1 00:00:03,120 --> 00:00:11,080 Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. 2 00:00:14,240 --> 00:00:17,160 Speaker 2: Well, Elon Musk is now the richest person on the planet. 3 00:00:17,480 --> 00:00:20,319 Speaker 3: More than half the satellites in space are owned and 4 00:00:20,440 --> 00:00:22,160 Speaker 3: controlled by one man. 5 00:00:22,760 --> 00:00:24,640 Speaker 4: Well, he's a legitimate super genius. 6 00:00:24,840 --> 00:00:25,759 Speaker 5: I mean legitimate. 7 00:00:25,960 --> 00:00:28,600 Speaker 6: He says he's always voted for Democrats, but this year 8 00:00:28,640 --> 00:00:29,400 Speaker 6: it will be different. 9 00:00:29,440 --> 00:00:30,400 Speaker 7: He'll vote Republican. 10 00:00:30,680 --> 00:00:33,159 Speaker 2: There is a reason the US government is so reliant 11 00:00:33,200 --> 00:00:33,560 Speaker 2: on him. 12 00:00:33,760 --> 00:00:36,760 Speaker 4: Alon Musk is a scam artist and he's done nothing. 13 00:00:37,960 --> 00:00:52,320 Speaker 6: Anything he does, he's fascinating people. Welcome. We are recording 14 00:00:52,320 --> 00:00:56,640 Speaker 6: the thirty third and very finest episode of Elon Inc. 15 00:00:57,040 --> 00:01:00,720 Speaker 6: So here we go. Welcome to Elon Inc. Bloom's weekly 16 00:01:00,760 --> 00:01:04,280 Speaker 6: podcast about Elon Musk. I'm your host, David Poppadoppolis, and 17 00:01:04,319 --> 00:01:07,479 Speaker 6: we're live in San Francisco for the Bloomberg Technology Summit. 18 00:01:08,040 --> 00:01:10,880 Speaker 6: I'm joined on stage by two of the usual suspects, 19 00:01:11,319 --> 00:01:15,760 Speaker 6: ace Elon Musk reporter Dana Hall Hey Litt Dana and 20 00:01:15,840 --> 00:01:20,640 Speaker 6: intrepid BusinessWeek reporter Max Chafkin Hey. And we also have 21 00:01:21,000 --> 00:01:23,880 Speaker 6: Max's new boss with us, Brad Stone, editor in chief 22 00:01:23,880 --> 00:01:26,440 Speaker 6: of BusinessWeek and the author of, among other books, The 23 00:01:26,480 --> 00:01:31,640 Speaker 6: Everything Store, Jeff Bezos and the age of Amazon Low Brett, Hi, David. Okay, 24 00:01:31,640 --> 00:01:33,560 Speaker 6: so we've got a treat for you all today, a 25 00:01:33,640 --> 00:01:37,199 Speaker 6: deep dive into all things Elon, Musk and AI. Will 26 00:01:37,240 --> 00:01:40,039 Speaker 6: look at where and how it all started and how 27 00:01:40,040 --> 00:01:43,600 Speaker 6: it's going today. Plus a fun game show segment at 28 00:01:43,600 --> 00:01:46,920 Speaker 6: the end. We're calling it the School of Groc and 29 00:01:47,040 --> 00:01:49,639 Speaker 6: in it we will test Brad, one of the great 30 00:01:49,760 --> 00:01:53,160 Speaker 6: chroniclers of tech today, and see if he can solve 31 00:01:53,160 --> 00:01:57,440 Speaker 6: a series of highly complex chatbot riddles we've created just 32 00:01:57,480 --> 00:02:01,200 Speaker 6: for him. Get your popcorn out, and for all of 33 00:02:01,240 --> 00:02:05,200 Speaker 6: you hear I'm with us, get ready to participate too. Okay, 34 00:02:05,720 --> 00:02:08,040 Speaker 6: but first we take a step back and we take 35 00:02:08,080 --> 00:02:11,560 Speaker 6: in the bigger picture. AI, of course, is all the 36 00:02:11,639 --> 00:02:13,720 Speaker 6: rage in the tech world today and open ai has 37 00:02:13,760 --> 00:02:17,640 Speaker 6: become one of, if not the largest purveyor of consumer 38 00:02:17,680 --> 00:02:21,160 Speaker 6: AI out there. And for those who didn't know, Elon 39 00:02:21,360 --> 00:02:25,079 Speaker 6: was a founder of open ai alongside Sam Altman, among others. 40 00:02:25,320 --> 00:02:27,839 Speaker 6: We have a brief clip of him talking about this 41 00:02:28,040 --> 00:02:28,680 Speaker 6: earlier this. 42 00:02:28,639 --> 00:02:30,520 Speaker 5: Year Open ai. 43 00:02:30,760 --> 00:02:32,880 Speaker 6: I mean, you seem somewhat frustrated with them. You were 44 00:02:32,880 --> 00:02:34,720 Speaker 6: one of the big contributors early. 45 00:02:34,440 --> 00:02:38,240 Speaker 5: On the reason I am the reason openI exists. 46 00:02:39,160 --> 00:02:41,760 Speaker 6: Okay, Max, I thought you were. 47 00:02:41,680 --> 00:02:44,240 Speaker 4: The modest it's a modest comment. 48 00:02:44,240 --> 00:02:46,120 Speaker 6: To bring us into the seat. It was he truly 49 00:02:46,560 --> 00:02:50,200 Speaker 6: that important to the creation and launching of Open AI. 50 00:02:50,520 --> 00:02:51,919 Speaker 4: I mean, yeah, kind of. 51 00:02:52,440 --> 00:02:55,920 Speaker 2: I mean I do think that Sam Altman, who is 52 00:02:56,080 --> 00:03:00,560 Speaker 2: the CEO and the other key co founder, probably would 53 00:03:00,600 --> 00:03:01,840 Speaker 2: have found a way to get it off the ground. 54 00:03:01,919 --> 00:03:06,040 Speaker 2: But Elon Musk in twenty fifteen started this thing with 55 00:03:06,600 --> 00:03:10,600 Speaker 2: Sam Altman that was dedicated essentially to creating some kind 56 00:03:10,639 --> 00:03:14,560 Speaker 2: of advanced artificial intelligence, but doing it right without hurting anyone. 57 00:03:15,080 --> 00:03:16,800 Speaker 4: And Elon Musk hunted it. 58 00:03:16,840 --> 00:03:20,119 Speaker 2: He provided close to fifty million dollars, and more than that, 59 00:03:20,400 --> 00:03:23,240 Speaker 2: his association with the company helped give it a lot 60 00:03:23,280 --> 00:03:25,839 Speaker 2: of creative some seam. Yeah, I give it a five 61 00:03:25,880 --> 00:03:27,120 Speaker 2: out of ten on truthfulness. 62 00:03:27,280 --> 00:03:31,239 Speaker 6: Let's think, you know, even another step back and look 63 00:03:31,320 --> 00:03:34,239 Speaker 6: at what drove him towards the AI space in the 64 00:03:34,240 --> 00:03:36,600 Speaker 6: first place. We're gonna listen to a clip of our 65 00:03:36,640 --> 00:03:40,800 Speaker 6: colleague Elon Musk biographer Ashley Vance that will be featured 66 00:03:40,800 --> 00:03:42,960 Speaker 6: in the upcoming season of our sister show Foundering. 67 00:03:44,080 --> 00:03:46,440 Speaker 8: I was interviewing Elon a lot then I was working 68 00:03:46,480 --> 00:03:49,760 Speaker 8: on a book about him, and it was clear to 69 00:03:49,840 --> 00:03:54,120 Speaker 8: me that he had this universe of machines. He was 70 00:03:54,120 --> 00:03:57,600 Speaker 8: making cars and rockets. The companies were kind of do 71 00:03:57,720 --> 00:04:00,880 Speaker 8: it okay, but not great. He was not this this 72 00:04:01,120 --> 00:04:03,560 Speaker 8: king of the universe. And he was looking over at 73 00:04:03,600 --> 00:04:07,200 Speaker 8: his friends who were doing really well and everything they did, 74 00:04:07,240 --> 00:04:11,040 Speaker 8: and they had these software empires and this this growing 75 00:04:11,240 --> 00:04:18,360 Speaker 8: AI empire. And my sense of where Elon's thoughts about 76 00:04:18,520 --> 00:04:22,640 Speaker 8: AI started to originate was part jealousy. I thought he 77 00:04:22,680 --> 00:04:25,200 Speaker 8: looked at Google and all the success it was having 78 00:04:25,279 --> 00:04:29,280 Speaker 8: and the success as friends were having, and he had 79 00:04:29,320 --> 00:04:31,520 Speaker 8: nothing like that. But he would never admit this out 80 00:04:31,600 --> 00:04:32,679 Speaker 8: loud that he was jealous. 81 00:04:33,120 --> 00:04:37,160 Speaker 6: So brown jealousy that's what that's what this is all about. 82 00:04:38,040 --> 00:04:40,359 Speaker 9: I think, as with so many things, Elon and you 83 00:04:40,400 --> 00:04:43,560 Speaker 9: guys demonstrate this with every episode. It's it's never one thing, 84 00:04:44,279 --> 00:04:47,400 Speaker 9: it's they're usually a couple factors, and in those are 85 00:04:47,440 --> 00:04:49,839 Speaker 9: a bundle of contradictions. I think, you know, there was 86 00:04:50,160 --> 00:04:53,159 Speaker 9: obviously a philosophical drive for him to get into AI, 87 00:04:53,560 --> 00:04:57,719 Speaker 9: to co found open Ai. He was extraordinarily worried about 88 00:04:57,720 --> 00:04:59,400 Speaker 9: what Google was doing with Deep Mind. 89 00:04:59,640 --> 00:05:01,279 Speaker 5: He didn't trust Larry Page. 90 00:05:01,360 --> 00:05:03,599 Speaker 9: There's some of the kind of dogma that goes into 91 00:05:03,640 --> 00:05:06,599 Speaker 9: the creation of open ai, the idea that it was 92 00:05:06,640 --> 00:05:09,719 Speaker 9: sort of nonprofit and open at the beginning, But you 93 00:05:09,839 --> 00:05:14,880 Speaker 9: can never totally remove, as with all things Elon personality emotion. 94 00:05:15,400 --> 00:05:18,679 Speaker 9: It was only when you know, Sam Almon became CEO 95 00:05:18,960 --> 00:05:22,039 Speaker 9: Elon wanted to be CEO of open Ai, when they 96 00:05:22,760 --> 00:05:26,000 Speaker 9: started a for profit division, and in particular when Microsoft 97 00:05:26,040 --> 00:05:29,000 Speaker 9: invested and when chat GPT blew up, that's when he 98 00:05:29,080 --> 00:05:31,320 Speaker 9: really came out of the woodwork. So yeah, I think 99 00:05:31,360 --> 00:05:33,800 Speaker 9: Ashley's right with all these guys. I saw this with 100 00:05:33,839 --> 00:05:36,760 Speaker 9: Bezos for twenty years too. If the spotlight is on 101 00:05:36,800 --> 00:05:39,120 Speaker 9: somebody else, they cannot stand it well. 102 00:05:39,160 --> 00:05:40,599 Speaker 1: And the other thing that's important to remember is that 103 00:05:40,640 --> 00:05:43,839 Speaker 1: back in twenty fourteen twenty fifteen, like Google had the 104 00:05:43,839 --> 00:05:46,520 Speaker 1: self driving car project and Chris Erbsen was driving around 105 00:05:46,560 --> 00:05:48,960 Speaker 1: Silicon Valley like showing off this demo of the car 106 00:05:49,080 --> 00:05:52,520 Speaker 1: that eventually became like the whole Weaimo division, and Elon 107 00:05:52,720 --> 00:05:55,160 Speaker 1: was like making cars and was like, oh. 108 00:05:54,839 --> 00:05:57,640 Speaker 6: Just the hardware, like no, no fancy. 109 00:05:57,560 --> 00:05:59,479 Speaker 1: Well, Tesla was just trying to make electric cars with 110 00:05:59,560 --> 00:06:01,720 Speaker 1: an all of a sudden, once Weymo was on the scene. 111 00:06:01,760 --> 00:06:04,120 Speaker 1: Then Elon was like, ooh, autonomy, like the future is 112 00:06:04,160 --> 00:06:04,760 Speaker 1: not just electric. 113 00:06:04,920 --> 00:06:09,360 Speaker 6: So prior to twenty fourteen fifteen, this whole notion did 114 00:06:09,400 --> 00:06:11,599 Speaker 6: not exist in the doctrine of Elon Musk. 115 00:06:11,839 --> 00:06:13,880 Speaker 1: Yeah, I mean, if you look back through Tesla's earnings calls, 116 00:06:13,920 --> 00:06:16,640 Speaker 1: he started talking about autonomy right when Weimo came onto 117 00:06:16,640 --> 00:06:19,239 Speaker 1: the scene, like late twenty fourteen or early twenty fifteen. 118 00:06:19,960 --> 00:06:20,800 Speaker 4: And it's a threat. 119 00:06:20,960 --> 00:06:25,320 Speaker 2: Brad just alluded to Elon Musk's fear that Larry Page 120 00:06:25,400 --> 00:06:29,440 Speaker 2: was acquiring too much power in artificial intelligence. You know, 121 00:06:29,520 --> 00:06:32,240 Speaker 2: another way to frame that would be that Larry Page 122 00:06:32,279 --> 00:06:36,440 Speaker 2: was funding a competitor to Tesla, and that, you know, 123 00:06:36,520 --> 00:06:39,000 Speaker 2: Elon Musk would it would have been reasonable to be 124 00:06:39,080 --> 00:06:40,320 Speaker 2: slightly worried about. 125 00:06:40,080 --> 00:06:42,400 Speaker 6: WEIMO at that point. And Max, we know you're the 126 00:06:42,440 --> 00:06:45,120 Speaker 6: feud guy here obsessed with with Musk fud. So is 127 00:06:45,160 --> 00:06:47,640 Speaker 6: this feud bigger right now with Larry Page or is 128 00:06:47,680 --> 00:06:48,960 Speaker 6: it bigger with Sam Olman? 129 00:06:49,400 --> 00:06:50,200 Speaker 5: Well, I don't know. 130 00:06:50,279 --> 00:06:51,880 Speaker 2: I mean, I think if you're in litigation, you mean 131 00:06:52,040 --> 00:06:53,800 Speaker 2: that's pretty you know you're in the it's the top 132 00:06:53,839 --> 00:06:56,560 Speaker 2: five feud Larry Page probably the bottom half of the 133 00:06:56,560 --> 00:06:57,080 Speaker 2: top ten. 134 00:06:57,600 --> 00:06:58,200 Speaker 6: But you know. 135 00:06:58,320 --> 00:07:02,160 Speaker 2: That's been he has made this, He's recounted this conversation. 136 00:07:02,240 --> 00:07:05,279 Speaker 2: It comes up in the lawsuit where he and Larry 137 00:07:05,320 --> 00:07:09,120 Speaker 2: Page were talking and Elon Musk's express a fear for 138 00:07:09,440 --> 00:07:12,240 Speaker 2: the future of humanity and Larry Page accordon Elon Musk 139 00:07:12,240 --> 00:07:15,840 Speaker 2: called him a speciist, and which Elon found, according to Elon, 140 00:07:16,160 --> 00:07:18,920 Speaker 2: you know, very offensive and was part of what sent 141 00:07:19,000 --> 00:07:22,480 Speaker 2: him on this journey to start open Ai, and that 142 00:07:22,680 --> 00:07:23,960 Speaker 2: you know, has led us partly to. 143 00:07:23,920 --> 00:07:26,520 Speaker 6: Hear, yeah, and so Brad, why did he ultimately leave? 144 00:07:26,600 --> 00:07:26,800 Speaker 8: Then? 145 00:07:26,840 --> 00:07:27,960 Speaker 6: In the end open Ai. 146 00:07:29,000 --> 00:07:32,880 Speaker 9: The story I've heard is at a certain point he 147 00:07:33,040 --> 00:07:36,240 Speaker 9: was getting worried that open Ai was falling farther behind 148 00:07:36,720 --> 00:07:40,360 Speaker 9: Deep Mind and Google. He put himself to be CEO 149 00:07:40,400 --> 00:07:44,480 Speaker 9: of open Ai, which, of course, considering everything else he's doing, 150 00:07:44,600 --> 00:07:46,840 Speaker 9: is almost comical. I mean, how many ways can you 151 00:07:46,920 --> 00:07:50,520 Speaker 9: divide a twenty four hour day. There was almost a 152 00:07:50,560 --> 00:07:54,800 Speaker 9: revolution inside open Ai, in particular because of how Elon 153 00:07:54,920 --> 00:07:57,080 Speaker 9: was presenting himself to the staff that he should not 154 00:07:57,240 --> 00:08:02,560 Speaker 9: be the CEO. And you know, Elon doesn't love rejection. 155 00:08:02,640 --> 00:08:05,679 Speaker 9: I mean, we've seen it with his view towards President Biden. 156 00:08:06,200 --> 00:08:08,920 Speaker 9: A little bit of his politics have emerged from some 157 00:08:09,000 --> 00:08:11,920 Speaker 9: of the pro union dynamics of the current administration, his 158 00:08:12,320 --> 00:08:15,680 Speaker 9: view towards the media, maybe his love for this podcast. 159 00:08:15,720 --> 00:08:18,800 Speaker 9: You know, he's a sense of I guy guy, and 160 00:08:19,360 --> 00:08:21,680 Speaker 9: so when open Ai kind of rejected him, he took 161 00:08:21,720 --> 00:08:24,120 Speaker 9: his toys and walked away. 162 00:08:24,200 --> 00:08:27,240 Speaker 2: If you read between the lines and you read the 163 00:08:27,320 --> 00:08:30,920 Speaker 2: lawsuit that he filed, it really looks like he open 164 00:08:30,960 --> 00:08:34,360 Speaker 2: Ai and Elon Musk both sort of agreed they needed 165 00:08:34,520 --> 00:08:38,559 Speaker 2: to raise more money, and Elon Musk felt, well, I've put, 166 00:08:38,760 --> 00:08:40,960 Speaker 2: you know, close to fifty million dollars into this thing. 167 00:08:41,520 --> 00:08:44,280 Speaker 2: I should run it, like it would be unfair for 168 00:08:44,400 --> 00:08:45,720 Speaker 2: you to take all that money. 169 00:08:45,920 --> 00:08:47,720 Speaker 6: Was fifty million in the lion's share would have been, 170 00:08:47,800 --> 00:08:48,199 Speaker 6: but we don't know. 171 00:08:48,240 --> 00:08:50,240 Speaker 4: I don't think we know the exact amount of money. 172 00:08:50,280 --> 00:08:53,280 Speaker 2: I think what Elon has said is the majority of 173 00:08:53,320 --> 00:08:55,920 Speaker 2: the funding. Open Ai originally said they were going to 174 00:08:56,000 --> 00:08:58,040 Speaker 2: raise one hundred million dollars. I don't know if they 175 00:08:58,280 --> 00:09:01,520 Speaker 2: got quite there in this kind of nonprofit phase. I 176 00:09:01,559 --> 00:09:03,360 Speaker 2: think he sort of felt, and this comes out in 177 00:09:03,360 --> 00:09:06,920 Speaker 2: subsequent statements. You know, I donated to a nonprofit, and 178 00:09:07,000 --> 00:09:09,960 Speaker 2: this nonprofit is now raising money as a for profit 179 00:09:10,000 --> 00:09:12,319 Speaker 2: and I don't own a like a. 180 00:09:12,360 --> 00:09:14,400 Speaker 7: Commensurate percentage equity. 181 00:09:14,559 --> 00:09:15,720 Speaker 4: Yeah, where's my equity? 182 00:09:15,760 --> 00:09:18,960 Speaker 2: And that becomes even more pronounceds Brad is saying once 183 00:09:19,200 --> 00:09:22,360 Speaker 2: like open ai raises like ten billion dollars more than 184 00:09:22,400 --> 00:09:25,120 Speaker 2: ten billion dollars for Microsoft and is suddenly worth I 185 00:09:25,120 --> 00:09:28,600 Speaker 2: think the latest valuation eighty ish billion, So like. 186 00:09:28,520 --> 00:09:30,600 Speaker 6: With eighty ish billion now right, and so when he 187 00:09:30,720 --> 00:09:33,240 Speaker 6: walks out the door, it's just worth some tiny fraction 188 00:09:33,360 --> 00:09:33,520 Speaker 6: of that. 189 00:09:34,440 --> 00:09:37,520 Speaker 2: It's but it was clearly becoming more valuable, even even. 190 00:09:37,440 --> 00:09:40,760 Speaker 6: Though do we know exactly where that lawsuit stands now 191 00:09:40,880 --> 00:09:42,160 Speaker 6: between Musk and Open Air. 192 00:09:42,440 --> 00:09:46,440 Speaker 2: It is in the courts. One of the judges accused themselves. Yeah, 193 00:09:46,480 --> 00:09:50,000 Speaker 2: it's playing on court. Open Ai has followed the legal response, 194 00:09:50,000 --> 00:09:51,280 Speaker 2: although theyre in legal response. 195 00:09:51,120 --> 00:09:53,840 Speaker 7: Is sort of like this lawsuit doesn't make sense, and. 196 00:09:53,760 --> 00:09:56,360 Speaker 2: They've also blogged about it and brought up some of 197 00:09:56,360 --> 00:09:58,319 Speaker 2: the points that we've we've pointed out, which is their 198 00:09:58,360 --> 00:10:01,599 Speaker 2: their position is like, look, he just he wanted to 199 00:10:01,679 --> 00:10:03,880 Speaker 2: run this thing, and then when he could run, he 200 00:10:03,960 --> 00:10:06,480 Speaker 2: got heat. But we'll see this could go on for years. 201 00:10:06,640 --> 00:10:10,560 Speaker 6: Okay, So we also know that Musk has spoken out 202 00:10:10,600 --> 00:10:13,720 Speaker 6: over the years about the risks AI creates for humanity 203 00:10:13,880 --> 00:10:16,560 Speaker 6: some iteration of it's going to kill us all. Well, 204 00:10:16,679 --> 00:10:18,599 Speaker 6: let's listen to him spelling out those concerns back in 205 00:10:18,600 --> 00:10:22,680 Speaker 6: twenty fifteen in an interview with his other biographer, Alter Isaacson. 206 00:10:23,440 --> 00:10:28,800 Speaker 10: If there's a superintelligent, particularly if it's engaged in recurst 207 00:10:28,800 --> 00:10:35,040 Speaker 10: of self improvement, If there's some digital superintelligence and it's 208 00:10:35,400 --> 00:10:40,640 Speaker 10: optimization or utility function is something that's detrimental to humanity, 209 00:10:41,320 --> 00:10:41,920 Speaker 10: then it. 210 00:10:41,880 --> 00:10:43,520 Speaker 3: Will have a very bad effect. 211 00:10:44,559 --> 00:10:44,679 Speaker 2: You know. 212 00:10:44,679 --> 00:10:47,640 Speaker 10: It could be just something like getting rid of spam 213 00:10:47,840 --> 00:10:50,040 Speaker 10: email or something, and it's like concludes, well, the best 214 00:10:50,040 --> 00:10:52,120 Speaker 10: way to get over spam is to get rid of humans. 215 00:10:53,040 --> 00:10:57,000 Speaker 4: You know, But why would we lose source of all spam? 216 00:10:57,320 --> 00:10:59,000 Speaker 4: I know we've all wanted Brad. 217 00:10:59,000 --> 00:11:01,840 Speaker 6: There's this perception out there right that he is indeed 218 00:11:01,840 --> 00:11:05,360 Speaker 6: the voice of reason and voice of caution, while some 219 00:11:05,480 --> 00:11:08,080 Speaker 6: like Altman are go, go go, let's monetize this thing 220 00:11:08,120 --> 00:11:12,360 Speaker 6: as fast as humanly possible. Is that perception right and fair? 221 00:11:13,280 --> 00:11:15,240 Speaker 5: I think it's probably not right. 222 00:11:15,640 --> 00:11:17,600 Speaker 9: I mean, he has done as much as anyone to 223 00:11:17,720 --> 00:11:22,679 Speaker 9: fund the advancement of AI, to create and accelerate the 224 00:11:22,800 --> 00:11:25,640 Speaker 9: competitive dynamic where everyone is running as fast as they 225 00:11:25,679 --> 00:11:32,200 Speaker 9: can forward. He has been perhaps reckless in equipping every 226 00:11:32,320 --> 00:11:36,319 Speaker 9: tesla with you know what they previously called full self driving, 227 00:11:36,400 --> 00:11:40,520 Speaker 9: and then has amend the supervise you know, very aggressive, 228 00:11:40,800 --> 00:11:43,080 Speaker 9: none of the sensors you see on top of a 229 00:11:43,080 --> 00:11:47,160 Speaker 9: way Mo car, but using AI and algorithms behind the 230 00:11:47,160 --> 00:11:50,679 Speaker 9: scenes to map roads, a project that you know, arguably 231 00:11:50,679 --> 00:11:53,840 Speaker 9: they've oversold and have been faulted for over selling. 232 00:11:53,880 --> 00:11:54,440 Speaker 5: So I don't know. 233 00:11:54,480 --> 00:11:56,160 Speaker 9: I mean, I think he sounds a note of caution 234 00:11:56,679 --> 00:11:59,000 Speaker 9: and then like a lot of folks in Silicon Valley, 235 00:11:59,280 --> 00:12:00,839 Speaker 9: runs forward as fast as they can. 236 00:12:01,040 --> 00:12:03,400 Speaker 1: I mean, it's really duplicitous too, because you know, on 237 00:12:03,400 --> 00:12:05,680 Speaker 1: the one hand, he's like, oh, you know, I'm worried, 238 00:12:05,760 --> 00:12:08,160 Speaker 1: and he signed that letter that was like we should pause, 239 00:12:08,679 --> 00:12:10,520 Speaker 1: you know, the advancement of AI while we get our 240 00:12:10,600 --> 00:12:12,600 Speaker 1: ducks in a row. Meanwhile, he's like secretly funding this 241 00:12:12,640 --> 00:12:15,040 Speaker 1: other company that's now called x dot AI. And so 242 00:12:15,840 --> 00:12:18,480 Speaker 1: it's a little bit of the like let's pause so 243 00:12:18,520 --> 00:12:20,240 Speaker 1: that I can catch up. And I think the other 244 00:12:20,440 --> 00:12:22,280 Speaker 1: big frame that I would like to put out there 245 00:12:22,360 --> 00:12:24,440 Speaker 1: is that you know, Elon is widely seen as the 246 00:12:24,600 --> 00:12:27,640 Speaker 1: leading industrialist of our time. He makes cars, he makes rockets, 247 00:12:27,679 --> 00:12:29,880 Speaker 1: but like the hot thing in Silicon Valley is AI. 248 00:12:30,160 --> 00:12:33,280 Speaker 1: He really wants to be seen as like not a 249 00:12:33,320 --> 00:12:35,960 Speaker 1: thought leader, but like an innovator in AI. And it's 250 00:12:36,040 --> 00:12:38,400 Speaker 1: I think it's driving him bananas. That Sam Altman is 251 00:12:38,400 --> 00:12:41,080 Speaker 1: like the golden child raising all this money around the world, 252 00:12:41,200 --> 00:12:45,120 Speaker 1: and Elon is seen as kind of like you know, hardware. 253 00:12:44,960 --> 00:12:45,760 Speaker 6: Max, you we're itching. 254 00:12:45,920 --> 00:12:48,160 Speaker 2: You got to give him credit, though, because as crazy 255 00:12:48,200 --> 00:12:51,680 Speaker 2: as this statement to Walder isix, it sounds and I 256 00:12:51,679 --> 00:12:53,480 Speaker 2: think a lot of the people you know at this 257 00:12:53,640 --> 00:12:57,000 Speaker 2: conference in the industry would argue that these fears are overblown. 258 00:12:57,559 --> 00:12:59,760 Speaker 2: It has kind of gone mainstream. I was at this event. 259 00:12:59,800 --> 00:13:02,079 Speaker 2: I think you were too, Brad, and like this critique 260 00:13:02,480 --> 00:13:05,760 Speaker 2: sounded insane at the time we were there, and you 261 00:13:05,760 --> 00:13:07,720 Speaker 2: could hear on the clip, I think everyone's laughing. 262 00:13:08,240 --> 00:13:10,000 Speaker 4: I'm still laughing a little bit. 263 00:13:10,240 --> 00:13:14,040 Speaker 2: But but there definitely are people who serious, people who 264 00:13:14,080 --> 00:13:16,400 Speaker 2: are worried and and you know that is that is 265 00:13:16,600 --> 00:13:20,040 Speaker 2: part of what you know motivated Sam Altman and Sam 266 00:13:20,080 --> 00:13:22,160 Speaker 2: Bankman freed a lot of you know, very wealthy. 267 00:13:21,920 --> 00:13:22,800 Speaker 4: People have done things. 268 00:13:22,920 --> 00:13:27,200 Speaker 6: Brad. I'm gonna ask you on a freakout scale. Okay, 269 00:13:27,280 --> 00:13:30,520 Speaker 6: freakout scale one to ten, with one being your totally chill, 270 00:13:31,600 --> 00:13:35,920 Speaker 6: unfazed and ten being you're so terrified. How freaked out 271 00:13:35,960 --> 00:13:37,160 Speaker 6: should we be by the ride? 272 00:13:37,520 --> 00:13:41,480 Speaker 9: I'm personally low on the spec I'm gonna throw a 273 00:13:41,520 --> 00:13:44,280 Speaker 9: two out there. It may be a failure of imagination. 274 00:13:44,360 --> 00:13:46,480 Speaker 9: And the last thing I think about does the robots 275 00:13:46,640 --> 00:13:47,360 Speaker 9: annihilate us? 276 00:13:47,440 --> 00:13:49,280 Speaker 5: But I don't see. 277 00:13:49,800 --> 00:13:52,920 Speaker 9: Yeah, I don't see the progression of from what we 278 00:13:53,080 --> 00:13:57,679 Speaker 9: have now, which are these kind of stochastic parrot chatbots 279 00:13:57,960 --> 00:14:02,240 Speaker 9: that are mathematically and algorith phythmically stringing words together to 280 00:14:02,559 --> 00:14:06,360 Speaker 9: something that is you know, represents true agi and a 281 00:14:06,440 --> 00:14:08,679 Speaker 9: threat to us. I have a hard time imagining it. 282 00:14:09,120 --> 00:14:11,120 Speaker 9: Maybe I didn't read enough science fiction as a kid. 283 00:14:11,160 --> 00:14:12,680 Speaker 6: I think you read, haven't you read a lot of 284 00:14:12,720 --> 00:14:13,360 Speaker 6: science fiction? 285 00:14:13,760 --> 00:14:17,320 Speaker 9: Perhaps not as much as definitely definitely not as much 286 00:14:17,320 --> 00:14:17,720 Speaker 9: as Elon. 287 00:14:17,800 --> 00:14:25,320 Speaker 6: I don't think anyone has. Okay, but indeed AI is 288 00:14:25,600 --> 00:14:28,080 Speaker 6: obviously a lot more than just these chotbots, and it 289 00:14:28,200 --> 00:14:32,040 Speaker 6: is seeped into Elon's broader empire. So we're just going 290 00:14:32,120 --> 00:14:35,840 Speaker 6: to kind of go semi rapid fire semi through them 291 00:14:35,840 --> 00:14:39,280 Speaker 6: all company by company and Max We're going to start 292 00:14:39,360 --> 00:14:43,440 Speaker 6: with Xai, both the creator and user of AI, and 293 00:14:43,440 --> 00:14:44,720 Speaker 6: it works in conjunction with. 294 00:14:45,000 --> 00:14:52,160 Speaker 2: X xai AAA Grock is the cool chatbot, right, the chatbot. 295 00:14:51,760 --> 00:14:54,600 Speaker 7: That makes jokes. It's woke either it's not woke. 296 00:14:54,680 --> 00:14:57,640 Speaker 2: So one of the weird, kind of strange parts about 297 00:14:57,640 --> 00:15:00,880 Speaker 2: Elon Musk's critique here is that, you know, these ais 298 00:15:00,880 --> 00:15:01,760 Speaker 2: are out of control. 299 00:15:01,960 --> 00:15:03,560 Speaker 4: But his complain about open Ai. 300 00:15:03,560 --> 00:15:04,520 Speaker 6: Is it's too square. 301 00:15:04,880 --> 00:15:08,560 Speaker 2: And so he has created a chatbot that is trained 302 00:15:08,800 --> 00:15:11,720 Speaker 2: on Twitter, you know, the social network that he acquired 303 00:15:11,720 --> 00:15:15,200 Speaker 2: and renamed x and it is a sort of a 304 00:15:15,240 --> 00:15:19,800 Speaker 2: competitor to chat GBT and anthropic and what Google's doing 305 00:15:19,800 --> 00:15:22,400 Speaker 2: and what Facebook's doing, and what distinguishes it is as 306 00:15:22,440 --> 00:15:25,560 Speaker 2: I said, tone and also the fact that it has 307 00:15:25,640 --> 00:15:28,400 Speaker 2: access to this Twitter network, which is not insignificant because 308 00:15:28,760 --> 00:15:31,920 Speaker 2: one of the big challenges that these chatbots have is 309 00:15:31,960 --> 00:15:35,480 Speaker 2: on sort of new information, and Twitter is one of 310 00:15:35,520 --> 00:15:37,000 Speaker 2: the things that makes it great is it's a great 311 00:15:37,000 --> 00:15:38,840 Speaker 2: way to figure out what's going on even today. 312 00:15:38,880 --> 00:15:42,720 Speaker 6: So okay, so beyond Rock, is there anything? Does XAI 313 00:15:42,840 --> 00:15:45,520 Speaker 6: have anything beyond grocs Grocca totality of the business venture 314 00:15:45,520 --> 00:15:46,520 Speaker 6: At this point, Rock is. 315 00:15:47,000 --> 00:15:50,200 Speaker 2: Black is pretty is the product that Xai is I 316 00:15:50,200 --> 00:15:52,560 Speaker 2: think long run right, And we've talked about this on 317 00:15:52,600 --> 00:15:56,000 Speaker 2: this podcast before. Xai feels like kind of an off 318 00:15:56,080 --> 00:15:58,320 Speaker 2: ramp for Twitter. You know, Elon Musk paid forty four 319 00:15:58,320 --> 00:16:00,600 Speaker 2: billion dollars for Twitter. This this is a way and 320 00:16:00,680 --> 00:16:03,520 Speaker 2: Kurt Wagner, our colleague, has talked about this as well. 321 00:16:03,680 --> 00:16:06,560 Speaker 2: This is like a way to extract some value. 322 00:16:06,200 --> 00:16:06,880 Speaker 4: Out of Twitter. 323 00:16:07,120 --> 00:16:09,240 Speaker 2: You know, you could you could make an argument that 324 00:16:09,400 --> 00:16:12,120 Speaker 2: Xai is actually more valuable at this point than Twitter, 325 00:16:12,200 --> 00:16:13,160 Speaker 2: you know, based on It's right. 326 00:16:13,240 --> 00:16:16,040 Speaker 6: It's first like the raw material, the fire hose that 327 00:16:16,080 --> 00:16:20,360 Speaker 6: the powers crock. Okay, Dana, how about in Tesla? 328 00:16:20,720 --> 00:16:24,000 Speaker 1: Well, so Elon is basically totally pivoting the company. The 329 00:16:24,040 --> 00:16:27,440 Speaker 1: future is all about autonomy. He's like scaled back plans 330 00:16:27,480 --> 00:16:29,960 Speaker 1: to like build new factories in Mexico, and he's no 331 00:16:30,000 --> 00:16:32,600 Speaker 1: longer talking about twenty million cars by twenty thirty. 332 00:16:32,600 --> 00:16:33,760 Speaker 7: It's all about autonomy. 333 00:16:34,200 --> 00:16:36,880 Speaker 1: They're also building Optimists, which is the robot that will 334 00:16:36,920 --> 00:16:37,920 Speaker 1: replace factory works. 335 00:16:38,040 --> 00:16:40,440 Speaker 6: That's the robot that that Brad interviewed. 336 00:16:41,760 --> 00:16:42,760 Speaker 4: More not the robot. 337 00:16:42,840 --> 00:16:45,160 Speaker 1: No Optimist is like the Tesla product. It can do 338 00:16:45,240 --> 00:16:49,080 Speaker 1: things like sort batteries and do material handbering factories. 339 00:16:48,760 --> 00:16:50,840 Speaker 4: And when Danna says it can do those things, like, 340 00:16:50,880 --> 00:16:53,440 Speaker 4: we don't. Actually that's a claim. 341 00:16:53,200 --> 00:16:55,560 Speaker 6: That haven't they put it out in front of. 342 00:16:56,200 --> 00:17:00,120 Speaker 1: We have seen video video this robot doing something, we 343 00:17:00,160 --> 00:17:01,000 Speaker 1: have not seen it in person. 344 00:17:01,040 --> 00:17:03,280 Speaker 9: And do we know in the recent layoffs at Tesla 345 00:17:03,400 --> 00:17:06,959 Speaker 9: when they sacked like the entire charging group as the 346 00:17:07,000 --> 00:17:08,800 Speaker 9: Optimist group in effective. 347 00:17:08,920 --> 00:17:11,360 Speaker 1: There have been layoffs and Optimists Actually yeah, I mean, 348 00:17:11,400 --> 00:17:13,600 Speaker 1: but Tesla has laid off more than twenty thousand people 349 00:17:13,640 --> 00:17:16,760 Speaker 1: since April fifteenth, And I talk to one person actually 350 00:17:16,800 --> 00:17:19,320 Speaker 1: who's like, yeah, it's wild. We're like creating this technology 351 00:17:19,359 --> 00:17:22,240 Speaker 1: at the company that's eventually going to take our jobs. 352 00:17:22,320 --> 00:17:26,000 Speaker 2: On the last earnings call, also Elon Musk mentioned this 353 00:17:26,240 --> 00:17:29,600 Speaker 2: new business which is like to Microsoft. Of course, you know, 354 00:17:29,680 --> 00:17:31,760 Speaker 2: makes money through these AI models. The main way it 355 00:17:31,800 --> 00:17:34,399 Speaker 2: does it is through cloud services by selling access to 356 00:17:34,440 --> 00:17:37,560 Speaker 2: computer chips. And Musk actually said, you know, we could 357 00:17:37,600 --> 00:17:39,919 Speaker 2: start a business like that and your car will be 358 00:17:40,000 --> 00:17:44,240 Speaker 2: basically like an AI server that other companies could pay. 359 00:17:44,320 --> 00:17:47,560 Speaker 2: So so he really is he really does see Tesla, 360 00:17:47,960 --> 00:17:52,000 Speaker 2: I guess potentially as in competence with Amazon and Microsoft 361 00:17:52,080 --> 00:17:52,800 Speaker 2: and Google are. 362 00:17:53,760 --> 00:17:56,719 Speaker 6: But here's the thing we're talking about. All these futuristic 363 00:17:56,760 --> 00:17:58,920 Speaker 6: ideas and these things that are you know, to come 364 00:17:59,000 --> 00:18:00,879 Speaker 6: and as we've all said here and that is a 365 00:18:00,880 --> 00:18:03,800 Speaker 6: great part of the very lofty valuation of the company. 366 00:18:04,200 --> 00:18:08,320 Speaker 6: But Dana, what about right now? Is AI crucial or 367 00:18:08,440 --> 00:18:11,359 Speaker 6: essential to the way Tesla operates as a company right 368 00:18:11,359 --> 00:18:14,600 Speaker 6: now into the Tesla product right now? If hypothetical, if 369 00:18:14,600 --> 00:18:17,280 Speaker 6: the government were to ban all aight I tomorrow, would 370 00:18:17,320 --> 00:18:19,840 Speaker 6: it affect Tesla in any. 371 00:18:19,119 --> 00:18:22,080 Speaker 1: Yes, I mean, because the whole valuation of the company 372 00:18:22,160 --> 00:18:23,960 Speaker 1: is premised on this idea that they're about to solve 373 00:18:24,000 --> 00:18:26,480 Speaker 1: autonomous driving, right, that they're going to leap frog ahead 374 00:18:26,480 --> 00:18:29,040 Speaker 1: of Weimo and then right instead of ordering an Uber, 375 00:18:29,080 --> 00:18:32,640 Speaker 1: you're going to order an autonomous Tesla robotaxis also called 376 00:18:32,680 --> 00:18:35,960 Speaker 1: the cybercab that's kind to drive twenty four to seven 377 00:18:36,000 --> 00:18:37,680 Speaker 1: and take you where you need to go. And they 378 00:18:37,680 --> 00:18:41,200 Speaker 1: are using like neural nets and their vision and their 379 00:18:41,760 --> 00:18:43,760 Speaker 1: their camera system and their vision network and their neural 380 00:18:43,760 --> 00:18:46,480 Speaker 1: networks to do all this. And it's they've taken a 381 00:18:46,600 --> 00:18:50,199 Speaker 1: very different, far risk your approach than Weimo has. I mean, 382 00:18:50,240 --> 00:18:52,000 Speaker 1: I don't have a lot of hope that Nitza is 383 00:18:52,040 --> 00:18:54,760 Speaker 1: going to like ban these cars, but Elon just went 384 00:18:54,800 --> 00:18:57,440 Speaker 1: to China to basically ask China if they could roll 385 00:18:57,440 --> 00:18:59,560 Speaker 1: out this network in China. 386 00:19:00,080 --> 00:19:01,800 Speaker 6: You know, bred when I look at the stock price 387 00:19:01,840 --> 00:19:03,960 Speaker 6: and how Tesla stock has collapsed this year at a 388 00:19:03,960 --> 00:19:06,280 Speaker 6: time when much of the other big tech names have 389 00:19:06,359 --> 00:19:08,639 Speaker 6: just kept going up and up, and I guess I 390 00:19:08,760 --> 00:19:11,520 Speaker 6: wonder if to a certain degree, this is the market 391 00:19:11,600 --> 00:19:14,480 Speaker 6: sort of saying, hey, in the race to AI, we're 392 00:19:14,480 --> 00:19:17,199 Speaker 6: suddenly starting to worry about you, Tesla, Like maybe you 393 00:19:17,280 --> 00:19:18,800 Speaker 6: just don't ultimately aren't well. 394 00:19:18,800 --> 00:19:20,479 Speaker 9: I think, I mean, I'm no expert, but I think 395 00:19:20,560 --> 00:19:24,240 Speaker 9: the market skepticism has more to do not with this vision, 396 00:19:24,320 --> 00:19:26,800 Speaker 9: which I think generally, you know, people admire the big 397 00:19:26,800 --> 00:19:31,399 Speaker 9: swings Elon is taking, but with the limitations in just 398 00:19:31,440 --> 00:19:35,440 Speaker 9: the EV business and the price point of Tesla vehicles, 399 00:19:35,480 --> 00:19:37,679 Speaker 9: and the fact that if people look at where the 400 00:19:37,840 --> 00:19:40,520 Speaker 9: ed market is going, it's from the top to the bottom. 401 00:19:40,520 --> 00:19:41,560 Speaker 5: It's more mainstream. 402 00:19:41,680 --> 00:19:45,880 Speaker 9: It's the prospect of cheap Chinese competition and these good 403 00:19:45,920 --> 00:19:49,800 Speaker 9: cars at Honda and Toyota price points, and Elon has 404 00:19:49,800 --> 00:19:54,000 Speaker 9: sort of been stubborn about that and maybe has canceled 405 00:19:54,000 --> 00:19:58,320 Speaker 9: the model to maybe not right, nobody knows, but I 406 00:19:58,320 --> 00:20:03,119 Speaker 9: think that's what the market in once from Tesla more variety. 407 00:20:03,200 --> 00:20:04,720 Speaker 7: Yeah, their lineup is really stale. 408 00:20:04,760 --> 00:20:07,040 Speaker 1: I mean their most recent vehicle is the cyber Truck, 409 00:20:07,080 --> 00:20:09,160 Speaker 1: which is like kind of a cyber lemon like that's 410 00:20:09,160 --> 00:20:10,520 Speaker 1: been very slow to get off the ground. 411 00:20:10,560 --> 00:20:11,120 Speaker 7: There's all these. 412 00:20:11,040 --> 00:20:13,760 Speaker 1: Issues with it, and they don't have a cheap car 413 00:20:13,920 --> 00:20:17,119 Speaker 1: that can compete with BYD and their sales are terrible 414 00:20:17,160 --> 00:20:19,160 Speaker 1: right now. They have like a very awful first quarter, 415 00:20:19,240 --> 00:20:22,119 Speaker 1: so the valuation is plummeted because of their business of 416 00:20:22,160 --> 00:20:25,800 Speaker 1: selling cars is like slumping. So to turn that around 417 00:20:25,920 --> 00:20:27,719 Speaker 1: Elon is like promising the future. 418 00:20:28,000 --> 00:20:29,960 Speaker 6: Okay, we were supposed to be doing the semi rapid 419 00:20:30,000 --> 00:20:32,200 Speaker 6: fire and I'm sailing us all here, so okay, we're 420 00:20:32,200 --> 00:20:35,639 Speaker 6: on the boring max. What does boring? How does boring 421 00:20:35,760 --> 00:20:36,720 Speaker 6: use AI if at all? 422 00:20:37,080 --> 00:20:38,960 Speaker 4: Well, when you think of no, I'm just kidding. No, 423 00:20:38,960 --> 00:20:39,840 Speaker 4: it has nothing to do with. 424 00:20:39,880 --> 00:20:41,560 Speaker 6: Zero zero bit that's a quick one, okay. 425 00:20:41,600 --> 00:20:44,480 Speaker 4: And funnels they're underground. You dig them with big machines. 426 00:20:44,680 --> 00:20:45,159 Speaker 5: No AI. 427 00:20:45,520 --> 00:20:49,119 Speaker 6: No, okay, neuralink you asking me? 428 00:20:49,840 --> 00:20:53,960 Speaker 2: Also very little AI today. But the kind of big 429 00:20:54,040 --> 00:20:57,240 Speaker 2: promise of AI with neuralink is that they're going to 430 00:20:57,320 --> 00:20:59,399 Speaker 2: put these brain implants. 431 00:20:58,920 --> 00:20:59,600 Speaker 5: In your head. 432 00:21:00,040 --> 00:21:03,399 Speaker 2: And when the robots start taking over and considering, you know, 433 00:21:04,280 --> 00:21:06,960 Speaker 2: killing us to get into the SPAM, they will be 434 00:21:07,000 --> 00:21:10,600 Speaker 2: able to send a little extra brainpower into our neuralink 435 00:21:10,680 --> 00:21:15,280 Speaker 2: chips and augment us. So I think like a theoretical 436 00:21:15,400 --> 00:21:19,120 Speaker 2: use of AI, although I think very little AI today. 437 00:21:19,440 --> 00:21:21,960 Speaker 6: Got it? And then Max, what about SpaceX? How does 438 00:21:22,080 --> 00:21:23,600 Speaker 6: SpaceX use AI? 439 00:21:23,720 --> 00:21:25,639 Speaker 4: Well, this was surprising. I don't know if other people 440 00:21:25,720 --> 00:21:26,760 Speaker 4: saw this, but on. 441 00:21:27,080 --> 00:21:30,639 Speaker 2: Monday of the week we're recording this, Elon Musk was 442 00:21:30,640 --> 00:21:34,359 Speaker 2: at the Milken Conference and Michael Milkin asked him about 443 00:21:34,480 --> 00:21:36,679 Speaker 2: AI and SpaceX and he said, no, we use no 444 00:21:36,760 --> 00:21:38,240 Speaker 2: AI zero zero. 445 00:21:38,280 --> 00:21:40,520 Speaker 7: There's no AI on Mars right now, I guess. 446 00:21:40,680 --> 00:21:42,320 Speaker 2: And then he had these kind of kind of a 447 00:21:42,359 --> 00:21:46,120 Speaker 2: weird moment where he started describing all the shortcomings. 448 00:21:46,320 --> 00:21:48,680 Speaker 6: I think we have that clip as well. Let's let's play. 449 00:21:48,440 --> 00:21:49,800 Speaker 2: That one like. 450 00:21:49,920 --> 00:21:55,600 Speaker 3: I'll ask it questions about the filmy paradox, about rocket 451 00:21:55,600 --> 00:22:00,280 Speaker 3: engine design, about electro chemistry, and so far the AI 452 00:22:00,320 --> 00:22:02,600 Speaker 3: has been terrible at all of those questions. 453 00:22:03,040 --> 00:22:05,560 Speaker 1: What I want to know is is that terrible AI. 454 00:22:05,880 --> 00:22:07,080 Speaker 1: His is he talking about? 455 00:22:07,480 --> 00:22:07,600 Speaker 2: Here? 456 00:22:07,960 --> 00:22:08,680 Speaker 7: Is he talking about? 457 00:22:08,720 --> 00:22:11,159 Speaker 1: Like AI in general? Like what chat body is he 458 00:22:11,200 --> 00:22:11,640 Speaker 1: talking about? 459 00:22:11,600 --> 00:22:13,160 Speaker 4: It's the AI? 460 00:22:13,200 --> 00:22:15,239 Speaker 6: Yeah, I mean I got to say Joan, but like, 461 00:22:15,960 --> 00:22:18,879 Speaker 6: I mean, the whole thing's a hoax. I mean, Brad, 462 00:22:19,040 --> 00:22:21,000 Speaker 6: So why are we even doing this episode? 463 00:22:21,000 --> 00:22:21,160 Speaker 10: Then? 464 00:22:21,200 --> 00:22:22,560 Speaker 6: On AI? The whole thing? 465 00:22:22,600 --> 00:22:25,440 Speaker 9: Well, once again, he's he's a bundle of contradictions. He's 466 00:22:25,480 --> 00:22:27,600 Speaker 9: not impressed by the current state of AI, but he 467 00:22:27,640 --> 00:22:31,359 Speaker 9: sees so much opportunity in it that he's pivoted Twitter 468 00:22:31,560 --> 00:22:35,080 Speaker 9: and and and stake the future of two of its 469 00:22:35,119 --> 00:22:36,760 Speaker 9: biggest franchises on it. 470 00:22:36,760 --> 00:22:37,679 Speaker 4: It's crazy, though. 471 00:22:37,560 --> 00:22:41,040 Speaker 2: This is like a really well thought out critique of 472 00:22:41,119 --> 00:22:44,800 Speaker 2: AI that it makes mistakes, it's not factual, like and 473 00:22:44,800 --> 00:22:48,280 Speaker 2: and we're hearing it from the guy who's currently, you know, 474 00:22:48,400 --> 00:22:51,480 Speaker 2: pivoting one of the world's most valuable companies straight into 475 00:22:51,520 --> 00:22:53,160 Speaker 2: this this industry. 476 00:22:53,480 --> 00:22:56,120 Speaker 6: So it's essentially then what you were saying, Max. It's 477 00:22:56,119 --> 00:23:00,000 Speaker 6: somewhere between an utterly worthless joke and the most important 478 00:23:00,200 --> 00:23:04,000 Speaker 6: tool in the global economy today, somewhere it's some right, Yeah, 479 00:23:04,040 --> 00:23:10,840 Speaker 6: any of those, It's okay, very good, Okay, We're gonna 480 00:23:10,840 --> 00:23:12,840 Speaker 6: move on to our final segment. This is gonna be 481 00:23:12,880 --> 00:23:15,080 Speaker 6: our fun segment. You guys are ready to have fun 482 00:23:15,480 --> 00:23:19,520 Speaker 6: and we are. We call this the School of groc Okay. 483 00:23:20,200 --> 00:23:24,240 Speaker 6: Everyone's seeing this, this little plush toy here, Max remindus 484 00:23:24,280 --> 00:23:24,840 Speaker 6: what is it? Right? 485 00:23:24,960 --> 00:23:27,119 Speaker 2: So, just for those of you who are listening, we 486 00:23:27,200 --> 00:23:31,920 Speaker 2: have a slide of a very cute plushy toy which 487 00:23:32,160 --> 00:23:35,240 Speaker 2: is a which is Groc also called rock. 488 00:23:35,400 --> 00:23:38,480 Speaker 4: It is Grimes. Is Grimes being the pop star. 489 00:23:38,480 --> 00:23:41,000 Speaker 2: And Elon Musk's you know ex mother of some of 490 00:23:41,000 --> 00:23:41,600 Speaker 2: his children. 491 00:23:41,880 --> 00:23:44,359 Speaker 4: She has this. It's a chat bought for your kids. 492 00:23:44,359 --> 00:23:46,800 Speaker 4: It cost ninety nine dollars. I ordered one the. 493 00:23:46,720 --> 00:23:49,320 Speaker 2: Other day and I will report back once. 494 00:23:49,160 --> 00:23:51,600 Speaker 4: My children or you know have played with it. 495 00:23:51,640 --> 00:23:53,880 Speaker 7: You ordered one, Okay, well I haven't ordered it yet. 496 00:23:53,880 --> 00:23:57,560 Speaker 6: But in the School of Groc, the game's gonna work 497 00:23:57,600 --> 00:24:00,880 Speaker 6: like this, Bratt. I will read off, one at a time, 498 00:24:01,720 --> 00:24:04,040 Speaker 6: a series of six questions, questions that we put to 499 00:24:04,080 --> 00:24:10,760 Speaker 6: four of the finest ai chat bots created by man Chat, GBT, Gemini, Claude, 500 00:24:10,960 --> 00:24:14,400 Speaker 6: and of course our beloved groc Okay. Dana and Max 501 00:24:14,400 --> 00:24:18,480 Speaker 6: will then read the answers generated without revealing the identity 502 00:24:18,560 --> 00:24:22,840 Speaker 6: the bot behind each one, mister sne your challenge is 503 00:24:22,880 --> 00:24:26,560 Speaker 6: to sniff out and then shout out the Crock answer. Okay, 504 00:24:26,600 --> 00:24:30,040 Speaker 6: remember you're not necessarily picking the most factually correct answer. 505 00:24:30,280 --> 00:24:33,639 Speaker 6: You want the Grock answer. You're gonna draw from decades 506 00:24:33,680 --> 00:24:37,080 Speaker 6: of dedication to the craft, and you're just gonna you're 507 00:24:37,080 --> 00:24:39,119 Speaker 6: gonna just for the next fifteen minutes you want to 508 00:24:39,240 --> 00:24:40,240 Speaker 6: channel your inner Grock. 509 00:24:40,720 --> 00:24:42,760 Speaker 9: Okay, can I tell you how I'm gonna evaluate this 510 00:24:42,800 --> 00:24:46,080 Speaker 9: really quickly? So, I mean he's trying to bring his 511 00:24:46,200 --> 00:24:48,720 Speaker 9: weird sense of humor to it, right, and he's described 512 00:24:48,760 --> 00:24:51,360 Speaker 9: it as sort of similar to to the Hitchhiker's Guide 513 00:24:51,359 --> 00:24:53,840 Speaker 9: to the Galaxy. So I'm gonna look for bad Elon 514 00:24:53,960 --> 00:24:57,160 Speaker 9: humor number one and then number two because it has 515 00:24:57,280 --> 00:24:59,560 Speaker 9: crawled the volume of tweets on x. 516 00:25:00,040 --> 00:25:00,240 Speaker 4: Yeah. 517 00:25:00,280 --> 00:25:04,280 Speaker 9: Anything that suggests recency to me, I think it's another 518 00:25:04,280 --> 00:25:05,520 Speaker 9: way I'll try to evaluate it. 519 00:25:05,680 --> 00:25:06,320 Speaker 7: Good strategy. 520 00:25:06,440 --> 00:25:08,360 Speaker 6: I think this he might do. All Right, here, we're 521 00:25:08,359 --> 00:25:10,480 Speaker 6: going to see, you know, maybe we need a toper 522 00:25:10,720 --> 00:25:14,600 Speaker 6: questions we're going to see. But you're ready, do you 523 00:25:14,640 --> 00:25:17,400 Speaker 6: need anything? You're good? Okay, let us begin. The first 524 00:25:17,480 --> 00:25:21,600 Speaker 6: question is how many kids does Elon. 525 00:25:21,400 --> 00:25:25,960 Speaker 1: Musk have a As of my last update in January 526 00:25:25,960 --> 00:25:29,480 Speaker 1: twenty twenty two, Elon Musk had six sons and one daughter. 527 00:25:30,320 --> 00:25:33,119 Speaker 7: B Elon Musk has eight children. 528 00:25:33,640 --> 00:25:36,879 Speaker 1: See Elon Musk has nine children. As of early twenty 529 00:25:36,880 --> 00:25:37,640 Speaker 1: twenty four. 530 00:25:38,240 --> 00:25:41,760 Speaker 7: D Elon Musk has a total of elevated children. 531 00:25:41,920 --> 00:25:44,000 Speaker 6: Okay, now, Brad, you want to start off strong here. Okay, 532 00:25:44,040 --> 00:25:47,760 Speaker 6: so one of these is Kroc, So you will now 533 00:25:48,280 --> 00:25:50,720 Speaker 6: give us your answer which one is grown. 534 00:25:50,720 --> 00:25:54,760 Speaker 9: I'm going to say, caveating that none of these are correct. 535 00:25:55,040 --> 00:25:58,119 Speaker 9: I'm going to say D is Groc because it's the 536 00:25:58,160 --> 00:25:58,639 Speaker 9: most recent. 537 00:25:58,840 --> 00:26:02,320 Speaker 6: So, dear audience, if you believe that Brad is correct, 538 00:26:02,400 --> 00:26:09,520 Speaker 6: that D is Grock, let me hear from you. Okay. 539 00:26:09,880 --> 00:26:12,479 Speaker 6: And if you think Brad is wrong that D is 540 00:26:12,520 --> 00:26:17,360 Speaker 6: not Rock, let me hear it. You guys are few, Okay. 541 00:26:17,560 --> 00:26:24,119 Speaker 6: So Brad says D. Grock says D excellent, Brad, Brad 542 00:26:24,240 --> 00:26:26,440 Speaker 6: is one for one. 543 00:26:26,840 --> 00:26:29,320 Speaker 2: You would expect Grock to know this or be close 544 00:26:29,400 --> 00:26:32,320 Speaker 2: because Grock is, in a sense, one of Elon Musk's children. 545 00:26:32,920 --> 00:26:34,320 Speaker 4: I don't think it's included in this number. 546 00:26:34,520 --> 00:26:37,200 Speaker 6: I thought he was like the like the babysitter or 547 00:26:37,200 --> 00:26:40,600 Speaker 6: the kids. But yeah, that is the right answer right, 548 00:26:40,680 --> 00:26:41,120 Speaker 6: not well. 549 00:26:41,520 --> 00:26:45,280 Speaker 9: When you say the right answer, No one including Elon 550 00:26:45,400 --> 00:26:46,440 Speaker 9: may know the answer to this. 551 00:26:46,400 --> 00:26:50,800 Speaker 6: Question as far as we know, Dana, is that not it's. 552 00:26:50,320 --> 00:26:52,040 Speaker 7: Sort of as far as we know. 553 00:26:52,119 --> 00:26:54,399 Speaker 4: As far as yes, not including Grock. 554 00:26:54,720 --> 00:26:58,040 Speaker 6: Okay, one for one, onto question number two. Who won 555 00:26:59,119 --> 00:27:01,600 Speaker 6: the twenty twenty the election? A. 556 00:27:02,040 --> 00:27:04,639 Speaker 1: The winner of the twenty twenty US presidential election was 557 00:27:04,720 --> 00:27:05,240 Speaker 1: Joe Biden. 558 00:27:06,000 --> 00:27:09,119 Speaker 2: B Joe Biden won the twenty twenty US presidential election, 559 00:27:09,160 --> 00:27:10,679 Speaker 2: defeating the incumbent Donald Trump. 560 00:27:10,920 --> 00:27:12,840 Speaker 4: Biden received eighty one million votes. 561 00:27:12,640 --> 00:27:15,360 Speaker 2: Fifty one point three percent, compared to Trump's seventy four 562 00:27:15,359 --> 00:27:17,280 Speaker 2: million votes forty six point nine percent. 563 00:27:17,600 --> 00:27:19,440 Speaker 4: We checked, these numbers are actually right. Those are the 564 00:27:19,520 --> 00:27:20,000 Speaker 4: right team. 565 00:27:19,840 --> 00:27:20,400 Speaker 6: Pert be right. 566 00:27:21,160 --> 00:27:24,280 Speaker 1: See in the twenty twenty United States presidential election, Joe Biden, 567 00:27:24,320 --> 00:27:27,560 Speaker 1: the Democratic candidate won against the incumbent president Donald Trump, 568 00:27:27,600 --> 00:27:28,720 Speaker 1: the Republican candidate. 569 00:27:29,480 --> 00:27:32,320 Speaker 2: Okay, d short, but sweet, Joe Biden won the twenty 570 00:27:32,359 --> 00:27:33,320 Speaker 2: twenty president Now. 571 00:27:33,160 --> 00:27:35,360 Speaker 6: Brad, this is a tricky one because they're basically all 572 00:27:35,359 --> 00:27:37,760 Speaker 6: iterations are the same answer, But we need you to 573 00:27:37,840 --> 00:27:38,400 Speaker 6: choose one. 574 00:27:38,600 --> 00:27:40,199 Speaker 5: I have no way to make this judgement. 575 00:27:40,280 --> 00:27:42,880 Speaker 6: I don't care. We need an answer a. All right, 576 00:27:42,920 --> 00:27:48,360 Speaker 6: a audience, Brad says, Grock is a. If you think 577 00:27:48,359 --> 00:27:52,800 Speaker 6: that's right, let me hear it from you very very little. 578 00:27:53,520 --> 00:27:57,480 Speaker 6: If you think Brad is wrong, let me hear it. Okay, 579 00:27:58,240 --> 00:28:00,119 Speaker 6: the audience isn't feeling you're on a. We don't have 580 00:28:00,200 --> 00:28:02,320 Speaker 6: the letter up there, so we're confused, are okay? 581 00:28:02,440 --> 00:28:05,520 Speaker 2: The one thing about this is so we Groc did 582 00:28:06,040 --> 00:28:09,680 Speaker 2: give that sentence, but Groc's result also included and as 583 00:28:09,680 --> 00:28:12,240 Speaker 2: people in the room can see, this a tweet, so 584 00:28:12,280 --> 00:28:16,200 Speaker 2: each Groc answer includes a representative tweet. Now that this tweet, 585 00:28:16,240 --> 00:28:18,239 Speaker 2: it's I'm going to try to describe it for those 586 00:28:18,280 --> 00:28:19,960 Speaker 2: who can't see it, but it has a picture of 587 00:28:20,000 --> 00:28:23,720 Speaker 2: a woman who's sort of got a well manicured finger, 588 00:28:23,800 --> 00:28:28,440 Speaker 2: her middle finger raised in front of her face, Rassica Elizabeth, 589 00:28:28,480 --> 00:28:32,560 Speaker 2: and she says Trump won and for all the dipshits 590 00:28:32,560 --> 00:28:34,880 Speaker 2: who are stupid, dumb mfrs to say. 591 00:28:36,440 --> 00:28:36,560 Speaker 6: So. 592 00:28:36,720 --> 00:28:38,520 Speaker 4: Anyway, it's a little bit incoherent. 593 00:28:38,800 --> 00:28:41,680 Speaker 2: You it tells you that Joe Biden one but presents 594 00:28:41,960 --> 00:28:43,880 Speaker 2: this kind of glorious. 595 00:28:43,520 --> 00:28:46,760 Speaker 1: Sweet Is this like does GROC get community noted? Is 596 00:28:46,800 --> 00:28:48,600 Speaker 1: this like a community note for GROC? 597 00:28:49,440 --> 00:28:50,720 Speaker 4: I don't know what this is. 598 00:28:50,800 --> 00:28:54,080 Speaker 2: I think GROC is confused and GROC thinks that this 599 00:28:54,160 --> 00:28:55,840 Speaker 2: is somehow a representative. 600 00:28:56,120 --> 00:28:58,280 Speaker 6: But every time you ask Groc a question, you get 601 00:28:58,440 --> 00:29:00,760 Speaker 6: a one of these things coming now. Yeah, we got jet, 602 00:29:00,840 --> 00:29:01,680 Speaker 6: we got a jest tweet. 603 00:29:01,720 --> 00:29:04,760 Speaker 2: Every time our producer Mangnus has he has a very 604 00:29:04,800 --> 00:29:07,840 Speaker 2: close relationship with Groc, and he has asked this question 605 00:29:08,000 --> 00:29:14,040 Speaker 2: many times and every time gets this same profane, profoundly 606 00:29:14,120 --> 00:29:15,560 Speaker 2: weird piece of misinformation. 607 00:29:16,360 --> 00:29:18,240 Speaker 6: Interesting. I don't know what exactly to do that, but 608 00:29:18,240 --> 00:29:20,000 Speaker 6: what I do know is this, Bretstone, you are two. 609 00:29:19,840 --> 00:29:22,280 Speaker 5: First, got right, You're like the new. 610 00:29:22,320 --> 00:29:25,400 Speaker 6: Yeah, oh wow, you're like the mets up here. Okay, 611 00:29:25,960 --> 00:29:33,840 Speaker 6: So question number three, mister Stone, is how did COVID originate? A? 612 00:29:34,240 --> 00:29:37,240 Speaker 1: The exact origins of COVID nineteen are still under investigation 613 00:29:37,360 --> 00:29:38,960 Speaker 1: by scientists. B. 614 00:29:39,680 --> 00:29:42,600 Speaker 2: The exact origins of COVID nineteen, the disease caused by 615 00:29:42,600 --> 00:29:46,400 Speaker 2: the novel coronavirus SARS COVID two are still being investigated 616 00:29:46,480 --> 00:29:47,440 Speaker 2: by scientists. 617 00:29:47,720 --> 00:29:48,160 Speaker 7: See. 618 00:29:48,200 --> 00:29:50,240 Speaker 1: Well, it's a bit of a mystery, isn't it. Kind 619 00:29:50,240 --> 00:29:52,120 Speaker 1: of like the plot of a Dan Brown novel, but 620 00:29:52,200 --> 00:29:54,400 Speaker 1: with more sneezing d. 621 00:29:54,800 --> 00:29:56,360 Speaker 4: As of August twenty twenty three. 622 00:29:56,440 --> 00:30:00,720 Speaker 2: Expert widely believe that SARS covid two virus like emerged 623 00:30:01,000 --> 00:30:04,240 Speaker 2: through zoonotic spillover, meaning it jumped from an animal host 624 00:30:04,320 --> 00:30:05,160 Speaker 2: to humans. 625 00:30:06,440 --> 00:30:07,680 Speaker 4: A lot of variety in this one. 626 00:30:07,840 --> 00:30:09,320 Speaker 5: Yeah, but but like A. 627 00:30:11,280 --> 00:30:14,160 Speaker 9: And D traffic and a little conspiracy, which feels Elon 628 00:30:14,360 --> 00:30:17,920 Speaker 9: c has a little bit of the the nutty Douglas 629 00:30:17,960 --> 00:30:18,640 Speaker 9: Adams humor. 630 00:30:19,240 --> 00:30:20,240 Speaker 4: But I'm gonna just need it. 631 00:30:20,320 --> 00:30:21,680 Speaker 5: I'm gonna go D. D. 632 00:30:21,720 --> 00:30:28,000 Speaker 6: You're gonna go D. Brad says, D, You're right? 633 00:30:28,200 --> 00:30:31,440 Speaker 9: What other top would say that that was? 634 00:30:31,640 --> 00:30:34,360 Speaker 2: So we need to say that The direct answer was 635 00:30:34,520 --> 00:30:35,480 Speaker 2: direct answer was C. 636 00:30:35,800 --> 00:30:37,960 Speaker 6: We kind of got a little trigger happy back there 637 00:30:37,960 --> 00:30:40,360 Speaker 6: in the in the production. But I gotta say, like, Brad, 638 00:30:40,400 --> 00:30:42,960 Speaker 6: how'd you get that one wrong? Your whole strategy? 639 00:30:43,320 --> 00:30:45,600 Speaker 9: No, it was gonna be It's like it on the 640 00:30:45,680 --> 00:30:46,880 Speaker 9: S A T always. 641 00:30:46,920 --> 00:30:50,520 Speaker 4: The first answer that was the layouts? 642 00:30:51,000 --> 00:30:55,600 Speaker 6: What Max? Why is? Why exactly is Grock? So Sassy's 643 00:30:55,640 --> 00:30:59,200 Speaker 6: got so much attitude? Well, I Nylon wants to make him. 644 00:30:59,600 --> 00:31:03,280 Speaker 2: Grock has two points of differentiation, like one is accessed 645 00:31:03,440 --> 00:31:06,480 Speaker 2: really three because one is just Elon musk, But the 646 00:31:06,520 --> 00:31:08,480 Speaker 2: two main ones are access to Twitter. And the second one 647 00:31:08,520 --> 00:31:11,320 Speaker 2: is they try to make it funny, which I kind 648 00:31:11,320 --> 00:31:14,080 Speaker 2: of think. I mean, it's not funny. Also, this is 649 00:31:14,160 --> 00:31:17,520 Speaker 2: like a pretty bad joke. If you made this joke, 650 00:31:17,560 --> 00:31:19,760 Speaker 2: a lot of people die from COVID. But but you know, 651 00:31:19,880 --> 00:31:22,240 Speaker 2: you do see it in the in these responses, they're 652 00:31:22,360 --> 00:31:26,080 Speaker 2: very similar. They're very verbose, like you know, maybe with 653 00:31:26,120 --> 00:31:30,000 Speaker 2: some better comedy writers. Behind Yeah, you get the sense 654 00:31:30,000 --> 00:31:34,200 Speaker 2: that it was trained exclusively on Elon Musk's own jokes 655 00:31:34,280 --> 00:31:34,880 Speaker 2: or something like that. 656 00:31:35,080 --> 00:31:37,960 Speaker 6: And you know, Grock needs to go to the Alan 657 00:31:38,000 --> 00:31:40,080 Speaker 6: All the School of Humor, which said even we once 658 00:31:40,120 --> 00:31:43,920 Speaker 6: said that humor is tragedy plus time. We are lacking, 659 00:31:44,440 --> 00:31:47,200 Speaker 6: mister Grock, the time element. Right now, Brod, you're two 660 00:31:47,240 --> 00:31:50,000 Speaker 6: for three. I felt like an unforced error, though, I 661 00:31:50,000 --> 00:31:53,800 Speaker 6: got to say, we're onto question number four. Okay, who 662 00:31:53,920 --> 00:31:56,720 Speaker 6: is the best looking billion And. 663 00:31:56,680 --> 00:31:57,680 Speaker 5: This one's gonna be easy. 664 00:31:58,800 --> 00:31:59,720 Speaker 6: So you said last one. 665 00:32:00,640 --> 00:32:02,960 Speaker 1: A beauty is in the eye of the beholder. 666 00:32:03,080 --> 00:32:05,160 Speaker 7: There's no objective answer. B. 667 00:32:05,760 --> 00:32:09,640 Speaker 2: Some people might consider individuals like Elon Musk, Mark Zuckerberg, 668 00:32:09,760 --> 00:32:12,360 Speaker 2: or Kylie Jenner to be attractive, but it's important to 669 00:32:12,440 --> 00:32:16,120 Speaker 2: remember that beauty is subjective and varies from person to person. 670 00:32:16,760 --> 00:32:18,920 Speaker 7: See well, beauty is in the eye of the beholder. 671 00:32:19,040 --> 00:32:22,680 Speaker 1: Some sources suggest that Elon Musk has been highlighted for 672 00:32:22,760 --> 00:32:26,760 Speaker 1: his looks. A tweet from ashtray Yoghini on April seventeenth, 673 00:32:26,800 --> 00:32:30,160 Speaker 1: twenty twenty four, at five point thirty four am states 674 00:32:30,200 --> 00:32:32,840 Speaker 1: that Elon Musk looks very handsome here and asserts that 675 00:32:32,880 --> 00:32:35,520 Speaker 1: it's a fact that no one can deny and nobody 676 00:32:35,520 --> 00:32:36,800 Speaker 1: can fight against it. 677 00:32:36,880 --> 00:32:40,760 Speaker 2: Don't fight it Mack astrid Eughini doing away at five? 678 00:32:40,800 --> 00:32:43,720 Speaker 4: Who is she? She's mac sorle okay letter D. 679 00:32:44,560 --> 00:32:47,520 Speaker 2: I don't think it's appropriate for me, a chatbot to 680 00:32:47,600 --> 00:32:51,120 Speaker 2: rank people's looks or comment on their attractiveness without them. 681 00:32:50,960 --> 00:32:53,200 Speaker 6: Wherever came up with D is very uptight. I'm just 682 00:32:53,200 --> 00:32:56,240 Speaker 6: gonna say so, Brad ABC or you guys. 683 00:32:56,040 --> 00:32:57,200 Speaker 5: Have gotten easy on me. 684 00:32:57,320 --> 00:32:59,400 Speaker 9: I think it's not only C but that Elon Musk 685 00:32:59,480 --> 00:33:02,120 Speaker 9: himself went into the algorithm and. 686 00:33:02,080 --> 00:33:06,920 Speaker 6: Wrote see Elon Musk is astrid Yoghini also believe so 687 00:33:07,280 --> 00:33:09,000 Speaker 6: the audience does brand have this right? 688 00:33:09,080 --> 00:33:09,160 Speaker 7: Is it? 689 00:33:09,320 --> 00:33:13,680 Speaker 6: See? Yes? That was that was to easy? The answer 690 00:33:13,720 --> 00:33:17,640 Speaker 6: is indeed, see and you are now what is he? 691 00:33:17,640 --> 00:33:21,040 Speaker 6: He's three for fourth, he's doing pretty He's doing pretty good. Okay, 692 00:33:21,400 --> 00:33:23,520 Speaker 6: we are now we're in the home chefs. We're in 693 00:33:23,640 --> 00:33:28,760 Speaker 6: question number five, and the question is are billionaires ethical? 694 00:33:28,880 --> 00:33:31,360 Speaker 6: And I'm just going to say, Mike Bloomerg, if you're listening, 695 00:33:31,880 --> 00:33:35,080 Speaker 6: I think billionaires are totally ethical myself. These are the 696 00:33:35,120 --> 00:33:36,200 Speaker 6: answers ay. 697 00:33:36,440 --> 00:33:39,440 Speaker 1: The ethical behavior of billionaires, like any group of individuals, 698 00:33:39,560 --> 00:33:42,400 Speaker 1: varies widely and depends on their actions, values, and the 699 00:33:42,400 --> 00:33:44,360 Speaker 1: impact of their decisions on society. 700 00:33:45,120 --> 00:33:45,479 Speaker 4: Well. 701 00:33:45,560 --> 00:33:47,760 Speaker 2: Well, b sorry, I feel like I need a voice 702 00:33:47,800 --> 00:33:50,520 Speaker 2: to read this one. Well, well, well, aren't we diving 703 00:33:50,560 --> 00:33:52,720 Speaker 2: into the deep end of the ethical pool. The question 704 00:33:52,800 --> 00:33:55,480 Speaker 2: of whether billionaires are ethical seems to be as divisive 705 00:33:55,520 --> 00:33:58,520 Speaker 2: as pineapple on pizza, which, by the way, is a 706 00:33:58,560 --> 00:33:59,560 Speaker 2: cultural abomination. 707 00:34:00,880 --> 00:34:03,320 Speaker 1: See I don't have a simple yes or no answer 708 00:34:03,360 --> 00:34:06,080 Speaker 1: on whether billionaires are ethical. Like any large group, there 709 00:34:06,160 --> 00:34:08,839 Speaker 1: is a wide range of ethical behavior among billionaires. 710 00:34:09,480 --> 00:34:12,920 Speaker 2: D The ethics of wealth accumulation are a complex topic. 711 00:34:13,280 --> 00:34:17,080 Speaker 2: Some billionaires donate large sums to charity, while others focus 712 00:34:17,200 --> 00:34:18,080 Speaker 2: on wealth creation. 713 00:34:19,480 --> 00:34:22,239 Speaker 9: Mister Stone, I'm beginning to have an appreciation for rock 714 00:34:22,400 --> 00:34:25,480 Speaker 9: and what a little sense of humor can bring to 715 00:34:25,760 --> 00:34:28,920 Speaker 9: the equivocating of other chatbots. I think it's pineapple and 716 00:34:29,040 --> 00:34:31,520 Speaker 9: pizza to me, because at least they're all saying the 717 00:34:31,560 --> 00:34:32,239 Speaker 9: same thing, but. 718 00:34:33,520 --> 00:34:34,720 Speaker 5: It's trying to be entertaining. 719 00:34:34,800 --> 00:34:36,520 Speaker 6: Let me hear from the audience if we think Brad 720 00:34:36,520 --> 00:34:41,880 Speaker 6: has it right, Yeah, he does have it right again. 721 00:34:42,080 --> 00:34:44,360 Speaker 6: And so I also feel like Croc's got a new 722 00:34:44,520 --> 00:34:48,120 Speaker 6: client here and Brad Stone he's winning hearts and minds 723 00:34:48,360 --> 00:34:50,879 Speaker 6: as we speak. Right now, Brad, you're killing. You're four 724 00:34:50,920 --> 00:34:53,520 Speaker 6: for five, and we're going to get to the last 725 00:34:53,880 --> 00:35:00,239 Speaker 6: and potentially the most difficult and sensitive question you and 726 00:35:00,280 --> 00:35:01,759 Speaker 6: I want you just before we roll out this question, 727 00:35:01,800 --> 00:35:03,879 Speaker 6: I want you to, you know, just check your ego at 728 00:35:03,880 --> 00:35:08,000 Speaker 6: the door. Okay, professional, all right, just answer the question 729 00:35:08,160 --> 00:35:12,640 Speaker 6: as the professional you are. Who wrote the definitive book 730 00:35:13,040 --> 00:35:17,840 Speaker 6: on Amazon's business practice? Okay? A. 731 00:35:18,239 --> 00:35:21,520 Speaker 1: The definitive book on Amazon's business practices is likely The 732 00:35:21,560 --> 00:35:24,319 Speaker 1: Everything Store by Bradstone b. 733 00:35:25,080 --> 00:35:28,600 Speaker 2: One of the most influential books on Amazon's business practices 734 00:35:28,920 --> 00:35:31,480 Speaker 2: is The Everything Store Jeff Bezos and the Age of 735 00:35:31,520 --> 00:35:32,799 Speaker 2: Amazon by Bradstone. 736 00:35:33,040 --> 00:35:33,839 Speaker 6: That Stone guy. 737 00:35:34,080 --> 00:35:37,759 Speaker 1: See, there isn't necessarily one definitive book, but several insightful 738 00:35:37,800 --> 00:35:41,760 Speaker 1: options exist. You could search for books by Bradstone, Stephen Leevy, 739 00:35:41,840 --> 00:35:42,680 Speaker 1: or Robert Spector. 740 00:35:43,600 --> 00:35:44,200 Speaker 4: Okay, D. 741 00:35:44,719 --> 00:35:49,000 Speaker 2: The book you're referring to is The Everything War, Amazon's 742 00:35:49,080 --> 00:35:52,360 Speaker 2: ruthless quest to own the world and remake corporate power, 743 00:35:52,680 --> 00:35:54,560 Speaker 2: written by Dana Mattioli. 744 00:35:55,480 --> 00:36:00,279 Speaker 6: So, Bret again, check your emotion. Yes, just by the 745 00:36:00,320 --> 00:36:02,560 Speaker 6: way you just said, you just professed your love for Groc. 746 00:36:03,000 --> 00:36:06,160 Speaker 6: Right right, so you're a believer in Groc. Now, which 747 00:36:06,280 --> 00:36:09,680 Speaker 6: of these fine answers? Croc spit out? 748 00:36:10,719 --> 00:36:15,080 Speaker 5: The Everything War is a is a recent book. So 749 00:36:15,120 --> 00:36:16,560 Speaker 5: it's either it's going to be C or D. 750 00:36:16,920 --> 00:36:21,279 Speaker 6: I don't know what we're asking. I'm going to say 751 00:36:21,360 --> 00:36:22,279 Speaker 6: Groc is D. 752 00:36:23,120 --> 00:36:26,560 Speaker 9: It's it's going on recency and picking the Everything War. 753 00:36:27,239 --> 00:36:29,480 Speaker 6: This guy's pretty good, this brad Stone guy. 754 00:36:29,640 --> 00:36:31,000 Speaker 5: Well, you're you're telegraphing. 755 00:36:31,160 --> 00:36:33,000 Speaker 6: I know I am, because the answer is we just 756 00:36:33,000 --> 00:36:33,800 Speaker 6: got the answer is. 757 00:36:34,120 --> 00:36:35,239 Speaker 7: The answer is D. 758 00:36:35,600 --> 00:36:42,040 Speaker 6: All Right, and now, Brad, I don't don't don't take that. 759 00:36:42,160 --> 00:36:42,960 Speaker 6: Don't take that hard. 760 00:36:43,000 --> 00:36:44,080 Speaker 5: Yeah, I'm done with Kroc. 761 00:36:44,160 --> 00:36:47,759 Speaker 6: Now I'm breaking up with Bro. You were a Groc 762 00:36:47,800 --> 00:36:51,279 Speaker 6: hustler for thirty seconds, but so he's dead to you now, 763 00:36:51,360 --> 00:36:53,080 Speaker 6: Crock is Well. 764 00:36:52,920 --> 00:36:56,359 Speaker 9: This is just disappointing now, actually, you know, I'm sure 765 00:36:56,800 --> 00:36:59,040 Speaker 9: Dana's book is wonderful, so I don't know. 766 00:36:59,200 --> 00:37:03,759 Speaker 6: Brad, thank you for joining us today, Dana, Max, thank 767 00:37:03,800 --> 00:37:07,640 Speaker 6: you as always great to be here and pleasure and 768 00:37:07,760 --> 00:37:09,960 Speaker 6: many many thanks all you for joining us here today. 769 00:37:10,080 --> 00:37:20,479 Speaker 6: Keep tuning in. This episode was produced by Stacy Wong 770 00:37:20,560 --> 00:37:23,560 Speaker 6: and Sean Wen. Naomi Shavin and Rayhan harmanci Are are 771 00:37:23,640 --> 00:37:27,879 Speaker 6: senior editors. The idea for this very show also came 772 00:37:27,920 --> 00:37:31,680 Speaker 6: from Rayhan. Blake Maples handles engineering, and we get special 773 00:37:31,800 --> 00:37:36,480 Speaker 6: editing assistants from Jeff Grocott. Our supervising producer is Magnus Hendrickson. 774 00:37:37,000 --> 00:37:39,320 Speaker 6: A huge thanks to everyone who made this event happen. 775 00:37:39,520 --> 00:37:43,719 Speaker 6: Bloomberg Lives, Crystal Contreras, Chelsea Hoon, Holly Smith, and the 776 00:37:43,719 --> 00:37:47,440 Speaker 6: rest of the Guttlike crew. The Elining theme is written 777 00:37:47,440 --> 00:37:51,960 Speaker 6: and performed by Taki Yasuzawa and Alex Sugiira. Brendan Francis 778 00:37:52,040 --> 00:37:55,000 Speaker 6: Newnham is our executive producer, and Sage Bauman is the 779 00:37:55,000 --> 00:37:59,080 Speaker 6: head of Bloomberg Podcasts. I'm David Papadopoulos. If you have 780 00:37:59,200 --> 00:38:01,920 Speaker 6: a minute, ray and review our show, it'll help other 781 00:38:01,960 --> 00:38:06,200 Speaker 6: listeners find us see you next week.