080 --> 00:41:05,160 Speaker 2: been the same. And I also I don't think it 710 00:41:05,200 --> 00:41:08,600 Speaker 2: would have been as successful because just from what I've read, 711 00:41:10,000 --> 00:41:12,440 Speaker 2: Wa was was just too nice of a guy. 712 00:41:12,680 --> 00:41:16,719 Speaker 1: Right, So, which, of course, is these dilemmas when you're 713 00:41:16,760 --> 00:41:19,719 Speaker 1: when you when we deal with these supposed you know, 714 00:41:19,840 --> 00:41:25,160 Speaker 1: are we supposed to accept that the only successful disruptors 715 00:41:25,600 --> 00:41:29,920 Speaker 1: to develop technologies that are amazing, you know, have to 716 00:41:29,920 --> 00:41:30,600 Speaker 1: be assholes? 717 00:41:32,320 --> 00:41:34,320 Speaker 2: I don't think we have to accept it. I think 718 00:41:34,360 --> 00:41:39,239 Speaker 2: that we for some reason as a society, we we 719 00:41:39,360 --> 00:41:41,200 Speaker 2: not only do we let it happen, but but like 720 00:41:41,280 --> 00:41:45,439 Speaker 2: we we we perpetuate that, like we prop that up. 721 00:41:46,920 --> 00:41:50,320 Speaker 2: I don't think they have to be I don't. I 722 00:41:50,640 --> 00:41:53,319 Speaker 2: really don't. I think I think nice guys can can 723 00:41:53,440 --> 00:41:55,920 Speaker 2: get ahead. But as a society, this is, this is 724 00:41:55,960 --> 00:41:58,960 Speaker 2: what we've I think we're taught even growing up. 725 00:42:00,480 --> 00:42:04,320 Speaker 1: Especially the great CEOs. You know, they're they're they're ruthless, 726 00:42:04,600 --> 00:42:06,719 Speaker 1: and you have to be ruthless to succeed. And then 727 00:42:06,760 --> 00:42:09,160 Speaker 1: I want to say, have you met the CEO of Costco. 728 00:42:11,719 --> 00:42:14,560 Speaker 1: You know, he's a pretty successful dude, and his employees 729 00:42:14,600 --> 00:42:17,719 Speaker 1: love him because he pays her employees pretty well, and 730 00:42:17,800 --> 00:42:21,440 Speaker 1: he's you know, he's different than the average average bottom 731 00:42:21,440 --> 00:42:22,000 Speaker 1: line ceo. 732 00:42:22,680 --> 00:42:25,239 Speaker 2: Yeah, no, it's it's it's a narrative that we buy 733 00:42:25,280 --> 00:42:27,840 Speaker 2: into and that we teach our kids. And I was 734 00:42:27,960 --> 00:42:30,480 Speaker 2: believe me, I was taught at and it took me 735 00:42:30,520 --> 00:42:35,000 Speaker 2: a while, And it took me. It took me following 736 00:42:36,480 --> 00:42:41,360 Speaker 2: shadowing certain directors that were really genuinely good, nice people 737 00:42:41,400 --> 00:42:44,640 Speaker 2: like Jim Jarmish to realize you don't have to be 738 00:42:44,680 --> 00:42:45,920 Speaker 2: an asshole director. 739 00:42:46,719 --> 00:42:49,239 Speaker 1: Well, by the way, I'm glad you just brought that up, right. 740 00:42:49,280 --> 00:42:54,000 Speaker 1: Hollywood's riddled with stories about you know, the asshole director 741 00:42:54,480 --> 00:42:57,840 Speaker 1: that made the great movies versus you know, and and 742 00:42:59,400 --> 00:43:01,640 Speaker 1: is that even tolerated behavior anymore? 743 00:43:02,239 --> 00:43:05,600 Speaker 2: It's not tolerated anymore. We we we really did away 744 00:43:05,640 --> 00:43:07,000 Speaker 2: with it during. 745 00:43:07,480 --> 00:43:10,840 Speaker 1: The Harvey basically me too in Harvey. 746 00:43:11,160 --> 00:43:15,400 Speaker 2: Yeah, it was Harvey. Basically it was definitely tolerated. I 747 00:43:15,480 --> 00:43:17,960 Speaker 2: witnessed a lot of it at the beginning, you know, 748 00:43:17,960 --> 00:43:20,440 Speaker 2: because I've been doing this for twenty five years. Like 749 00:43:21,360 --> 00:43:26,239 Speaker 2: I knew, I knew Brett Rattner, for instance, Like I 750 00:43:26,280 --> 00:43:30,680 Speaker 2: saw him do terrible, horrible people, horrible things to people. 751 00:43:31,280 --> 00:43:34,480 Speaker 2: But then on the flip side, you know, I worked 752 00:43:34,480 --> 00:43:37,880 Speaker 2: with alongside people like Jim Jarmish who couldn't couldn't have 753 00:43:37,880 --> 00:43:41,680 Speaker 2: been the most complete opposite of a Brett Rattner. But 754 00:43:41,719 --> 00:43:47,000 Speaker 2: we've largely like disposed of those people. The scary thing 755 00:43:47,080 --> 00:43:49,799 Speaker 2: is I see them like starting to slowly creep back in. 756 00:43:50,640 --> 00:43:53,239 Speaker 2: So I think we I think I think we need 757 00:43:53,239 --> 00:43:56,719 Speaker 2: to like, you know, keep keep up, keep on our tone. 758 00:43:56,760 --> 00:43:58,880 Speaker 1: Well, I mean this is where political leaders may have 759 00:43:58,920 --> 00:44:03,440 Speaker 1: offered a permission and selg or bad behavior again exactly exactly. 760 00:44:03,480 --> 00:44:06,920 Speaker 2: I mean, that's part of it. And unfortunately, like younger 761 00:44:06,960 --> 00:44:12,160 Speaker 2: generations pick up on that, and you know, bad behavior 762 00:44:12,239 --> 00:44:18,320 Speaker 2: starts to become in vogue. Again, let me go back. 763 00:44:18,160 --> 00:44:23,520 Speaker 1: To the to the movie. What do you want people 764 00:44:23,560 --> 00:44:24,160 Speaker 1: to take away? 765 00:44:25,440 --> 00:44:29,640 Speaker 2: So to me, there's a lot of anxiety around AI 766 00:44:31,600 --> 00:44:34,920 Speaker 2: and you know, people are worried. People are worried, is 767 00:44:34,920 --> 00:44:38,319 Speaker 2: it's going to take my job? Is is this what's 768 00:44:38,360 --> 00:44:40,360 Speaker 2: going to happen to my to my kids, what's going 769 00:44:40,400 --> 00:44:44,080 Speaker 2: to happen to humans in general? Like, there's there's a 770 00:44:44,120 --> 00:44:49,000 Speaker 2: lot of talk, right, and I don't my movie doesn't 771 00:44:49,040 --> 00:44:53,360 Speaker 2: have any easy answers. But what I would like people 772 00:44:53,360 --> 00:44:54,919 Speaker 2: to take away. And part of the reason why I'm 773 00:44:54,960 --> 00:44:57,120 Speaker 2: like going around the country and like doing Q and 774 00:44:57,160 --> 00:45:00,000 Speaker 2: a's and talking about this film is because I think 775 00:45:00,000 --> 00:45:03,279 Speaker 2: think one of the best ways to cure anxiety is 776 00:45:03,400 --> 00:45:06,719 Speaker 2: just to talk about it, right, And so if I 777 00:45:06,760 --> 00:45:13,040 Speaker 2: could start conversation and get people talking, that's that's all. 778 00:45:13,200 --> 00:45:16,279 Speaker 2: That's all I want to achieve. Really, that's that's the 779 00:45:16,560 --> 00:45:19,440 Speaker 2: that's that's the least that I could really do to 780 00:45:19,440 --> 00:45:22,680 Speaker 2: to just help people, you know, and then entertain them 781 00:45:22,680 --> 00:45:24,799 Speaker 2: at the same time. It is a comedy. I want 782 00:45:24,800 --> 00:45:26,440 Speaker 2: people to laugh, I want people to like. 783 00:45:26,760 --> 00:45:29,440 Speaker 1: I'm glad you said that because I felt this was 784 00:45:29,560 --> 00:45:36,080 Speaker 1: not satire, but you were trying to You weren't trying 785 00:45:36,120 --> 00:45:39,320 Speaker 1: to be boring, that was for sure. And you weren't 786 00:45:40,160 --> 00:45:41,440 Speaker 1: and you weren't trying to lecture. 787 00:45:42,160 --> 00:45:45,160 Speaker 2: Can I put that on the poster, go for it, 788 00:45:45,160 --> 00:45:47,880 Speaker 2: that he wasn't trying to be boring Chuck tub right. 789 00:45:48,680 --> 00:45:51,560 Speaker 1: Uh. But and you clearly weren't trying to lecture, right, yeah. 790 00:45:51,640 --> 00:45:53,480 Speaker 1: And that's why I was curious. What what did you 791 00:45:53,520 --> 00:45:55,920 Speaker 1: hope to take away? I mean, you know, I I 792 00:45:55,960 --> 00:45:58,120 Speaker 1: thought it was very clever some of the things you did, 793 00:45:58,160 --> 00:46:00,399 Speaker 1: which is you know, part of the process of making 794 00:46:00,440 --> 00:46:04,080 Speaker 1: the film and searching for Sam Altman, and is also 795 00:46:04,880 --> 00:46:07,400 Speaker 1: you went around and figured out, okay, what's the legal 796 00:46:07,600 --> 00:46:09,600 Speaker 1: you talk You brought your lawyer on what can I 797 00:46:09,680 --> 00:46:14,080 Speaker 1: do legally? Which I thought was an InTru you know, look, 798 00:46:14,120 --> 00:46:18,640 Speaker 1: it was transparent and are you at all concerned about 799 00:46:18,719 --> 00:46:19,880 Speaker 1: legal exposure on this? 800 00:46:20,000 --> 00:46:22,759 Speaker 2: By the way, not at all, And I think it's 801 00:46:22,800 --> 00:46:27,839 Speaker 2: for those reasons, Like I made the process extremely transparent, 802 00:46:29,760 --> 00:46:34,440 Speaker 2: so no spoilers, this isn't a spoiler, but like we 803 00:46:34,680 --> 00:46:37,760 Speaker 2: show the guts of like the deep fake and how 804 00:46:37,800 --> 00:46:38,440 Speaker 2: we make it. 805 00:46:38,680 --> 00:46:41,759 Speaker 1: Oh exactly, it's the whole process, which I think was 806 00:46:41,800 --> 00:46:45,200 Speaker 1: a healthy exercise. I you know, I've had people recommend 807 00:46:46,040 --> 00:46:49,400 Speaker 1: tell me I should make my own chuck bot large 808 00:46:49,719 --> 00:46:55,560 Speaker 1: language model, and I am, there's I I'm like, why 809 00:46:55,560 --> 00:46:58,319 Speaker 1: do I want to do that? You know, I don't know. 810 00:46:58,520 --> 00:47:00,759 Speaker 1: You know, it's like upload my book and upload all that, 811 00:47:01,160 --> 00:47:05,760 Speaker 1: and you're like, I don't know. I My thinking's evolved 812 00:47:05,760 --> 00:47:09,360 Speaker 1: over thirty five years. I don't want to always have 813 00:47:09,480 --> 00:47:13,040 Speaker 1: what I thought twenty years ago be the baseline for 814 00:47:13,120 --> 00:47:16,680 Speaker 1: what my bot thinks. Anyway, It's it's a but I 815 00:47:16,680 --> 00:47:18,680 Speaker 1: thought it was a healthy thing to let people know 816 00:47:19,120 --> 00:47:20,040 Speaker 1: how this is done. 817 00:47:21,000 --> 00:47:24,319 Speaker 2: Yeah, and and to be transparent that like, you know, 818 00:47:24,360 --> 00:47:28,600 Speaker 2: we're not what you're seeing is not supposed to manipulate 819 00:47:28,640 --> 00:47:32,040 Speaker 2: you or trick you, like, because that is where I 820 00:47:32,120 --> 00:47:34,600 Speaker 2: draw the line. I think there are ethics around this, 821 00:47:34,640 --> 00:47:36,680 Speaker 2: and I think when you're trying to manipulate people and 822 00:47:36,719 --> 00:47:41,600 Speaker 2: trick people through deep fakes or or through AI generated video, 823 00:47:42,600 --> 00:47:47,200 Speaker 2: that's just that's just wrong. That is unethical. And people 824 00:47:47,239 --> 00:47:48,800 Speaker 2: ask me, well, what do we what you know, what 825 00:47:49,200 --> 00:47:51,520 Speaker 2: do you think people should do about it, well, like, yeah, 826 00:47:51,520 --> 00:47:53,160 Speaker 2: I think I think there should be some type of 827 00:47:53,160 --> 00:47:56,239 Speaker 2: disclaimer on it in the same way that you know, 828 00:47:57,040 --> 00:48:00,200 Speaker 2: back in the nineties there were parental advisory logo was 829 00:48:00,200 --> 00:48:03,040 Speaker 2: on rap music that I would buy, Like you know, 830 00:48:03,120 --> 00:48:07,520 Speaker 2: it's that simple. Uh, but yeah, it's it's it's a 831 00:48:07,640 --> 00:48:11,799 Speaker 2: it's a huge it's a huge problem, this idea that 832 00:48:11,840 --> 00:48:14,160 Speaker 2: we won't be able to tell what's real and what's 833 00:48:14,400 --> 00:48:17,560 Speaker 2: not and what's AI generated in the very near future. 834 00:48:18,719 --> 00:48:20,799 Speaker 1: I think sometimes when we're trying to figure out what 835 00:48:20,920 --> 00:48:23,480 Speaker 1: to regulate, you almost have to go through the process 836 00:48:23,520 --> 00:48:27,439 Speaker 1: of doing what you did, deep faking of a well 837 00:48:27,480 --> 00:48:31,279 Speaker 1: known person to then understand what regulate, what a what 838 00:48:31,320 --> 00:48:35,239 Speaker 1: a pragmatic regulatory structure would look like. So you went 839 00:48:35,280 --> 00:48:38,000 Speaker 1: through this process. You might have had some idea of 840 00:48:38,040 --> 00:48:40,319 Speaker 1: what it would look like before, but now that you've 841 00:48:40,360 --> 00:48:43,040 Speaker 1: done it, what do you think? And now like, hey, 842 00:48:43,680 --> 00:48:46,360 Speaker 1: and I say this, advise me. What are the questions 843 00:48:46,400 --> 00:48:51,120 Speaker 1: I should be asking candidates running for office about AI 844 00:48:51,280 --> 00:48:55,239 Speaker 1: regulation that you now understand in a way that you 845 00:48:55,320 --> 00:48:57,320 Speaker 1: didn't when you started this film. 846 00:48:57,760 --> 00:49:01,800 Speaker 2: Yeah, I mean I hope that candidate's running for office 847 00:49:01,800 --> 00:49:04,400 Speaker 2: at at least have because some of these they are 848 00:49:04,560 --> 00:49:07,920 Speaker 2: just so many dinosaurs who don't even know, like I 849 00:49:07,960 --> 00:49:10,040 Speaker 2: can't even talk about this kind of stuff, But I 850 00:49:10,040 --> 00:49:12,520 Speaker 2: would hope that they would, that they would be able 851 00:49:12,560 --> 00:49:16,440 Speaker 2: to have a conversation and answer questions like what do 852 00:49:16,520 --> 00:49:20,400 Speaker 2: we what are we going to do about AI generated 853 00:49:20,520 --> 00:49:24,200 Speaker 2: video in the future, like how are we like, how 854 00:49:24,239 --> 00:49:27,440 Speaker 2: are we going to differentiate it? How are we going 855 00:49:27,520 --> 00:49:30,719 Speaker 2: to make sure that that our children and are like 856 00:49:30,840 --> 00:49:35,080 Speaker 2: grandparents aren't like duped by this kind of stuff? Like 857 00:49:35,480 --> 00:49:37,319 Speaker 2: how are you how are like how is that going 858 00:49:37,360 --> 00:49:38,919 Speaker 2: to happen? How are you gonna how are you gonna 859 00:49:39,360 --> 00:49:41,319 Speaker 2: do this? Like the problem is they have no idea 860 00:49:41,360 --> 00:49:42,759 Speaker 2: what that even means, most of them. 861 00:49:43,080 --> 00:49:44,400 Speaker 1: Can I just tell you. I can't tell you how 862 00:49:44,440 --> 00:49:47,240 Speaker 1: many times I go on social media and I'll follow 863 00:49:47,239 --> 00:49:50,239 Speaker 1: and somebody will say, well, we'll sort of forward a 864 00:49:50,280 --> 00:49:53,680 Speaker 1: post and say, well, this is obvious AI slop and 865 00:49:53,760 --> 00:49:56,040 Speaker 1: I look at at it with my fifty three year 866 00:49:56,040 --> 00:49:59,480 Speaker 1: old eyes, and I'm like, going, it is I see, 867 00:49:59,560 --> 00:50:02,279 Speaker 1: I don't see two knees, I don't see six fingers, right, 868 00:50:02,520 --> 00:50:05,799 Speaker 1: you know, I know that the rudimentary checklist, But as 869 00:50:05,800 --> 00:50:09,880 Speaker 1: we know, all this stuff gets better, you know, I 870 00:50:09,920 --> 00:50:12,880 Speaker 1: think it's harder than ever to see the distinction, and 871 00:50:12,960 --> 00:50:15,400 Speaker 1: yet it does seem as if people with younger eyes 872 00:50:16,000 --> 00:50:17,480 Speaker 1: like you go through this in the film. Again, I 873 00:50:17,520 --> 00:50:20,280 Speaker 1: apology for the spoiler, but I remember at one point, 874 00:50:20,560 --> 00:50:23,399 Speaker 1: the first time you debut your your deep fake, after 875 00:50:23,440 --> 00:50:26,080 Speaker 1: the whole there's kind of a little bit of a 876 00:50:26,600 --> 00:50:28,759 Speaker 1: of an escapade of trying to get the Guide to 877 00:50:29,880 --> 00:50:31,640 Speaker 1: the Caribbean and all that stuff. I don't want to that. 878 00:50:31,680 --> 00:50:33,200 Speaker 1: I think that's a fun part of the movie. I 879 00:50:33,239 --> 00:50:35,520 Speaker 1: don't want to spoil that. It's kind of a little 880 00:50:35,600 --> 00:50:39,880 Speaker 1: humorous part. But I remember I think it was I 881 00:50:39,880 --> 00:50:43,040 Speaker 1: think it was you or a colleague who said, oh man, 882 00:50:43,200 --> 00:50:46,359 Speaker 1: my fourteen year old would not believe that that looks 883 00:50:46,400 --> 00:50:50,760 Speaker 1: like a bad video game character. And I'm like, okay, 884 00:50:51,120 --> 00:50:54,480 Speaker 1: if you say so, Like I didn't. I felt like, 885 00:50:54,719 --> 00:50:56,640 Speaker 1: oh no, are my eyes too old to see this? 886 00:50:57,640 --> 00:51:01,480 Speaker 2: Yeah? Yeah, that was that was being a perfectionist director, 887 00:51:01,719 --> 00:51:05,239 Speaker 2: I guess, but it did. You're right, some people have 888 00:51:05,360 --> 00:51:08,560 Speaker 2: seen the deep Fake, and this this is like kind 889 00:51:08,560 --> 00:51:10,759 Speaker 2: of a spoiler, but like some people have seen the 890 00:51:10,800 --> 00:51:13,000 Speaker 2: deep Fake when it's finally created in the movie and 891 00:51:13,040 --> 00:51:15,799 Speaker 2: thought like what are you talking about, Adam? That was 892 00:51:15,800 --> 00:51:20,000 Speaker 2: actually really good and so yeah, it is, it is, 893 00:51:21,120 --> 00:51:23,440 Speaker 2: but that to me is even scarier because I feel 894 00:51:23,440 --> 00:51:29,160 Speaker 2: like then I think about, you know, certain certain members 895 00:51:29,200 --> 00:51:32,200 Speaker 2: of my family who maybe aren't as like on you know, 896 00:51:32,400 --> 00:51:36,399 Speaker 2: always on the internet, as as I am right being 897 00:51:36,520 --> 00:51:40,120 Speaker 2: duped by this getting a getting a call. I mean, 898 00:51:40,160 --> 00:51:42,319 Speaker 2: the craziest things are going to happen, right, getting a 899 00:51:42,360 --> 00:51:45,960 Speaker 2: FaceTime from me, like saying like I'm in trouble, I 900 00:51:46,000 --> 00:51:47,400 Speaker 2: need money, Like right now. 901 00:51:47,239 --> 00:51:49,600 Speaker 1: Have you come up with the code words with your family? 902 00:51:49,680 --> 00:51:49,879 Speaker 2: Yet? 903 00:51:49,920 --> 00:51:51,880 Speaker 1: I keep having people tell me I should and I 904 00:51:51,920 --> 00:51:52,480 Speaker 1: have it yet. 905 00:51:53,160 --> 00:51:56,120 Speaker 2: Yeah, that reminds me that I have to as well. 906 00:51:56,160 --> 00:52:00,360 Speaker 2: I mean, you know, right now, it's it's not so 907 00:52:00,440 --> 00:52:04,040 Speaker 2: much the video, it's the voice cloning. The voice cloning 908 00:52:04,120 --> 00:52:05,080 Speaker 2: is pretty. 909 00:52:06,200 --> 00:52:08,839 Speaker 1: I'm personally petrified to that because of how much of 910 00:52:08,880 --> 00:52:11,760 Speaker 1: my voice is out there. Yeah right, it's just sitting 911 00:52:11,800 --> 00:52:15,279 Speaker 1: out there and fair used land, fair use land yet, right, 912 00:52:15,440 --> 00:52:18,560 Speaker 1: And that is something that has concerned me. I don't 913 00:52:18,560 --> 00:52:20,280 Speaker 1: know what to do about it. What am I suppos 914 00:52:20,400 --> 00:52:24,080 Speaker 1: it's a concern? I really have, what I mean, I'm 915 00:52:24,080 --> 00:52:28,359 Speaker 1: not Matthew McConaughey, who's decided to like trademark is at 916 00:52:28,440 --> 00:52:32,360 Speaker 1: least some of his sayings, which is maybe he's onto something. 917 00:52:32,360 --> 00:52:32,759 Speaker 1: I don't know. 918 00:52:33,440 --> 00:52:37,120 Speaker 2: Yeah, I think he is onto something. I don't think 919 00:52:37,120 --> 00:52:38,879 Speaker 2: there's much we can do about it other than yeah, 920 00:52:39,040 --> 00:52:42,640 Speaker 2: like have a safe word. This sounds so crazy to 921 00:52:42,680 --> 00:52:43,600 Speaker 2: even talk about. 922 00:52:43,360 --> 00:52:45,879 Speaker 1: It, but like, did you think twenty years ago you'd 923 00:52:45,880 --> 00:52:49,080 Speaker 1: be having geez, we got to have safe words and 924 00:52:49,160 --> 00:52:52,239 Speaker 1: it had nothing to do with sex. Yeah, sorry, I mean, 925 00:52:52,280 --> 00:52:54,440 Speaker 1: I don't mean to go but like, you know, the 926 00:52:54,480 --> 00:52:57,080 Speaker 1: first time I heard safe words, it's usually associated with that. 927 00:52:59,719 --> 00:53:03,360 Speaker 2: It's it's crazy to think that we uh have to 928 00:53:03,440 --> 00:53:07,239 Speaker 2: have a conversation with our kids of like, hey, or 929 00:53:07,360 --> 00:53:09,280 Speaker 2: our our grandparents are our parents. 930 00:53:09,040 --> 00:53:11,240 Speaker 1: Are just going to say, it's really our parents I think. 931 00:53:11,160 --> 00:53:14,720 Speaker 2: Might value and I'm I'm and I'm in trouble asking 932 00:53:14,760 --> 00:53:18,360 Speaker 2: for money. That might not be me, Like hold on 933 00:53:18,560 --> 00:53:22,040 Speaker 2: just a second, and you know, text me on the 934 00:53:22,040 --> 00:53:25,240 Speaker 2: other line because that may not be me. But that's 935 00:53:25,440 --> 00:53:27,560 Speaker 2: the kind of conversations that we're going to have to 936 00:53:27,719 --> 00:53:31,560 Speaker 2: have like now, like not not not now, but right now. 937 00:53:31,800 --> 00:53:33,799 Speaker 1: Well, it's funny, you know, with AI safety, I mean, 938 00:53:34,040 --> 00:53:36,759 Speaker 1: who should be paying for those public service announcements, like, 939 00:53:36,800 --> 00:53:38,919 Speaker 1: in some ways, if this is what we have to do, 940 00:53:40,120 --> 00:53:43,440 Speaker 1: and you know, there's a part of me that wonders 941 00:53:43,480 --> 00:53:46,440 Speaker 1: if you start doing that as sort of like a PSA, 942 00:53:47,040 --> 00:53:49,080 Speaker 1: are we accepting the premise that we're all just going 943 00:53:49,160 --> 00:53:51,680 Speaker 1: to get deep faked? Or do we still want to 944 00:53:51,680 --> 00:53:52,120 Speaker 1: fight this? 945 00:53:53,400 --> 00:53:55,799 Speaker 2: I mean, I don't think we can fight it. I 946 00:53:55,840 --> 00:53:59,160 Speaker 2: think we need to accept the head premise. But maybe 947 00:53:59,160 --> 00:54:03,879 Speaker 2: that's just me. But I also made this movie called 948 00:54:03,920 --> 00:54:05,359 Speaker 2: Deep Faking Sam Altman. 949 00:54:06,120 --> 00:54:09,720 Speaker 1: Right, speaking of making that movie, what parts of AI 950 00:54:10,200 --> 00:54:13,520 Speaker 1: as a movie maker you like, Oh, man, I I 951 00:54:13,760 --> 00:54:17,239 Speaker 1: I you know, I walk that picket line this or that. 952 00:54:17,400 --> 00:54:20,839 Speaker 1: But boy, that's kind of nice that I could do 953 00:54:21,000 --> 00:54:24,360 Speaker 1: this and this short of time. Is there some tools 954 00:54:24,400 --> 00:54:28,040 Speaker 1: that you just like definitely guilty, guilty that you feel 955 00:54:28,040 --> 00:54:29,359 Speaker 1: guilty about how great they are? 956 00:54:29,800 --> 00:54:35,200 Speaker 2: Yes? Yes, Like so I'm a documentary filmmaker. I do 957 00:54:35,400 --> 00:54:39,400 Speaker 2: make more traditional documentary films. This one is is definitely 958 00:54:39,400 --> 00:54:42,239 Speaker 2: a weird or more narrative hybrid one. But you know 959 00:54:42,280 --> 00:54:46,320 Speaker 2: I did that the alt right documentary about Charlottesville riot. 960 00:54:46,360 --> 00:54:52,400 Speaker 2: And so the ability to create, to generate archival footage, 961 00:54:53,280 --> 00:54:56,239 Speaker 2: to not have to go spend. 962 00:54:56,080 --> 00:54:59,040 Speaker 1: The amount of time, right to get archival footage like 963 00:54:59,280 --> 00:55:01,480 Speaker 1: that that could in some ways take more time than 964 00:55:01,640 --> 00:55:04,240 Speaker 1: just shooting what you're shooting, right. 965 00:55:04,160 --> 00:55:07,120 Speaker 2: Yeah, like going out and hunting down a piece of 966 00:55:07,239 --> 00:55:11,320 Speaker 2: archival like taking hours and hours and hours in costing 967 00:55:11,400 --> 00:55:15,120 Speaker 2: maybe costing like you know, uh, tens tens of thousands 968 00:55:15,200 --> 00:55:17,560 Speaker 2: of dollars to license. To be able to just sit 969 00:55:17,600 --> 00:55:21,920 Speaker 2: down and create it in thirty seconds. Incredible, Like I 970 00:55:21,920 --> 00:55:23,680 Speaker 2: would love to be able to do that, but we're 971 00:55:23,719 --> 00:55:27,440 Speaker 2: already shutting that down. Within the documentary world. There's like 972 00:55:27,480 --> 00:55:29,799 Speaker 2: a code of ethics now around AI that's being kind 973 00:55:29,800 --> 00:55:33,000 Speaker 2: of passed around, which I get it, I understand, but 974 00:55:33,120 --> 00:55:35,040 Speaker 2: like part of me is like, oh, man, like I 975 00:55:35,560 --> 00:55:38,520 Speaker 2: that's that's that's going to make things so much easier. 976 00:55:39,120 --> 00:55:42,320 Speaker 1: Yeah, well that's what I you know, I think about 977 00:55:42,360 --> 00:55:48,000 Speaker 1: this when you know, look, there's new technologies always create fear, right, 978 00:55:48,080 --> 00:55:51,600 Speaker 1: there is a there's a so and and look AI 979 00:55:53,480 --> 00:55:56,359 Speaker 1: AI optimists talk about this a lot, right, and it's 980 00:55:56,400 --> 00:56:00,440 Speaker 1: a fair counterpoint that you know, there's we're always stopian 981 00:56:00,480 --> 00:56:03,600 Speaker 1: about a new technology, but it usually is is more 982 00:56:03,640 --> 00:56:07,239 Speaker 1: benign than we by the the actual implementation is a 983 00:56:07,280 --> 00:56:14,040 Speaker 1: lot more benign than ever before. Why isn't that going 984 00:56:14,080 --> 00:56:15,040 Speaker 1: to be the case with AI? 985 00:56:16,239 --> 00:56:18,480 Speaker 2: Well, I mean I think it. I think it will 986 00:56:18,520 --> 00:56:24,440 Speaker 2: be the case to a certain extent, right, But the 987 00:56:24,600 --> 00:56:28,960 Speaker 2: problem is where where where it becomes more dystopian is 988 00:56:29,000 --> 00:56:32,000 Speaker 2: when you start to when you start to put AI 989 00:56:32,080 --> 00:56:37,680 Speaker 2: into things like weapons, you know, guns, missiles, bombs, grenades, 990 00:56:37,719 --> 00:56:42,960 Speaker 2: stuff like that, because it can be used and it 991 00:56:43,000 --> 00:56:46,400 Speaker 2: is already being used in that degree, and you know, 992 00:56:46,520 --> 00:56:49,160 Speaker 2: other things like social media not necessarily and I can't 993 00:56:49,200 --> 00:56:54,360 Speaker 2: necessarily like this. This AI is kind of perfect for defense, 994 00:56:55,320 --> 00:56:58,080 Speaker 2: and that's where it gets really dark and dystopian. 995 00:56:59,120 --> 00:57:03,160 Speaker 1: Yeah, I'm speaking of that. I've been wondering why that 996 00:57:03,239 --> 00:57:06,000 Speaker 1: we haven't seen a reboot of war Games, you know, 997 00:57:06,040 --> 00:57:09,959 Speaker 1: the whole premise of Wargames, the Matthew Broderick movie, which 998 00:57:10,040 --> 00:57:12,120 Speaker 1: may have come out before you were born. So I 999 00:57:12,160 --> 00:57:16,800 Speaker 1: don't have you, do you, So my apologize. I'm forty six, 1000 00:57:16,840 --> 00:57:19,880 Speaker 1: so okay, you're much closer, Okay then you were around 1001 00:57:19,920 --> 00:57:24,480 Speaker 1: when War Games. You know, how have we not rebooted 1002 00:57:24,520 --> 00:57:27,200 Speaker 1: that movie with the with with the with the scary 1003 00:57:27,240 --> 00:57:30,520 Speaker 1: times that were that was a bit far fetched at 1004 00:57:30,520 --> 00:57:34,040 Speaker 1: the time, right, we couldn't really imagine that there wouldn't 1005 00:57:34,080 --> 00:57:35,800 Speaker 1: be human elements to get in the way of that. 1006 00:57:36,440 --> 00:57:41,840 Speaker 2: Now, Yeah, I mean it's a good idea. Maybe I 1007 00:57:41,880 --> 00:57:44,120 Speaker 2: need to get on get on that, get on pitching that. 1008 00:57:44,280 --> 00:57:49,440 Speaker 1: There you go. Yeah, speaking of speaking of that, what 1009 00:57:49,600 --> 00:57:52,320 Speaker 1: is next for you? What are the next topics that 1010 00:57:52,320 --> 00:57:54,360 Speaker 1: that are keeping you up at night that you think 1011 00:57:54,440 --> 00:57:56,840 Speaker 1: deserve deserve the documentary treatment? 1012 00:57:57,520 --> 00:58:01,400 Speaker 2: Oh? Man, I mean, I've got a couple of things 1013 00:58:01,400 --> 00:58:06,160 Speaker 2: that I'm out there pitching right now. I'm pitching. I'm 1014 00:58:06,200 --> 00:58:09,200 Speaker 2: pitching a project that I don't I don't want to. 1015 00:58:09,360 --> 00:58:13,400 Speaker 2: I don't want to reveal exactly what it is because 1016 00:58:13,400 --> 00:58:17,000 Speaker 2: it's such I can't believe nobody has done something on it. 1017 00:58:18,760 --> 00:58:21,960 Speaker 2: But it's in the it's in the world of personal 1018 00:58:22,000 --> 00:58:27,640 Speaker 2: injury law, okay, And I think that is a world 1019 00:58:27,920 --> 00:58:35,960 Speaker 2: that is ripe for documentary treatment, Like we've only really 1020 00:58:36,000 --> 00:58:38,880 Speaker 2: seen it, I think in better call Saul. Like, I 1021 00:58:38,880 --> 00:58:41,640 Speaker 2: can't believe there hasn't been a documentary in that world. 1022 00:58:42,240 --> 00:58:46,800 Speaker 2: It's a fascinating for a couple of reasons. Fascinating people, right, 1023 00:58:47,120 --> 00:58:51,000 Speaker 2: These lawyers are incredible characters. 1024 00:58:50,960 --> 00:58:54,120 Speaker 1: Right I Yeah, Look, I'm a sucker for a good 1025 00:58:54,280 --> 00:58:58,680 Speaker 1: sort of like you know, uh uh these when you 1026 00:58:58,720 --> 00:59:00,880 Speaker 1: there's a few sort of lawyer, but Bok, Scott Turou 1027 00:59:00,960 --> 00:59:04,440 Speaker 1: and all those guys love to write about those lawyers, 1028 00:59:04,880 --> 00:59:09,520 Speaker 1: right the ones that you know, frankly are the working 1029 00:59:09,560 --> 00:59:11,200 Speaker 1: class lawyer. I don't know, let us to call them 1030 00:59:11,240 --> 00:59:13,440 Speaker 1: right where they've got to just make a living one 1031 00:59:13,480 --> 00:59:14,560 Speaker 1: thousand bucks at a time. 1032 00:59:15,080 --> 00:59:18,920 Speaker 2: You know what's really I think interesting and urgent about 1033 00:59:19,320 --> 00:59:22,240 Speaker 2: that topic right now too, is because our healthcare system 1034 00:59:22,320 --> 00:59:26,520 Speaker 2: is so incredibly broken that there are a lot of 1035 00:59:26,560 --> 00:59:32,680 Speaker 2: people who don't have healthcare, and so when the shit 1036 00:59:32,760 --> 00:59:35,680 Speaker 2: hits the fan, when something tragic happens, those are the 1037 00:59:35,680 --> 00:59:39,440 Speaker 2: only people they can go to. Those are their heroes, 1038 00:59:39,520 --> 00:59:41,640 Speaker 2: are like are they're like local heroes. 1039 00:59:42,040 --> 00:59:42,680 Speaker 1: Yeah. 1040 00:59:42,720 --> 00:59:46,160 Speaker 2: And so on the one hand, they're seen as like 1041 00:59:46,200 --> 00:59:50,440 Speaker 2: these grifters, they're seen as these like con men. But 1042 00:59:50,480 --> 00:59:56,640 Speaker 2: on the other hand, in this terrible system that we're 1043 00:59:56,680 --> 00:59:59,760 Speaker 2: living in where healthcare is just could not be any worse, 1044 00:59:59,800 --> 01:00:03,000 Speaker 2: I mean it is, it is like it. 1045 01:00:02,080 --> 01:00:05,840 Speaker 1: It's well, the system, the healthcare companies, the the system 1046 01:00:05,920 --> 01:00:08,200 Speaker 1: is designed to pay the least amount of money that 1047 01:00:08,200 --> 01:00:08,760 Speaker 1: they can pay. 1048 01:00:09,280 --> 01:00:12,120 Speaker 2: Yeah, And so these guys come into the fray and 1049 01:00:12,160 --> 01:00:15,720 Speaker 2: are like, I'm going to bleed, these healthcare these these 1050 01:00:15,720 --> 01:00:19,160 Speaker 2: healthcare companies dry. I'm going to get you your money, right, 1051 01:00:19,200 --> 01:00:22,720 Speaker 2: And so there it's it's a great I think topic 1052 01:00:22,800 --> 01:00:24,000 Speaker 2: for documentary because. 1053 01:00:23,920 --> 01:00:26,680 Speaker 1: No, meanwhile, you know, I don't think meanwhile, a week 1054 01:00:26,720 --> 01:00:29,120 Speaker 1: doesn't go by where I don't have some company I 1055 01:00:29,200 --> 01:00:32,040 Speaker 1: have a relationship with to say, hey, we've changed our 1056 01:00:32,120 --> 01:00:35,800 Speaker 1: terms and conditions to now move to arbitration when there's 1057 01:00:35,800 --> 01:00:39,440 Speaker 1: an objection, right, which is like the amount of companies, 1058 01:00:39,520 --> 01:00:41,360 Speaker 1: And at some point I have a feeling government's got 1059 01:00:41,360 --> 01:00:44,120 Speaker 1: to step in here because the amount of companies that 1060 01:00:44,200 --> 01:00:47,040 Speaker 1: now basically don't ever want to have to face the 1061 01:00:47,120 --> 01:00:53,960 Speaker 1: judicial system and want to make consumers automatically accept the 1062 01:00:53,960 --> 01:00:58,600 Speaker 1: premise that arbitrations are only outlet if something goes wrong. 1063 01:00:59,520 --> 01:01:01,080 Speaker 1: Feels a bit coercive. 1064 01:01:01,760 --> 01:01:04,760 Speaker 2: Well, yeah, I mean it's criminal. I think I think 1065 01:01:04,800 --> 01:01:07,400 Speaker 2: these I think these people are criminals. And so in 1066 01:01:07,440 --> 01:01:11,720 Speaker 2: that in that world, like the personal injury lawyer the bill. 1067 01:01:11,760 --> 01:01:14,600 Speaker 1: You're one hundred percent right though about the characters, You're 1068 01:01:14,640 --> 01:01:17,080 Speaker 1: one hundred percent right on the characters. I'm I'm friendly 1069 01:01:17,120 --> 01:01:20,080 Speaker 1: with John Morgan. He's a sponsor at times of this podcast. 1070 01:01:20,120 --> 01:01:25,520 Speaker 1: He's sort of there the characters in this world make 1071 01:01:25,560 --> 01:01:28,160 Speaker 1: would make a good movie, Yeah, really would. 1072 01:01:29,000 --> 01:01:32,480 Speaker 2: I've been talking to Ross Sealino of Selino and Barnes. 1073 01:01:33,120 --> 01:01:36,520 Speaker 2: He's kind of like the Lebron James of the industry. 1074 01:01:36,880 --> 01:01:39,960 Speaker 2: And then I've been talking to three guys named the 1075 01:01:40,000 --> 01:01:44,160 Speaker 2: Hammer in three different states. There's three Hammers. 1076 01:01:44,760 --> 01:01:47,240 Speaker 1: You know. If it wasn't for personal injury lawyers, there 1077 01:01:47,280 --> 01:01:52,240 Speaker 1: would be no no buyers of billboard advertising on interstates, 1078 01:01:52,480 --> 01:01:54,600 Speaker 1: right Like that's you know, you go take a road 1079 01:01:54,600 --> 01:01:57,800 Speaker 1: trip anywhere around the country, go to Eddie Eddy interstate 1080 01:01:57,800 --> 01:02:02,360 Speaker 1: in the country, and there's basic religious groups or personal 1081 01:02:02,360 --> 01:02:06,480 Speaker 1: injury lawyers that are now BUCkies that buy the billboard ads. 1082 01:02:06,760 --> 01:02:10,040 Speaker 2: Yeah. I talked to a guy called called himself the 1083 01:02:10,040 --> 01:02:14,480 Speaker 2: Anti Lawyer. He spends four hundred thousand dollars a month 1084 01:02:14,520 --> 01:02:15,840 Speaker 2: on billboards. 1085 01:02:17,120 --> 01:02:20,040 Speaker 1: Yeah, well, I think you've you've sold me. I can't 1086 01:02:20,040 --> 01:02:23,560 Speaker 1: wait to you know you we could pre book the 1087 01:02:23,640 --> 01:02:26,560 Speaker 1: podcast right now the day after you premiere that one. 1088 01:02:26,600 --> 01:02:28,720 Speaker 1: That that sounds like a lot of fun. What else 1089 01:02:28,760 --> 01:02:30,400 Speaker 1: on your radar? Anything more in the tech world. 1090 01:02:32,200 --> 01:02:33,520 Speaker 2: I think I'm going to take a little bit of 1091 01:02:33,560 --> 01:02:35,040 Speaker 2: a break from the tech world. 1092 01:02:36,920 --> 01:02:39,280 Speaker 1: How long did this take of your time. How much 1093 01:02:39,320 --> 01:02:42,480 Speaker 1: of this was this an eighteen month two years? 1094 01:02:42,800 --> 01:02:45,400 Speaker 2: Eighteen months? Yeah, it was about eighteen months. It was 1095 01:02:45,440 --> 01:02:48,640 Speaker 2: a quick turnaround because we knew that, like, there was 1096 01:02:48,760 --> 01:02:51,600 Speaker 2: urgency in getting the story out. There's a lot of 1097 01:02:51,600 --> 01:02:55,120 Speaker 2: people working on AI documentaries. I think we're pretty much 1098 01:02:55,160 --> 01:02:59,480 Speaker 2: the first to come out, and you know, it's an 1099 01:02:59,560 --> 01:03:03,640 Speaker 2: urgent that people desperately wanted, want answers to, and want 1100 01:03:03,640 --> 01:03:06,080 Speaker 2: to talk about. So so we pushed this thing out 1101 01:03:06,120 --> 01:03:12,240 Speaker 2: pretty quickly and premiered at south By Southwest and last March. 1102 01:03:13,120 --> 01:03:15,560 Speaker 1: Well, look, I think the best part of this movie 1103 01:03:15,600 --> 01:03:18,040 Speaker 1: is just this. It's supposed to spark a conversation. It's 1104 01:03:18,040 --> 01:03:21,680 Speaker 1: supposed to spark questions, and you know, if you smoke 1105 01:03:21,720 --> 01:03:24,000 Speaker 1: out the leaders to talk about it. I mean I 1106 01:03:24,000 --> 01:03:27,080 Speaker 1: feel like, did you talk with Jeffrey Hinton, right, who's 1107 01:03:27,080 --> 01:03:30,120 Speaker 1: sort of the godfather of AI, who warns people about AI. 1108 01:03:31,480 --> 01:03:33,640 Speaker 2: So I reached out to him. I did get in 1109 01:03:33,680 --> 01:03:40,560 Speaker 2: touch with him, and he's overseas taking us about it 1110 01:03:40,640 --> 01:03:42,680 Speaker 2: or working on something or other, but he did actually 1111 01:03:42,760 --> 01:03:45,160 Speaker 2: his emails online and he actually did respond to me. 1112 01:03:47,480 --> 01:03:51,919 Speaker 1: Well, it's congratulations on it, and you know, I hope 1113 01:03:51,960 --> 01:03:53,840 Speaker 1: you keep track of the type of Q and a's 1114 01:03:53,880 --> 01:03:56,160 Speaker 1: that you have. In some ways there might be I'm 1115 01:03:56,200 --> 01:03:58,800 Speaker 1: not saying build a large language model from all of 1116 01:03:58,840 --> 01:04:03,240 Speaker 1: the q and as, but the AM I would love 1117 01:04:03,320 --> 01:04:07,440 Speaker 1: to see sort of after you've done this the collection, 1118 01:04:08,080 --> 01:04:12,000 Speaker 1: because I think you will have a potential resource of 1119 01:04:12,880 --> 01:04:17,720 Speaker 1: concerns that maybe our elected leaders should be aware of. 1120 01:04:18,480 --> 01:04:21,080 Speaker 2: Yeah, it's a good point. I'm definitely filming them all. 1121 01:04:21,200 --> 01:04:25,640 Speaker 2: And you know, the question is do our elected leaders 1122 01:04:25,800 --> 01:04:29,000 Speaker 2: care enough to even want to hear it? 1123 01:04:30,120 --> 01:04:32,760 Speaker 1: You know, I will always say this about the elected leaders. 1124 01:04:32,800 --> 01:04:34,439 Speaker 1: They care when their voters. 1125 01:04:34,080 --> 01:04:36,760 Speaker 2: Care, right right on. 1126 01:04:36,840 --> 01:04:41,040 Speaker 1: So your job is to make the voters care. And look, 1127 01:04:41,160 --> 01:04:44,680 Speaker 1: you did what a good educator should do. You also 1128 01:04:44,720 --> 01:04:46,800 Speaker 1: figured out how to entertain. Right at the end of 1129 01:04:46,880 --> 01:04:49,880 Speaker 1: the day, we're all educators in this media business if 1130 01:04:49,920 --> 01:04:51,840 Speaker 1: you're doing it right, and you've got to get people 1131 01:04:51,840 --> 01:04:54,640 Speaker 1: to pay attention, and this is an entertaining film and 1132 01:04:54,680 --> 01:04:56,680 Speaker 1: you made people pay attention. Congratulations. 1133 01:04:56,920 --> 01:04:58,680 Speaker 2: Oh, thank you very much. I appreciate that. 1134 01:04:59,360 --> 01:05:03,520 Speaker 1: All right, brother, And you're not a Phillies fan. We 1135 01:05:03,520 --> 01:05:05,440 Speaker 1: were talking about this pre pre tape, right am I 1136 01:05:05,480 --> 01:05:07,080 Speaker 1: am I supposed to take away that you are a 1137 01:05:07,080 --> 01:05:09,280 Speaker 1: Phillies fan or not a Phillies fan, not. 1138 01:05:09,280 --> 01:05:13,280 Speaker 2: A Phillies fan, a Dodgers fan. But I'm a collector 1139 01:05:13,360 --> 01:05:18,000 Speaker 2: of vintage jerseys, and uh, I figured somebody someone watching 1140 01:05:18,000 --> 01:05:21,040 Speaker 2: this podcast will appreciate the the Mike, the Michael Jack 1141 01:05:21,080 --> 01:05:23,760 Speaker 2: Schmidt vintage Phillies jersey. 1142 01:05:23,920 --> 01:05:26,640 Speaker 1: It's the best of all the all uniforms of the Phillies. 1143 01:05:26,640 --> 01:05:29,560 Speaker 1: That when they're doing the powder blue. It's funny how 1144 01:05:29,640 --> 01:05:31,360 Speaker 1: much the powder blue, right, it's the best of the 1145 01:05:31,440 --> 01:05:35,160 Speaker 1: Chargers uniforms. It's the best. We we that powder blue 1146 01:05:35,240 --> 01:05:38,960 Speaker 1: just does well on TV. Dodgers, evil Empire, or just 1147 01:05:39,000 --> 01:05:40,160 Speaker 1: smarter than everybody else. 1148 01:05:40,840 --> 01:05:44,560 Speaker 2: Oh, definitely smarter than everybody else. I mean there's there's 1149 01:05:44,600 --> 01:05:49,880 Speaker 2: also you know, I mean, look, Otani is greatest of 1150 01:05:49,880 --> 01:05:54,840 Speaker 2: all time. Sorry, like it's just it's Otani. Look it is. 1151 01:05:56,000 --> 01:05:57,600 Speaker 1: It's kind of boring. We don't even get to have 1152 01:05:57,600 --> 01:06:01,080 Speaker 1: a debate. It's not even close. Like it's like, oh yeah, 1153 01:06:01,240 --> 01:06:06,160 Speaker 1: and okay, next question. Like, you know, the beauty of 1154 01:06:06,200 --> 01:06:08,960 Speaker 1: sports is supposed to disagree, and we can't disagree right 1155 01:06:08,960 --> 01:06:10,600 Speaker 1: now in baseball, yeah. 1156 01:06:10,440 --> 01:06:15,680 Speaker 2: I mean all the evil Empire, money stuff, politics, you know, 1157 01:06:15,720 --> 01:06:19,840 Speaker 2: inner baseball politics, none of that. Take that all out 1158 01:06:19,840 --> 01:06:23,920 Speaker 2: of the conversation. It's just we have otani. Sorry, like that's. 1159 01:06:23,720 --> 01:06:27,920 Speaker 1: It, right, and everything else falls into place. Adam, good 1160 01:06:27,960 --> 01:06:30,480 Speaker 1: luck on the tour, be safe out there, and like 1161 01:06:30,520 --> 01:06:33,360 Speaker 1: I said, I hope document all your questions because I 1162 01:06:33,360 --> 01:06:35,800 Speaker 1: think there's going to be that's going to be a 1163 01:06:35,880 --> 01:06:37,840 Speaker 1: rich resource. If you can figure out how to put 1164 01:06:37,840 --> 01:06:38,040 Speaker 1: it to. 1165 01:06:38,120 --> 01:06:41,480 Speaker 2: Use, we'll do. Thank you all right, my friend appreciate it.