1 00:00:04,050 --> 00:00:08,527 I love this library. It is, it is, it is absolutely beautiful library. 2 00:00:08,610 --> 00:00:10,860 Are you a big reader? You know, I mean, I, I, 3 00:00:11,960 --> 00:00:15,787 I read a lot in general. I don't know that I've had that much time in 4 00:00:15,870 --> 00:00:17,160 over the last year or so. 5 00:00:18,780 --> 00:00:21,000 Dario Amodei is an unlikely AI celebrity. 6 00:00:22,080 --> 00:00:26,047 He's known for warning the world about the risks of artificial intelligence. 7 00:00:26,130 --> 00:00:31,267 Now, his company is an AI frontrunner valued at nearly a trillion dollars. 8 00:00:31,350 --> 00:00:34,207 You're shipping so much, so fast. How are you doing that? 9 00:00:34,290 --> 00:00:36,817 We use Claude, you know, across the product development cycle, 10 00:00:36,900 --> 00:00:39,510 and it allows us to release very fast. 11 00:00:40,710 --> 00:00:44,677 Founded by a team of OpenAI defectors in 2021, Anthropic 12 00:00:44,760 --> 00:00:47,227 started out as an underdog lab. 13 00:00:47,310 --> 00:00:50,857 Today it's the breakout star, wiping billions in value off 14 00:00:50,940 --> 00:00:53,977 software stocks, going head-to-head with the Pentagon, 15 00:00:54,060 --> 00:00:56,947 and creating models allegedly powerful enough to burst 16 00:00:57,030 --> 00:00:59,227 through the walls of modern cybersecurity. 17 00:00:59,310 --> 00:01:01,597 Some of the early companies that we gave this 18 00:01:01,680 --> 00:01:06,180 to said things like, this is a super weapon, please don't release this. 19 00:01:09,570 --> 00:01:13,147 Guiding Anthropic through this is a sibling duo, Dario, 20 00:01:13,230 --> 00:01:15,937 the brother and visionary, and Daniela, the sister 21 00:01:16,020 --> 00:01:20,077 and operator who puts Dario's swirling cosmic thoughts into action. 22 00:01:20,160 --> 00:01:22,507 Dario and I, we've always been really close since we were 23 00:01:22,590 --> 00:01:25,327 little, but I think we always really wanted to do something big together. 24 00:01:25,410 --> 00:01:27,150 Okay, but when you argue who wins? 25 00:01:28,680 --> 00:01:33,727 No one, No one. As the AI arms race escalates, the Amodeis are eager 26 00:01:33,810 --> 00:01:36,457 to establish themselves as the good guys. 27 00:01:36,540 --> 00:01:39,217 But Anthropic's technology could have profound implications 28 00:01:39,300 --> 00:01:43,507 for how humanity works, learns thinks, even fights wars. 29 00:01:43,590 --> 00:01:46,057 Anthropic is run by an ideological lunatic. 30 00:01:46,140 --> 00:01:51,697 This is a debate about what the proper use of AI by the government is. 31 00:01:51,780 --> 00:01:54,697 At some point, the models become very dangerous, you know, 32 00:01:54,780 --> 00:01:55,897 the bad guys will have it too. 33 00:01:55,980 --> 00:01:59,887 And at that point, we have to make sure that good guys have an even better model. 34 00:01:59,970 --> 00:02:02,647 The name Anthropic actually comes from the Greek word 35 00:02:02,730 --> 00:02:07,357 for human, which speaks to their mission to build responsible AI 36 00:02:07,440 --> 00:02:09,600 for the long-term benefit of humanity. 37 00:02:10,440 --> 00:02:12,517 Can you actually do that when you're building the most 38 00:02:12,600 --> 00:02:14,430 powerful technology in the world? 39 00:02:20,310 --> 00:02:23,137 You are at the center of the AI universe right now. 40 00:02:23,220 --> 00:02:27,877 What does that feel like? The experience I've had for my whole career, 41 00:02:27,960 --> 00:02:31,117 and certainly the whole time at Anthropic, is 42 00:02:31,200 --> 00:02:33,607 that there's this kind of smooth exponential, 43 00:02:33,690 --> 00:02:35,977 and the experience of the smooth exponential is, 44 00:02:36,060 --> 00:02:38,407 nothing's happening, nothing's happening, nothing's happening. 45 00:02:38,490 --> 00:02:41,677 Little things happen, and then zoom, it goes crazy. 46 00:02:41,760 --> 00:02:43,657 So, you know, I was watching this graph for a while 47 00:02:43,740 --> 00:02:46,177 and I said, oh yeah, we'll probably become the, you know, 48 00:02:46,260 --> 00:02:49,837 the, the AI company with, you know, the, the most revenue 49 00:02:49,920 --> 00:02:52,867 and the most valuation some sometime around this time. 50 00:02:52,950 --> 00:02:55,687 And, and indeed, indeed, it has happened. 51 00:02:55,770 --> 00:02:59,027 We're just keeping in mind all the things we usually keep in 52 00:02:59,110 --> 00:03:00,887 mind, you know, how do we train good models? 53 00:03:00,970 --> 00:03:02,417 How do we put them in good products? 54 00:03:02,500 --> 00:03:04,457 How do we make sure that everything's safe? 55 00:03:04,540 --> 00:03:07,067 What were you like as a kid growing up in San Francisco? 56 00:03:07,150 --> 00:03:08,837 I know your, your dad was a leather craftsman. 57 00:03:08,920 --> 00:03:12,527 Your mom worked in libraries. How did that shape you? 58 00:03:12,610 --> 00:03:14,987 You know, the whole, you know, like first, you know, 59 00:03:15,070 --> 00:03:19,240 internet revolution was happening around me and I had absolutely no interest in it. 60 00:03:20,620 --> 00:03:24,497 I was just interested in like, doing math and like scra, you know, scrawling things. 61 00:03:24,580 --> 00:03:27,857 I was interested in, like, you know, understanding the universe. 62 00:03:27,940 --> 00:03:29,860 I was interested in science fiction. 63 00:03:30,820 --> 00:03:33,580 I think I just felt a lot of curiosity about the world. 64 00:03:35,590 --> 00:03:37,427 What was it like growing up with Dario? 65 00:03:37,510 --> 00:03:43,037 He was so smart. He was taking, you know, calculus when he was, you know, 66 00:03:43,120 --> 00:03:45,137 in I think like middle school. 67 00:03:45,220 --> 00:03:46,997 He took math classes at UC Berkeley, 68 00:03:47,080 --> 00:03:52,577 when he was in high school. I was actually more into to reading and arts. 69 00:03:52,660 --> 00:03:55,460 So we were almost like complete compliments in that way. 70 00:03:56,260 --> 00:04:00,947 Dario studied neuroscience before turning to AI at Baidu and later Google. 71 00:04:01,030 --> 00:04:04,427 While Daniela started out as an early employee at Stripe. 72 00:04:04,510 --> 00:04:06,407 They lived together in San Francisco, along 73 00:04:06,490 --> 00:04:09,347 with Daniela's husband, Holden Karnofsky. 74 00:04:09,430 --> 00:04:14,477 Then in 2016, Dario joined the newly formed OpenAI followed by Daniela. 75 00:04:14,560 --> 00:04:17,717 In 2018, the company began as a nonprofit 76 00:04:17,800 --> 00:04:21,677 that promised a safe, more open path to super intelligence. 77 00:04:21,760 --> 00:04:23,837 I think we need to think right now about 78 00:04:23,920 --> 00:04:26,357 how we want this deployed, how everyone gets 79 00:04:26,440 --> 00:04:28,907 to benefit from it, how we're gonna govern it, how we're 80 00:04:28,990 --> 00:04:31,440 gonna make it safe and sort of good for humanity. 81 00:04:32,020 --> 00:04:35,657 At OpenAI, Dario developed the concept of scaling laws, 82 00:04:35,740 --> 00:04:39,707 predicting that large language models would improve simply by adding more data 83 00:04:39,790 --> 00:04:43,937 and computing power, even if the underlying algorithm stayed the same 84 00:04:44,020 --> 00:04:47,657 At that point in time, I know it sounds crazy now, looking back, not a lot 85 00:04:47,740 --> 00:04:49,757 of people believed, Hey, scale up is the way 86 00:04:49,840 --> 00:04:51,767 that these models are gonna get smarter and better. 87 00:04:51,850 --> 00:04:55,457 That was sort of an unusual counter-cultural scientific 88 00:04:55,540 --> 00:04:59,897 perspective that I think was really held by our, you know, founding research team. 89 00:04:59,980 --> 00:05:05,537 That approach helped supercharge OpenAI's models, paving the way for chatGPT, 90 00:05:05,620 --> 00:05:07,127 but the Amodeis reportedly clashed 91 00:05:07,210 --> 00:05:10,397 with Altman over the company's direction and values. 92 00:05:10,480 --> 00:05:12,677 Altman has said, for all the differences I have 93 00:05:12,760 --> 00:05:15,310 with Anthropic, I mostly trust them as a company. 94 00:05:16,780 --> 00:05:20,777 Your decision to leave OpenAI has become Silicon Valley lore. 95 00:05:20,860 --> 00:05:25,037 What did you disagree on? Look, I'll, I'm gonna say it, 96 00:05:25,120 --> 00:05:26,357 I'm gonna say it very simply. 97 00:05:26,440 --> 00:05:29,537 There are many valid disagreements to be had on safety. 98 00:05:29,620 --> 00:05:31,967 We certainly had some of those disagreements with them, 99 00:05:32,050 --> 00:05:36,527 but you know, people that, that, that, that alone is not sufficient to leave. 100 00:05:36,610 --> 00:05:39,587 When you feel that you can't trust someone, when you feel 101 00:05:39,670 --> 00:05:43,007 that their values are not what they say they are, 102 00:05:43,090 --> 00:05:45,497 when you feel that they're not honest, 103 00:05:45,580 --> 00:05:49,517 that makes it very hard to, you know, to continue to work 104 00:05:49,600 --> 00:05:51,767 with a company, to continue to trust the company. 105 00:05:51,850 --> 00:05:57,207 And look, at the end of the day, why argue with someone when you don't have the same 106 00:05:57,290 --> 00:05:58,890 vision and you don't trust them? 107 00:05:59,690 --> 00:06:02,667 The way to resolve it is you go off and do your thing. 108 00:06:02,750 --> 00:06:04,760 They go off and do their thing. 109 00:06:08,570 --> 00:06:13,857 So this is Precita Park. When Dario and Daniela decided to leave OpenAI 110 00:06:13,940 --> 00:06:18,477 to start Anthropic, this is where the early team would get together. 111 00:06:18,560 --> 00:06:21,357 It was during the pandemic. So the story goes, 112 00:06:21,440 --> 00:06:24,087 and a bunch of early employees would come here, 113 00:06:24,170 --> 00:06:27,027 they'd pull up a chair on the grass, they'd have lunch, 114 00:06:27,110 --> 00:06:29,410 and they'd talk about what they were building. 115 00:06:30,560 --> 00:06:32,967 When we started this company, there were seven co-founders 116 00:06:33,050 --> 00:06:34,677 of which me and Daniela are two. 117 00:06:34,760 --> 00:06:37,047 And now, you know, we we're, we're the, 118 00:06:37,130 --> 00:06:39,177 we're the basically the only company in the space 119 00:06:39,260 --> 00:06:41,337 that has all of its co-founders still here. 120 00:06:41,420 --> 00:06:45,327 You don't get to, you know, be a company of the size and scale that we are. 121 00:06:45,410 --> 00:06:47,960 With that happening like that almost never happens. 122 00:06:49,220 --> 00:06:51,087 From the start, Anthropic pitched itself 123 00:06:51,170 --> 00:06:53,690 as the ultimate safety conscious AI company. 124 00:06:54,560 --> 00:06:57,537 Dario has published long essays with names like Machines 125 00:06:57,620 --> 00:06:59,517 of Loving Grace and The Adolescence 126 00:06:59,600 --> 00:07:03,627 of Technology, musing on the miraculous potential of AI, 127 00:07:03,710 --> 00:07:05,510 as well as the worst case scenarios. 128 00:07:06,560 --> 00:07:09,417 Anthropic's chatbot, Claude has been trained to follow a set 129 00:07:09,500 --> 00:07:12,327 of principles called a constitution, intended 130 00:07:12,410 --> 00:07:14,517 to keep it on the straight and narrow. 131 00:07:14,600 --> 00:07:19,077 Claude has a very distinct style and feel a human name. 132 00:07:19,160 --> 00:07:20,637 What are you trying to convey? 133 00:07:20,720 --> 00:07:22,977 I think when people interact with Claude versus, you know, 134 00:07:23,060 --> 00:07:26,067 other systems, there is more of a feeling of, I like 135 00:07:26,150 --> 00:07:28,257 to describe it as professional warmth. 136 00:07:28,340 --> 00:07:31,077 So the goal is not for it to be your best friend, 137 00:07:31,160 --> 00:07:34,557 but it's not for it to be sort of cold, rote, calculating. 138 00:07:34,640 --> 00:07:37,647 It should feel approachable, but distant, right? Professional. 139 00:07:37,730 --> 00:07:41,547 Anthropic has talked about teaching Claude to be good. 140 00:07:41,630 --> 00:07:44,607 What is a good model? What is a bad model? 141 00:07:44,690 --> 00:07:48,087 You don't want a model that lies accidentally or intentionally, right? 142 00:07:48,170 --> 00:07:50,547 Lying, we call hallucinations. It makes something up. 143 00:07:50,630 --> 00:07:52,587 The models are just trained to predict the next word. 144 00:07:52,670 --> 00:07:55,977 So sometimes they don't know and they just invent something. 145 00:07:56,060 --> 00:07:58,197 Models sometimes, as we've shown in our research, 146 00:07:58,280 --> 00:08:00,177 can purposely try to deceive you. 147 00:08:00,260 --> 00:08:01,887 We have to make sure that doesn't happen in production 148 00:08:01,970 --> 00:08:03,837 models that we expose to customers. 149 00:08:03,920 --> 00:08:05,547 And then there's a lot of work around harmlessness, 150 00:08:05,630 --> 00:08:08,007 just making sure that the model is not accidentally 151 00:08:08,090 --> 00:08:11,397 producing, you know, information that is wrong or harmful 152 00:08:11,480 --> 00:08:13,347 or, or could cause someone to do something bad 153 00:08:13,430 --> 00:08:17,067 Whose values are being put into Claude, like is there a universal good? 154 00:08:17,150 --> 00:08:19,737 There's so many religions, so many different beliefs, like 155 00:08:19,820 --> 00:08:23,097 how do you even decide that? So of course there's no universal standard for 156 00:08:23,180 --> 00:08:25,167 what makes something helpful or harmless, 157 00:08:25,250 --> 00:08:27,867 but there are founding documents in human history, 158 00:08:27,950 --> 00:08:30,927 like the UN Declaration of Human Rights that we can use 159 00:08:31,010 --> 00:08:33,627 to train Claude's character. 160 00:08:33,710 --> 00:08:35,847 And I think, you know, interestingly on the religious front, 161 00:08:35,930 --> 00:08:38,247 we've actually started to have a lot of conversations 162 00:08:38,330 --> 00:08:41,187 with religious leaders around how should we think about, 163 00:08:41,270 --> 00:08:46,047 you know, Claude, the entity, and how can we bake in some of the kind of core values 164 00:08:46,130 --> 00:08:50,187 that are consistent across religions types of belief 165 00:08:50,270 --> 00:08:53,517 that really just transcend, you know, a specific worldview, 166 00:08:53,600 --> 00:08:55,447 but that are really things that human beings been grappling 167 00:08:55,530 --> 00:09:00,157 with for, for millennia. Have you tried to give Claude a trait that didn't take, 168 00:09:00,240 --> 00:09:03,697 or was there any trait that emerged that surprised you? 169 00:09:03,780 --> 00:09:07,477 I think in the early days, it's funny to look back on them now, the kind 170 00:09:07,560 --> 00:09:09,727 of early Claudes, Claude two era, 171 00:09:09,810 --> 00:09:11,947 sometimes Claude would be almost a little bit nannyish. 172 00:09:12,030 --> 00:09:14,647 Claude was like, I'm really concerned about you, and you're like, Claude, I was 173 00:09:14,730 --> 00:09:16,117 asking for the weather, right? 174 00:09:16,200 --> 00:09:17,737 It was like, it was something really benign. 175 00:09:17,820 --> 00:09:19,657 Thankfully we didn't, you know, release most of, most 176 00:09:19,740 --> 00:09:21,577 of the most egregious versions of those. 177 00:09:21,660 --> 00:09:23,767 But I think it is, you know, it is, when you think about it, 178 00:09:23,850 --> 00:09:27,457 it's like tuning a dial. And so our researchers have this very fine needle 179 00:09:27,540 --> 00:09:30,150 to thread in order to land at the center of that. 180 00:09:31,680 --> 00:09:35,917 Whatever Anthropic is doing with Claude, it seems to be working. 181 00:09:36,000 --> 00:09:38,707 Anthropic's revenue has skyrocketed over the past year, 182 00:09:38,790 --> 00:09:41,857 making the company profitable for the first time. 183 00:09:41,940 --> 00:09:46,267 That's largely thanks to the company's focus on more lucrative business tools. 184 00:09:46,350 --> 00:09:50,857 Claude Code, a major leap that automated large chunks of software engineering 185 00:09:50,940 --> 00:09:54,240 and Claude Cowork, which gave that power to everyone else. 186 00:09:55,140 --> 00:09:59,017 Now, early on, others focused on fun splashy consumer apps. 187 00:09:59,100 --> 00:10:02,077 You made a bet on coding and enterprise. 188 00:10:02,160 --> 00:10:03,607 Why did you make that bet? 189 00:10:03,690 --> 00:10:07,687 Was it a, was it a values decision or a business decision? 190 00:10:07,770 --> 00:10:11,887 Look, if you pick a business model that fundamentally conflicts with your values, 191 00:10:11,970 --> 00:10:13,837 you're gonna have a hard time, right? 192 00:10:13,920 --> 00:10:17,947 Either you betray your own values or you become irrelevant. 193 00:10:18,030 --> 00:10:20,437 And so when we thought about it, we said, look, you know, 194 00:10:20,520 --> 00:10:23,797 we've seen the world of social media, the consumer world, 195 00:10:23,880 --> 00:10:27,637 it, it really seems to, you know, encourage engagement, 196 00:10:27,720 --> 00:10:30,637 even addiction, you know, the slop we've seen 197 00:10:30,720 --> 00:10:32,947 with AI video models, it's like, what's going on? 198 00:10:33,030 --> 00:10:35,587 Is it wants to maximize the number of minutes that you're, 199 00:10:35,670 --> 00:10:39,727 you're paying attention to because that's the advertising revenue-driven incentive. 200 00:10:39,810 --> 00:10:43,357 Whereas if we look at enterprise, look, I mean, you know, 201 00:10:43,440 --> 00:10:46,027 we want to make these models useful to people. 202 00:10:46,110 --> 00:10:50,257 We want to use AI to, you know, cure diseases 203 00:10:50,340 --> 00:10:51,637 that we couldn't cure before, right? 204 00:10:51,720 --> 00:10:54,157 Well, that's working with biotech, it's working with pharma, 205 00:10:54,240 --> 00:10:56,467 it's working with academic research groups. 206 00:10:56,550 --> 00:10:58,567 All of those are enterprises, right? 207 00:10:58,650 --> 00:11:03,247 We want to use AI to like, you know, to make energy cheaper and more efficient. 208 00:11:03,330 --> 00:11:04,957 That's, that's all enterprise. 209 00:11:05,040 --> 00:11:08,017 And so I think it served us well to have this business model 210 00:11:08,100 --> 00:11:10,537 that largely aligns with our values. 211 00:11:10,620 --> 00:11:12,577 Soon after Claude Cowork was released, 212 00:11:12,660 --> 00:11:16,897 $285 billion in market value vanished overnight. 213 00:11:16,980 --> 00:11:18,907 Traders called it the SaaSpocalypse. 214 00:11:18,990 --> 00:11:23,377 This kind of white collar wipeout story in the software sector. Terrifying. 215 00:11:23,460 --> 00:11:27,127 Some of those are down for nine days in a row, so clearly the tension is building. 216 00:11:27,210 --> 00:11:30,517 If AI continues improving at this pace, how much 217 00:11:30,600 --> 00:11:34,417 of traditional software gets replaced and how fast. 218 00:11:34,500 --> 00:11:38,107 I think with AI like the pie is getting bigger, right? 219 00:11:38,190 --> 00:11:40,987 So the existing incumbents may be smaller 220 00:11:41,070 --> 00:11:44,587 and relative terms, some of them may, may go down in value, 221 00:11:44,670 --> 00:11:47,587 some of them may even may even go out of business if they don't, 222 00:11:47,670 --> 00:11:49,237 if they don't adapt in the right way. 223 00:11:49,320 --> 00:11:53,357 But like, I would guess that the software industry gets larger, not 224 00:11:53,440 --> 00:11:56,627 although there will be some big losers, those who don't kind 225 00:11:56,710 --> 00:11:58,727 of see what's coming, who don't identify the moats they 226 00:11:58,810 --> 00:12:00,947 have, they're gonna have a really hard time. 227 00:12:01,030 --> 00:12:02,327 It's sort of the quiet cafe. 228 00:12:02,410 --> 00:12:06,677 Okay. Anthropic's recent growth spurt might not have happened without this man. 229 00:12:06,760 --> 00:12:09,497 Boris Cherny, the engineer behind Claude Code 230 00:12:09,580 --> 00:12:13,547 and Claude Cowork. When Anthropic hired him in 2024, 231 00:12:13,630 --> 00:12:17,147 Cherny was living a very different life in rural Japan. 232 00:12:17,230 --> 00:12:19,367 There was a lot of farmer's markets, like very slow. 233 00:12:19,450 --> 00:12:21,497 We were making miso. That was sort of the big hobby. 234 00:12:21,580 --> 00:12:24,047 And I remember using the first AI chatbot 235 00:12:24,130 --> 00:12:27,587 that I'd ever used, and it just took my breath away 236 00:12:27,670 --> 00:12:31,247 and I was just like, oh my God, I have to, I have to just be a part of this. 237 00:12:31,330 --> 00:12:33,257 And I, I'm also just such a big scifi reader 238 00:12:33,340 --> 00:12:35,387 and so I just know how bad this thing can go. 239 00:12:35,470 --> 00:12:38,897 Like this technology is incredibly powerful and so we move, move back. 240 00:12:38,980 --> 00:12:44,357 You created Claude Code, you led the development of Claude Cowork, 241 00:12:44,440 --> 00:12:47,607 what problem were you trying to solve? 242 00:12:47,690 --> 00:12:49,877 If you look at the coding products, they were all pretty simple. 243 00:12:49,960 --> 00:12:51,377 It was like, it was sort of like, you know, 244 00:12:51,460 --> 00:12:53,837 like complete the word, complete the sentence. 245 00:12:53,920 --> 00:12:59,357 That was the extent of AI in coding and we just wanted to make a way bigger bet. 246 00:12:59,440 --> 00:13:03,107 And our bet was, we think actually a coding agent can do all of it. 247 00:13:03,190 --> 00:13:07,187 A year and a half ago you wrote the code by hand and sometimes you press tab 248 00:13:07,270 --> 00:13:08,867 and it would auto complete a line. 249 00:13:08,950 --> 00:13:13,427 Now I talk to my Claude and it writes the code and then while it does that, I talk 250 00:13:13,510 --> 00:13:15,197 to the next Claude and it writes some code. 251 00:13:15,280 --> 00:13:18,467 And at any point I have either, you know, like a few Claudes running and up 252 00:13:18,550 --> 00:13:21,347 to a few thousand Claudes running, doing things. 253 00:13:21,430 --> 00:13:25,597 How much code is Claude writing internally for engineers here? 254 00:13:25,680 --> 00:13:27,730 It's writing almost all of it on my team. 255 00:13:28,930 --> 00:13:31,697 So for me personally, it's been writing a hundred percent 256 00:13:31,780 --> 00:13:33,167 of my code for at least six months. 257 00:13:33,250 --> 00:13:36,107 The work of engineering has just completely changed. 258 00:13:36,190 --> 00:13:38,117 I feel like I suddenly have superpowers. 259 00:13:38,200 --> 00:13:41,447 I have like a jet pack and the engineering has never been this fun. 260 00:13:41,530 --> 00:13:43,997 Can we give Claude a spin? Yeah, let's do this. 261 00:13:44,080 --> 00:13:46,787 All right. Alright, so let's, let's make like a little recipe app. 262 00:13:46,870 --> 00:13:49,690 So make a recipe app. What do you want it to do? 263 00:13:51,130 --> 00:13:56,127 I would love it to suggest meals for the week. 264 00:13:56,210 --> 00:13:58,607 So, you know, this is gonna take maybe a few minutes 265 00:13:58,690 --> 00:14:01,547 and let's, let's just see if Claude builds this. 266 00:14:01,630 --> 00:14:07,727 Oh, it gave me some options. Hmm. Let's do These look pretty tasty actually. Yeah, 267 00:14:07,810 --> 00:14:09,797 I could do a Greek chicken power bowl. 268 00:14:09,880 --> 00:14:13,637 When I click on a recipe, I want it 269 00:14:13,720 --> 00:14:18,720 to show me how to do it. Thank you. 270 00:14:19,330 --> 00:14:22,590 Aw, I'm sure Claude appreciates that. 271 00:14:24,460 --> 00:14:25,637 I always try to say thank you. 272 00:14:25,720 --> 00:14:27,107 Yeah, I always try to be nice too. I 273 00:14:27,190 --> 00:14:30,317 don't know. A recipe app might not be the most mind 274 00:14:30,400 --> 00:14:32,717 blowing demo of Anthropic's technology, 275 00:14:32,800 --> 00:14:35,807 but what Claude Code just did in minutes, used to take hours 276 00:14:35,890 --> 00:14:39,827 or days for some. That looks like a big opportunity. 277 00:14:39,910 --> 00:14:41,927 Are you like nervous about next week or is it like.. 278 00:14:42,010 --> 00:14:43,937 What's next week, Your developer conference? 279 00:14:44,020 --> 00:14:48,580 Oh! Yes, yes. There's so many things happening every week. 280 00:14:52,190 --> 00:14:55,907 Year over year. API volume is up nearly 17 x on the cloud. 281 00:14:55,990 --> 00:15:01,827 In the last 12 months, we shipped eight frontier models to developers and users. 282 00:15:01,910 --> 00:15:04,887 Welcome to the second annual Code with Claude conference, 283 00:15:04,970 --> 00:15:08,337 where Claude superfans come, hoping to get a glimpse of the future. 284 00:15:08,420 --> 00:15:11,097 We're having a lot of fun. There's a ton of adrenaline. 285 00:15:11,180 --> 00:15:14,817 This is the first year we've grown faster than the exponential. 286 00:15:14,900 --> 00:15:18,507 In the first quarter of this year, we saw, if you were 287 00:15:18,590 --> 00:15:22,047 to annualize it, 80 x growth per year. 288 00:15:22,130 --> 00:15:25,647 Ooh. I 289 00:15:25,730 --> 00:15:30,927 use Claude every day in the form of Claude Code to do all sorts of stuff. Not 290 00:15:31,010 --> 00:15:34,467 just write code. I write way more code than I did five years ago. 291 00:15:34,550 --> 00:15:40,467 I've got the confidence of, I don't know, a 22-year-old with VC money. I'm not 22. 292 00:15:40,550 --> 00:15:43,377 My friend invited me and it's been, it's amazing. 293 00:15:43,460 --> 00:15:45,087 As someone that's not really technical, 294 00:15:45,170 --> 00:15:47,187 which I think it's really interesting just to see 295 00:15:47,270 --> 00:15:49,137 what everyone's working on, what's out there. 296 00:15:49,220 --> 00:15:51,267 I think the, the concerning part or the, 297 00:15:51,350 --> 00:15:54,987 and the exciting part is hey, they can do things that are, 298 00:15:55,070 --> 00:15:58,857 we couldn't dream of and timelines we couldn't ever expect. 299 00:15:58,940 --> 00:16:03,447 But the kind of alternate side of that reality is you really have to be prepared 300 00:16:03,530 --> 00:16:06,580 because they will really be a thousand times more productive. 301 00:16:08,090 --> 00:16:10,587 All of this raises an obvious question. 302 00:16:10,670 --> 00:16:14,397 Will engineers be the first casualties of the AI they're building? 303 00:16:14,480 --> 00:16:17,750 It's been revenge of the nerds for a while? Is that over? 304 00:16:19,700 --> 00:16:23,000 I think we all become nerds, But what happens to the actual nerds? 305 00:16:24,170 --> 00:16:28,827 I think the actual nerds will, they're they have to figure it out. 306 00:16:28,910 --> 00:16:32,187 Yeah. I I think for a lot of them, the skill that they had 307 00:16:32,270 --> 00:16:34,647 before is gonna help them in the future 308 00:16:34,730 --> 00:16:37,377 because they sort of have a pretty big head start. 309 00:16:37,460 --> 00:16:39,177 They do a lot of things that are not coding 310 00:16:39,260 --> 00:16:40,917 and general also have to talk to users. 311 00:16:41,000 --> 00:16:44,097 They have to plan, they have to think about what's next. 312 00:16:44,180 --> 00:16:48,130 So I, I think these are still parts of engineering that are gonna stick around. 313 00:16:48,260 --> 00:16:53,577 Silicon Valley may have AI fever, but elsewhere the mood is less upbeat. 314 00:16:53,660 --> 00:16:56,877 70% of Americans think AI will kill jobs 315 00:16:56,960 --> 00:16:59,610 and nearly a third worry, theirs will be one of them. 316 00:17:00,770 --> 00:17:03,567 Dario Amodei has been outspoken about this issue 317 00:17:03,650 --> 00:17:06,897 and some of his predictions don't exactly sound reassuring. 318 00:17:06,980 --> 00:17:10,077 I think we could have this very unusual combination 319 00:17:10,160 --> 00:17:14,397 of very fast GDP growth and high unemployment, 320 00:17:14,480 --> 00:17:19,197 or at least underemployment or, you know, low wage job. 321 00:17:19,280 --> 00:17:21,597 A lot of low wage jobs, high inequality. 322 00:17:21,680 --> 00:17:24,387 You've been really direct about job loss. 323 00:17:24,470 --> 00:17:27,387 AI could eliminate half of all entry level white collar jobs in 324 00:17:27,470 --> 00:17:32,487 the next one to five years. That was a year ago. AI has moved incredibly fast. 325 00:17:32,570 --> 00:17:35,127 Is it still 50% or is it higher? 326 00:17:35,210 --> 00:17:38,607 I don't know exactly, but I'm, I'm still, I'm still pretty concerned. 327 00:17:38,690 --> 00:17:40,617 I'm still the same order of concerns. 328 00:17:40,700 --> 00:17:45,207 You know, we are seeing right now that AI is making people more productive. 329 00:17:45,290 --> 00:17:48,507 But that's the usual hump. You automate 90% of the job, 330 00:17:48,590 --> 00:17:51,247 great people are 10 times more productive in the other 10% 331 00:17:51,330 --> 00:17:52,987 'cause they're 10 times more leveraged. 332 00:17:53,070 --> 00:17:56,047 But eventually it gets close to a hundred percent. 333 00:17:56,130 --> 00:17:58,147 Now the sequel to that is, well then you have 334 00:17:58,230 --> 00:17:59,797 to find something else for them to do. 335 00:17:59,880 --> 00:18:02,167 Right now AI makes the software engineers more productive, 336 00:18:02,250 --> 00:18:05,467 even though AI writes all the code or almost all the code. 337 00:18:05,550 --> 00:18:08,647 But we're already starting to see the beginning of like, 338 00:18:08,730 --> 00:18:10,597 you know, there may be some people that it's, 339 00:18:10,680 --> 00:18:13,237 it's not making more productive, that it's better for the AI 340 00:18:13,320 --> 00:18:14,977 to just, to just do the thing. 341 00:18:15,060 --> 00:18:16,410 How does that sit with you? 342 00:18:17,610 --> 00:18:20,250 It's, it's very uncomfortable. Yeah. 343 00:18:21,090 --> 00:18:23,977 I I think this is, this is the reason why I chose to go 344 00:18:24,060 --> 00:18:26,647 to Anthropic and I think this is the reason that a lot 345 00:18:26,730 --> 00:18:30,457 of people here chose to go here is artificial intelligence 346 00:18:30,540 --> 00:18:33,397 is this force that is far bigger than we are. 347 00:18:33,480 --> 00:18:36,667 But here we can hopefully make it go a little bit better. 348 00:18:36,750 --> 00:18:39,757 Do you feel like it's your job to do something about that? 349 00:18:39,840 --> 00:18:42,157 Or is that someone else's problem? 350 00:18:42,240 --> 00:18:43,687 I think it's a thing that we have to talk about. 351 00:18:43,770 --> 00:18:47,287 We have to advocate for it. Ultimately, it's up to society to solve it. 352 00:18:47,370 --> 00:18:48,997 This is bigger than one company. 353 00:18:49,080 --> 00:18:51,907 There has been a lot of pushback on, you know, your, 354 00:18:51,990 --> 00:18:54,667 and I know you've said you're trying to warn people, 355 00:18:54,750 --> 00:18:56,707 but that, you know, you're, you know, Jensen Huang said, 356 00:18:56,790 --> 00:18:58,627 you're conflating tasks with jobs. 357 00:18:58,710 --> 00:19:03,037 AI is creating jobs. Anybody who is saying that AI is wiping out jobs 358 00:19:03,120 --> 00:19:06,337 is scaring people. Other folks have said this, you know, it's sort 359 00:19:06,420 --> 00:19:08,867 of doom marketing that benefits anthropic. 360 00:19:08,950 --> 00:19:13,117 So, so I wanna be really clear and push back hard against this. 361 00:19:13,200 --> 00:19:16,597 In every interview I talk about the, 362 00:19:16,680 --> 00:19:21,787 the possible ways to address these risks from tax and macroeconomic policy to 363 00:19:21,870 --> 00:19:25,117 what the new jobs are in The Adolescence of Technology. 364 00:19:25,200 --> 00:19:29,707 I have like five pages where I lay out the difference between tasks and jobs. 365 00:19:29,790 --> 00:19:34,807 Why this time is different than other times, but social media, which I detest, 366 00:19:34,890 --> 00:19:40,447 which I detest as a category, people have these three second clips from, you know, 367 00:19:40,530 --> 00:19:45,697 from a year ago. I've, I've written much more carefully about these things 368 00:19:45,780 --> 00:19:47,107 where, where I talk about the risks. 369 00:19:47,190 --> 00:19:49,357 So these are this, this, the idea 370 00:19:49,440 --> 00:19:52,327 that this is cheap marketing is itself cheap marketing. 371 00:19:52,410 --> 00:19:55,327 I think it's, it's part of the disease of Silicon Valley. 372 00:19:55,410 --> 00:20:00,517 It's been caught up in this social media world of, of, of three seconds. 373 00:20:00,600 --> 00:20:03,037 And so my message is just definitely not doom is coming. 374 00:20:03,120 --> 00:20:05,827 My message is like, this is something, you know, 375 00:20:05,910 --> 00:20:07,807 that we should see coming, that we're worried about 376 00:20:07,890 --> 00:20:10,590 and that we need to actually respond to positively 377 00:20:13,770 --> 00:20:15,217 Beyond the software industry, 378 00:20:15,300 --> 00:20:19,927 the potential impact of AI on jobs seems harder to predict. 379 00:20:20,010 --> 00:20:22,627 Anthropic has published a paper estimating which fields 380 00:20:22,710 --> 00:20:25,160 could make the most use of AI in the near future. 381 00:20:25,980 --> 00:20:28,897 If its predictions are right, management, finance, 382 00:20:28,980 --> 00:20:31,280 and legal jobs could soon look very different. 383 00:20:32,460 --> 00:20:36,847 Which jobs go away, who gets replaced and what new jobs are created. 384 00:20:36,930 --> 00:20:38,430 So no one knows for sure 385 00:20:39,420 --> 00:20:41,587 because you know, the economy's unpredictable, right? 386 00:20:41,670 --> 00:20:46,447 The thing that that we have going for us here is the pie is gonna expand a lot. 387 00:20:46,530 --> 00:20:49,307 And so because the pie is gonna expand a lot, 388 00:20:49,390 --> 00:20:51,947 there are probably going to be places where people can go. 389 00:20:52,030 --> 00:20:54,287 It's just a matter of finding them fast enough. 390 00:20:54,370 --> 00:20:56,087 So play this out for me a little bit. 391 00:20:56,170 --> 00:21:02,087 You know, you wake up in five years, you know, what does this country look like? 392 00:21:02,170 --> 00:21:03,797 What are those people doing? 393 00:21:03,880 --> 00:21:05,957 Yeah, so 'cause if there's that much unemployment, is 394 00:21:06,040 --> 00:21:08,657 that not how revolutions start? 395 00:21:08,740 --> 00:21:10,607 Yeah, no, this is the outcome we wanna prevent. 396 00:21:10,690 --> 00:21:12,707 This is absolutely the outcome we want to prevent. 397 00:21:12,790 --> 00:21:15,527 I think there's a few places, none of them are guaranteed. 398 00:21:15,610 --> 00:21:18,377 We're not sure, but there's the physical world, 399 00:21:18,460 --> 00:21:21,527 we need a lot of more people to make, build, 400 00:21:21,610 --> 00:21:24,017 manufacture things in the physical world. 401 00:21:24,100 --> 00:21:27,857 Anything that's human centered, I think that's gonna be a big deal, right? 402 00:21:27,940 --> 00:21:30,617 People, or at least some people wanna talk to humans. 403 00:21:30,700 --> 00:21:33,707 So these kind of human relationship driven jobs, like, 404 00:21:33,790 --> 00:21:35,267 I think those are gonna be important 405 00:21:35,350 --> 00:21:39,077 and I think there'll be some effort by the humans to kind 406 00:21:39,160 --> 00:21:42,347 of direct AIs right at, at some level it has to be in line 407 00:21:42,430 --> 00:21:45,407 with someone's values and someone's intentions and, 408 00:21:45,490 --> 00:21:48,407 and so I, I think there's gonna be some role there, 409 00:21:48,490 --> 00:21:51,407 although I don't know how thin versus how thick it will be. 410 00:21:51,490 --> 00:21:53,177 I think I feel a little bit more hopeful 411 00:21:53,260 --> 00:21:56,507 that humans will continue to find ways to leverage AI 412 00:21:56,590 --> 00:21:59,627 to be productive, to do the parts of the work 413 00:21:59,710 --> 00:22:01,907 that are meaningful to us that only humans can do. 414 00:22:01,990 --> 00:22:05,687 I think the human to human interaction will never fully go away. 415 00:22:05,770 --> 00:22:10,187 The example I often reach for is, is what will happen in medicine today. 416 00:22:10,270 --> 00:22:13,720 We hire doctors who are expert diagnosticians. 417 00:22:14,620 --> 00:22:17,357 I think AI is gonna soon be pretty good at telling you 418 00:22:17,440 --> 00:22:19,697 what the suite of options of things that are wrong with you and what test to run. 419 00:22:19,780 --> 00:22:21,827 And you won't need a doctor to do that. 420 00:22:21,910 --> 00:22:24,437 But an AI can't physically examine you 421 00:22:24,520 --> 00:22:27,317 and say, Hey, does it hurt when I press, you know, here. 422 00:22:27,400 --> 00:22:30,407 They can't have a bedside manner with you that says like, 423 00:22:30,490 --> 00:22:31,607 tell me how you're feeling about this. 424 00:22:31,690 --> 00:22:34,377 How are you, how are you coping with going through this process? 425 00:22:34,460 --> 00:22:36,467 And I think we're going to pivot something like medicine 426 00:22:36,550 --> 00:22:38,597 to be much more focused on the interpersonal 427 00:22:38,680 --> 00:22:41,987 because the diagnostic tools are going to become much better. 428 00:22:42,070 --> 00:22:45,860 But the interpersonal human part, that's, that's not gonna change. 429 00:22:49,360 --> 00:22:52,187 Explain this to me. Daniela runs day-to-day operations. 430 00:22:52,270 --> 00:22:54,647 All the leadership team reports to you. Yes. 431 00:22:54,730 --> 00:22:57,407 No one reports to you. That sounds like a pretty sweet job. 432 00:22:57,490 --> 00:23:00,737 It's incredibly freeing. It lets me do all the things 433 00:23:00,820 --> 00:23:05,267 that I do much more easily than I would otherwise 434 00:23:05,350 --> 00:23:06,527 Because it's and she does all the work. 435 00:23:06,610 --> 00:23:08,797 Is that what you're saying? It's, it's, it's a, I'm just, 436 00:23:08,880 --> 00:23:13,727 I if if you, if you had to go through the things I had 437 00:23:13,810 --> 00:23:18,340 to go through during Department of War or no, 438 00:23:19,420 --> 00:23:22,780 The Amodeis hope AI can have a utopian influence on society, 439 00:23:23,710 --> 00:23:28,337 but there's no denying the technology's destructive and dystopian potential. 440 00:23:28,420 --> 00:23:31,697 Anthropic is at the center of a technological arms race 441 00:23:31,780 --> 00:23:33,980 with powerful governments vying for control. 442 00:23:34,990 --> 00:23:37,727 Dario has charged head on into this arena. 443 00:23:37,810 --> 00:23:39,827 He's not afraid to blast his competitors. 444 00:23:39,910 --> 00:23:42,587 And then I think there are some players who, you know, 445 00:23:42,670 --> 00:23:46,437 who, who are yoloing who, who pull, pull the risk dial too far. 446 00:23:46,520 --> 00:23:48,207 And I'm very concerned. Who is yoloing? 447 00:23:48,290 --> 00:23:53,097 So I, that's a question I'm not gonna answer Or critique the US government 448 00:23:53,180 --> 00:23:56,847 and Anthropic's own partners for selling AI chips to China. 449 00:23:56,930 --> 00:23:59,937 It's a bit like, you know, I I I don't know like selling, 450 00:24:00,020 --> 00:24:03,507 selling, you know, nuclear weapons to, to North Korea. 451 00:24:03,590 --> 00:24:08,877 I've been very outspoken about the need for export controls on chips to China. 452 00:24:08,960 --> 00:24:11,187 I say this because I think it would be really bad 453 00:24:11,270 --> 00:24:13,647 for America, for, you know, the state 454 00:24:13,730 --> 00:24:17,967 of democracy in the world for, you know, China to be ahead in AI capabilities. 455 00:24:18,050 --> 00:24:19,917 And you know, it's, it's like some 456 00:24:20,000 --> 00:24:23,367 of the chipmakers obviously don't agree with that view, 457 00:24:23,450 --> 00:24:25,137 but it hasn't stopped me from saying it. 458 00:24:25,220 --> 00:24:27,567 Even after we've signed more partnerships, 459 00:24:27,650 --> 00:24:30,117 I'm sure they wish we didn't say these things, 460 00:24:30,200 --> 00:24:32,277 but these things are what are what I believe. 461 00:24:32,360 --> 00:24:34,257 And so, you know, look, we're all adults here. 462 00:24:34,340 --> 00:24:37,940 We can work together on one thing while disagreeing about another thing. 463 00:24:39,050 --> 00:24:41,037 Amodei's statements about geopolitics date back 464 00:24:41,120 --> 00:24:44,157 to his days at Caltech. As a sophomore physics student, 465 00:24:44,240 --> 00:24:47,127 he developed a reputation as an anti-war advocate 466 00:24:47,210 --> 00:24:50,337 who believed scientists shouldn't sit in ivory towers. 467 00:24:50,420 --> 00:24:55,437 It's a worldview that would later define Anthropic's position on the future of war. 468 00:24:55,520 --> 00:24:57,717 You've had a longstanding anti-war stance 469 00:24:57,800 --> 00:25:00,567 and yet you were one of the first AI companies 470 00:25:00,650 --> 00:25:04,707 to sign a contract with the Department of Defense to operate on classified networks. 471 00:25:04,790 --> 00:25:09,177 These are the, are the networks that the US uses to fight wars. Explain that. 472 00:25:09,260 --> 00:25:10,887 Look, I mean, the world changes. 473 00:25:10,970 --> 00:25:13,797 Like, you know, my view of this technology, you know, 474 00:25:13,880 --> 00:25:17,667 when I see Russia invading Ukraine, when I see the risk 475 00:25:17,750 --> 00:25:22,017 of China invading Taiwan, you know, it worries me 476 00:25:22,100 --> 00:25:25,370 that we have a kind of resurgent authoritarian block 477 00:25:26,270 --> 00:25:29,967 that they're very aggressive and that we need to defend ourselves. 478 00:25:30,050 --> 00:25:34,107 You know, I may not agree with every policy of I of either administration, 479 00:25:34,190 --> 00:25:37,137 but you know, that's why we've generally been supportive of this. 480 00:25:37,220 --> 00:25:39,387 You've been working with Palantir since 2024. 481 00:25:39,470 --> 00:25:41,127 That's right. You know, their technology is used 482 00:25:41,210 --> 00:25:43,557 by ICE, police departments, in Gaza. 483 00:25:43,640 --> 00:25:46,287 Is Claude being used for surveillance in other ways? 484 00:25:46,370 --> 00:25:50,727 We, we don't work with ICE either through Palantir or anyone else. 485 00:25:50,810 --> 00:25:54,477 We don't work with CBP. I don't believe we work in Gaza. 486 00:25:54,560 --> 00:25:57,777 We're very careful about, you know, scoping our engagements 487 00:25:57,860 --> 00:25:59,310 to things that we believe in. 488 00:26:00,080 --> 00:26:03,927 In 2025, Anthropic along with OpenAI xAI 489 00:26:04,010 --> 00:26:07,647 and Google won a $200 million contract with the Pentagon. 490 00:26:07,730 --> 00:26:09,567 Anthropic framed it as an opportunity 491 00:26:09,650 --> 00:26:12,100 to become a leading AI vendor for the government. 492 00:26:14,210 --> 00:26:17,577 Claude was reportedly used by the US military in the operation 493 00:26:17,660 --> 00:26:20,547 to seize Venezuelan president Nicolas Maduro. 494 00:26:20,630 --> 00:26:23,277 Weeks later, everything started to unravel. 495 00:26:23,360 --> 00:26:26,187 We bring you breaking news from Anthropic, the Department 496 00:26:26,270 --> 00:26:28,437 of Defense demand that Anthropic allow full use 497 00:26:28,520 --> 00:26:31,167 of its AI technology without guardrails. 498 00:26:31,250 --> 00:26:35,697 Anthropic drew red lines refusing to let Claude be used for mass surveillance 499 00:26:35,780 --> 00:26:39,867 and autonomous weapons putting the company on a collision course with the Pentagon. 500 00:26:39,950 --> 00:26:44,047 The tech giant facing a deadline today to accept conditions or be blacklisted. 501 00:26:44,130 --> 00:26:47,137 Does a commercial entity have the right to determine 502 00:26:47,220 --> 00:26:51,247 how their products are used by the military? 503 00:26:51,330 --> 00:26:55,297 Lawsuits were filed and Anthropic was banned from the Pentagon, underscored 504 00:26:55,380 --> 00:26:59,677 by strong words from President Trump and defense secretary Pete Hegseth 505 00:26:59,760 --> 00:27:02,197 Anthropic is run by an ideological lunatic who 506 00:27:02,280 --> 00:27:04,477 shouldn't have a, so that's, but that's not my question. 507 00:27:04,560 --> 00:27:07,687 My question is AI decision making about what we do. 508 00:27:07,770 --> 00:27:11,947 Do you mind being called an ideological lunatic or a bunch of left wing nut jobs? 509 00:27:12,030 --> 00:27:14,677 You know, I've been called worse things than that all the time. 510 00:27:14,760 --> 00:27:17,317 What does winning this fight actually look like? 511 00:27:17,400 --> 00:27:23,257 I won't even call it a fight. This is more a debate about what the proper use of AI 512 00:27:23,340 --> 00:27:27,397 by the government is. And AI is an emerging new technology. 513 00:27:27,480 --> 00:27:31,987 We don't understand the ways in which it's reliable or unreliable. 514 00:27:32,070 --> 00:27:35,017 We don't understand the ways in which it promotes our values 515 00:27:35,100 --> 00:27:36,547 or undermines our values. 516 00:27:36,630 --> 00:27:39,427 And so one of the things that I thought was important was 517 00:27:39,510 --> 00:27:44,797 to establish a precedent on some of the, some of the use cases we think are good, 518 00:27:44,880 --> 00:27:46,597 which frankly is most of them. 519 00:27:46,680 --> 00:27:50,287 And, and some of the use cases that we're concerned about. 520 00:27:50,370 --> 00:27:52,837 A US official has said, with the help of LLMs, 521 00:27:52,920 --> 00:27:54,727 the US military has gone from being able 522 00:27:54,810 --> 00:27:58,957 to hit a thousand targets a day to 5,000 targets a day. 523 00:27:59,040 --> 00:28:03,097 That means Claude can help kill more people more quickly. 524 00:28:03,180 --> 00:28:04,597 Are you comfortable with that? 525 00:28:04,680 --> 00:28:06,757 Basically, you're, you're asking like, you know, 526 00:28:06,840 --> 00:28:08,047 do you believe in this country, right? 527 00:28:08,130 --> 00:28:12,037 Do you want this country to be a more powerful actor rather than a less powerful 528 00:28:12,120 --> 00:28:15,697 actor on the world stage? I do. I'm a patriot. It's not up to me. 529 00:28:15,780 --> 00:28:18,637 If we provide a technology, it's not up to us to say, 530 00:28:18,720 --> 00:28:22,417 you can do this military operation and you can't do that military operation. 531 00:28:22,500 --> 00:28:27,397 Now, I might privately believe that this military operation makes sense 532 00:28:27,480 --> 00:28:31,717 and that military operation is a bad idea, but we're not gonna deny the technology. 533 00:28:31,800 --> 00:28:36,157 You, you basically have to, you know, you have to leave policy in the hands 534 00:28:36,240 --> 00:28:38,827 of the military decision makers. 535 00:28:38,910 --> 00:28:41,437 Bloomberg has reported that Claude is being used 536 00:28:41,520 --> 00:28:43,687 by the US military and the war in Iran 537 00:28:43,770 --> 00:28:47,167 to do AI assisted targeting via a platform made 538 00:28:47,250 --> 00:28:50,317 by Palantir Maven Smart system. In February, 539 00:28:50,400 --> 00:28:54,307 US missile reportedly hit a girl school in Iran killing more 540 00:28:54,390 --> 00:28:56,850 than 150 people, most of them children. 541 00:28:57,930 --> 00:29:00,037 Did Claude play a role in that strike? 542 00:29:00,120 --> 00:29:02,977 We don't know exactly how, you know, 543 00:29:03,060 --> 00:29:06,630 these models were used, you know, obviously like, you know, 544 00:29:07,980 --> 00:29:09,817 these things that, that, you know, mistakes 545 00:29:09,900 --> 00:29:12,427 that happen in warfare are really, really terrible. 546 00:29:12,510 --> 00:29:15,307 Like this is a really terrible thing to happen. 547 00:29:15,390 --> 00:29:17,467 We were willing to risk the future of our company 548 00:29:17,550 --> 00:29:21,667 to like limit how, you know, these models are used 549 00:29:21,750 --> 00:29:24,727 and you know, what, what you're talking about is a use case 550 00:29:24,810 --> 00:29:26,317 that doesn't even violate our red lines. 551 00:29:26,400 --> 00:29:29,257 We're worried that there will be a hundred times as much, 552 00:29:29,340 --> 00:29:33,217 you know, with, with use cases that, that, that do violate our red lines. 553 00:29:33,300 --> 00:29:35,347 Now I, you know, I I I, you know, again, again, 554 00:29:35,430 --> 00:29:39,337 I would say I think overall the use of these, the, the use 555 00:29:39,420 --> 00:29:41,237 of these models is appropriate. 556 00:29:41,320 --> 00:29:43,577 I think it's good on net, you know, 557 00:29:43,660 --> 00:29:47,957 but military decision makers make terrible mistakes even 558 00:29:48,040 --> 00:29:49,787 when, even, even at the best of times, 559 00:29:49,870 --> 00:29:51,827 and I, I don't know if we're in the best of times. 560 00:29:51,910 --> 00:29:56,327 What we've seen here is Claude assists, but a human makes the final call. 561 00:29:56,410 --> 00:29:59,117 So a human made that final call, not Claude. 562 00:29:59,200 --> 00:30:02,447 Imagine if you had a world in which, not Claude, 563 00:30:02,530 --> 00:30:05,207 because we haven't allowed it, but someone else's AI model, 564 00:30:05,290 --> 00:30:07,937 the AI model just makes the decision and the human never sees it. 565 00:30:08,020 --> 00:30:09,437 That's what we were standing up for. 566 00:30:09,520 --> 00:30:11,087 That's what we were fighting against. 567 00:30:11,170 --> 00:30:14,477 This school had a website. You could have found it in a Google Search, 568 00:30:14,560 --> 00:30:16,517 like shouldn't Claude have spotted that? 569 00:30:16,600 --> 00:30:20,117 And it doesn't speak to a scarier issue about using 570 00:30:20,200 --> 00:30:22,337 technology as a shortcut in war. 571 00:30:22,420 --> 00:30:26,537 The principle that was obeyed here is a human makes the 572 00:30:26,620 --> 00:30:28,337 human makes the final decision. 573 00:30:28,420 --> 00:30:31,517 I don't know what role Claude or any other AI had, 574 00:30:31,600 --> 00:30:34,637 but like, if, if this isn't an illustration why 575 00:30:34,720 --> 00:30:40,817 that principle is so important, I don't know what is, Is AI warfare more likely 576 00:30:40,900 --> 00:30:45,587 to stop World War III, a war between the US and China? 577 00:30:45,670 --> 00:30:48,107 Or is it more likely to make it happen? 578 00:30:48,190 --> 00:30:50,567 I would say on balance it is more likely to stop it. 579 00:30:50,650 --> 00:30:55,097 But if we have no limits on how it's used, then I think, 580 00:30:55,180 --> 00:30:56,897 you know, it could be more likely to cause it. 581 00:30:56,980 --> 00:30:58,547 You've seen Dr. StrangeLove, right? 582 00:30:58,630 --> 00:31:01,697 The premise of it was like, you know, you have a doomsday device 583 00:31:01,780 --> 00:31:04,007 that automatically fires nuclear weapons when it thinks 584 00:31:04,090 --> 00:31:05,507 nuclear weapons are being fired at it. 585 00:31:05,590 --> 00:31:07,067 What could go wrong? Right? 586 00:31:07,150 --> 00:31:10,367 I think the way conflicts happen is that, you know, the, 587 00:31:10,450 --> 00:31:11,747 the two sides jump at each other. 588 00:31:11,830 --> 00:31:13,337 They misunderstand each other. 589 00:31:13,420 --> 00:31:16,037 And when we don't have proper oversight of this technology, 590 00:31:16,120 --> 00:31:19,337 I think those kinds of accidents are more likely to happen. 591 00:31:19,420 --> 00:31:22,667 Now, I think if AI is used in an appropriate way, in, in, 592 00:31:22,750 --> 00:31:28,637 in, not even warfare, but think of just, just intelligence collection, you know, 593 00:31:28,720 --> 00:31:31,727 let, let's say we're able to, you know, predict an invasion 594 00:31:31,810 --> 00:31:34,247 of Taiwan or a new movement in Ukraine. 595 00:31:34,330 --> 00:31:38,777 Like, you know, our adversaries will think twice about, 596 00:31:38,860 --> 00:31:42,827 you know, about conducting some kind of invasion or military operation if we know 597 00:31:42,910 --> 00:31:44,410 everything that they're doing. 598 00:31:46,510 --> 00:31:49,277 AI's role on the battlefield raises difficult questions, 599 00:31:49,360 --> 00:31:53,807 even for a company willing to debate the ethical dilemmas of its own technology. 600 00:31:53,890 --> 00:31:57,160 It's a conversation Amodei seems eager to have in public. 601 00:31:58,090 --> 00:32:00,107 How do you handle the pressure? 602 00:32:00,190 --> 00:32:04,757 I try very hard to communicate and always be straightforward and honest. 603 00:32:04,840 --> 00:32:08,057 I get up in front of the company every two weeks 604 00:32:08,140 --> 00:32:11,177 and just talk for an hour about just what's on my mind, 605 00:32:11,260 --> 00:32:14,147 what's going on in the industry, what's going on in the outside world. 606 00:32:14,230 --> 00:32:19,037 It's totally uncensored. And not only does it build trust, it's very freeing for me 607 00:32:19,120 --> 00:32:23,777 where I feel that I have 3,000 people who are on the same page as me. 608 00:32:23,860 --> 00:32:28,367 That is an incredible amplifier and is one of the most useful things in 609 00:32:28,450 --> 00:32:29,897 handing, handling the pressure. 610 00:32:29,980 --> 00:32:33,377 Then when we have to confront an external challenge, 611 00:32:33,460 --> 00:32:37,157 stand up to, you know, some very difficult situations 612 00:32:37,240 --> 00:32:39,567 that we've seen in the last few months, that is 613 00:32:39,650 --> 00:32:42,867 what allows us to have a consistent and coherent position. 614 00:32:42,950 --> 00:32:45,597 And, and I think, I think it's an incredible advantage 615 00:32:45,680 --> 00:32:47,480 because I never feel like I'm alone. 616 00:32:54,020 --> 00:32:56,427 Lurking in the background at Anthropic's headquarters was 617 00:32:56,510 --> 00:32:59,660 the surprise development of a new AI model called Mythos. 618 00:33:01,160 --> 00:33:03,470 A model so powerful, it spooked everyone. 619 00:33:04,550 --> 00:33:06,567 Anthropic is making headlines almost on a weekly basis. 620 00:33:06,650 --> 00:33:08,367 Yes, Most notably now around mythos. 621 00:33:08,450 --> 00:33:11,547 The company believes it's this enormous threat. 622 00:33:11,630 --> 00:33:15,387 Imagine a world where everyone had a nuclear bazooka basically 623 00:33:15,470 --> 00:33:18,777 Mythos identified thousands of cybersecurity vulnerabilities, 624 00:33:18,860 --> 00:33:22,887 exposing potential flaws in every major operating system. 625 00:33:22,970 --> 00:33:26,127 Anthropic signaled that if fully released mythos could hack 626 00:33:26,210 --> 00:33:30,260 banks, pry open state secrets and cripple critical infrastructure. 627 00:33:31,310 --> 00:33:35,637 I think the thing that surprised me most about it was the 628 00:33:35,720 --> 00:33:39,477 models had been climbing in their ability to find vulnerabilities. 629 00:33:39,560 --> 00:33:41,247 It was a particularly large jump. 630 00:33:41,330 --> 00:33:43,557 Some of the early companies that we gave this 631 00:33:43,640 --> 00:33:46,947 to said things like, this is a super weapon, you should have 632 00:33:47,030 --> 00:33:49,227 to own a gun license to use it. 633 00:33:49,310 --> 00:33:51,230 Please don't release this. In 634 00:33:52,370 --> 00:33:54,147 an initiative called Project Glasswing, 635 00:33:54,230 --> 00:33:57,560 Anthropic gave select organizations access to Mythos. 636 00:33:58,490 --> 00:34:02,007 Even federal agencies like the National Security Administration clamored 637 00:34:02,090 --> 00:34:05,180 to use it despite Anthropic's blacklisting by the Pentagon. 638 00:34:06,050 --> 00:34:11,127 I think the future is this kind of cat and mouse game where we need to make sure 639 00:34:11,210 --> 00:34:15,147 that the good guys have the tools that they need to defend, 640 00:34:15,230 --> 00:34:17,937 and then at some point the, you know, the bad guys will have it too. 641 00:34:18,020 --> 00:34:19,437 And, and at that point we have 642 00:34:19,520 --> 00:34:21,687 to make sure the good guys have an even better model 643 00:34:21,770 --> 00:34:23,187 so they can be ready for this. 644 00:34:23,270 --> 00:34:26,697 Is it possible to stay ahead of the bad guys really though? 645 00:34:26,780 --> 00:34:29,367 That's what we hope the criticism is. 646 00:34:29,450 --> 00:34:32,487 You're effectively deciding who gets access and who doesn't. 647 00:34:32,570 --> 00:34:36,777 Why should anyone be comfortable with that kind of concentration of power? 648 00:34:36,860 --> 00:34:40,497 It wasn't like, oh, it's so powerful and let's decide who gets the power. 649 00:34:40,580 --> 00:34:44,367 It was a very specific concern around cybersecurity 650 00:34:44,450 --> 00:34:47,397 and so the way that we decided who to give the model 651 00:34:47,480 --> 00:34:49,677 to was grounded in that specific fear. 652 00:34:49,760 --> 00:34:53,007 There's obviously nuance to decide, like where do you draw that circle? 653 00:34:53,090 --> 00:34:54,417 I think that's really complicated. 654 00:34:54,500 --> 00:34:58,137 I think we've tried to be as publicly open as possible 655 00:34:58,220 --> 00:35:00,687 to say we're trying our best to make this decision well, 656 00:35:00,770 --> 00:35:03,297 but like we might not do it perfectly. 657 00:35:03,380 --> 00:35:06,280 What about the folks who say this was just good marketing. 658 00:35:06,710 --> 00:35:10,017 You know, we, we have suffered enormously commercially 659 00:35:10,100 --> 00:35:11,547 from not releasing this model. 660 00:35:11,630 --> 00:35:15,057 This model has incredibly accelerated research within 661 00:35:15,140 --> 00:35:17,877 Anthropic and production and next models. 662 00:35:17,960 --> 00:35:21,447 It would do the same in the outside world if, if we were to release it. 663 00:35:21,530 --> 00:35:24,357 This has hurt us enormously commercially. 664 00:35:24,440 --> 00:35:27,837 Have you had to make trade-offs already that you're not entirely comfortable with? 665 00:35:27,920 --> 00:35:32,097 Throughout the entire history of Anthropic has been trade-offs, right? 666 00:35:32,180 --> 00:35:34,287 In some ideal world, you would prefer to, 667 00:35:34,370 --> 00:35:36,597 before you release the first chat bot, you know, 668 00:35:36,680 --> 00:35:39,067 you could spend years studying, you know, every, 669 00:35:39,150 --> 00:35:41,197 every possible thing that could go wrong with it. 670 00:35:41,280 --> 00:35:44,167 Now, we did delay, we did delay the initial release 671 00:35:44,250 --> 00:35:46,537 of Claude, but you know, we did it for a few months. 672 00:35:46,620 --> 00:35:51,037 So everything is a trade off now that we're in, you know, 673 00:35:51,120 --> 00:35:54,097 what I would describe as a commercially leading position, 674 00:35:54,180 --> 00:35:57,817 we can afford to move the dial even further towards being careful, right? 675 00:35:57,900 --> 00:35:59,947 That's what the Mythos release was about, right? 676 00:36:00,030 --> 00:36:03,997 It's very hard to do something like that if you're not the leading player. 677 00:36:04,080 --> 00:36:07,807 There's this argument, why wouldn't the government take you over? 678 00:36:07,890 --> 00:36:12,427 Why would they let a private company control technology? That's so powerful? 679 00:36:12,510 --> 00:36:14,917 So I think, I actually think that's a very, 680 00:36:15,000 --> 00:36:18,277 that's a very serious question and I share those concerns. 681 00:36:18,360 --> 00:36:21,260 I don't think the government should outright take us over. 682 00:36:22,260 --> 00:36:26,737 Every previous powerful technology we've seen in history was 683 00:36:26,820 --> 00:36:29,587 either built by the government or originated with the government. 684 00:36:29,670 --> 00:36:33,097 So nuclear weapons, obviously, you know, initially built 685 00:36:33,180 --> 00:36:38,887 by the government, the internet, GPS, cell phones, AI is the first technology 686 00:36:38,970 --> 00:36:40,920 that's been built in the private sector 687 00:36:41,700 --> 00:36:44,467 and where government has not really had a serious role 688 00:36:44,550 --> 00:36:46,567 and is coming in late to the game. 689 00:36:46,650 --> 00:36:49,747 I think that's actually a dangerous and unstable situation. 690 00:36:49,830 --> 00:36:53,287 It is not the situation I would've chosen this technology. 691 00:36:53,370 --> 00:36:56,827 I'm scared of companies having it, but I'm also scared of government having it. 692 00:36:56,910 --> 00:37:00,607 And then, you know, we need basic regulation of the technology. 693 00:37:00,690 --> 00:37:04,627 You know, more and more as I've seen what we've seen with Mythos, you know, 694 00:37:04,710 --> 00:37:07,447 I think we need to start doing pre-release testing, 695 00:37:07,530 --> 00:37:11,647 required pre-release testing, testing and auditing of the models. 696 00:37:11,730 --> 00:37:13,597 And this was an approach the White House rejected 697 00:37:13,680 --> 00:37:18,667 initially on his first day back in office, President Trump with the help of former AI 698 00:37:18,750 --> 00:37:22,477 and crypto czar, David Sachs dismantled President Biden's AI 699 00:37:22,560 --> 00:37:26,557 executive order seeking guardrails, instead favoring a hands-off, 700 00:37:26,640 --> 00:37:29,377 let Silicon Valley do its thing approach. 701 00:37:29,460 --> 00:37:31,717 We believe that excessive regulation 702 00:37:31,800 --> 00:37:36,247 of the AI sector could kill a transformative industry just 703 00:37:36,330 --> 00:37:40,657 as it's taking off. But with Mythos and its national security implications, 704 00:37:40,740 --> 00:37:44,077 proving hard to ignore the White House now seems to want 705 00:37:44,160 --> 00:37:46,620 to gatekeep the world's most powerful ai 706 00:37:47,910 --> 00:37:51,037 It's very funny to me how there's a particular group 707 00:37:51,120 --> 00:37:54,037 of people in the tech world in Silicon Valley, they started 708 00:37:54,120 --> 00:37:57,847 with a position of like, you know, even having transparency 709 00:37:57,930 --> 00:38:00,367 around this technology, even export control, you know, 710 00:38:00,450 --> 00:38:05,437 this is all, you know, just totally, it'll apocalyptically destroy our potential 711 00:38:05,520 --> 00:38:08,437 to create the technology. It'll kill innovation. 712 00:38:08,520 --> 00:38:10,567 And then as soon as they see the first real danger, 713 00:38:10,650 --> 00:38:13,057 which I've been expecting all along, there's all this talk 714 00:38:13,140 --> 00:38:15,907 of like nationalization and the government should just seize it. 715 00:38:15,990 --> 00:38:17,257 Come on folks here, you're yo, 716 00:38:17,340 --> 00:38:21,997 you're yo-yoing from like the most extreme anti-regulatory. 717 00:38:22,080 --> 00:38:25,267 If you look at us the wrong way, you're destroying the industry to, you know, 718 00:38:25,350 --> 00:38:28,477 this completely communist, the government should grab it all. 719 00:38:28,560 --> 00:38:32,977 We, we need, we, we need a more, we need a more sensible, moderate approach. 720 00:38:33,060 --> 00:38:36,797 That's the one we've been favoring all along because we've, we've understood the 721 00:38:36,880 --> 00:38:41,087 power of this technology. We're not panicking, we're not denying it. 722 00:38:41,170 --> 00:38:44,720 We see the smooth exponential and we're responding to it appropriately. 723 00:38:51,820 --> 00:38:56,087 The reaction to AI right now, it's intense. 724 00:38:56,170 --> 00:38:58,937 Anthropic has built this really loyal following. 725 00:38:59,020 --> 00:39:01,637 There are some people who just love what they stand for, 726 00:39:01,720 --> 00:39:04,967 but there've also been protests right outside their office. 727 00:39:05,050 --> 00:39:07,817 There's a lot of anxiety, a lot of confusion, 728 00:39:07,900 --> 00:39:12,617 there's some real anger right now about what's happening 729 00:39:12,700 --> 00:39:15,047 and it actually feels like it's escalating. 730 00:39:15,130 --> 00:39:20,530 Artificial intelligence is the next industrial revolution. Whoa. 731 00:39:25,960 --> 00:39:29,327 If you look at the data, people are more concerned than excited 732 00:39:29,410 --> 00:39:33,347 about what's going on. They think the risks outweigh the benefits 733 00:39:33,430 --> 00:39:37,097 and the truth is, if you talk to the people who are building this, 734 00:39:37,180 --> 00:39:41,320 even they will tell you they don't fully know how it's all going to play out. 735 00:39:45,160 --> 00:39:47,867 How do you think about the weight of this moment? 736 00:39:47,950 --> 00:39:50,477 I worry that something will go wrong. 737 00:39:50,560 --> 00:39:52,427 You know, are we doing literally everything we can? 738 00:39:52,510 --> 00:39:55,907 We're certainly trying our best, we're certainly trying very hard. 739 00:39:55,990 --> 00:39:58,217 What I want is to create a situation where if this set 740 00:39:58,300 --> 00:40:00,197 of people can't do it, it couldn't be done. 741 00:40:00,280 --> 00:40:03,330 You can't guarantee success, but maybe you can guarantee that 742 00:40:04,030 --> 00:40:08,417 It's getting personal. Sam Altman's home has been attacked. 743 00:40:08,500 --> 00:40:11,717 How is that affecting you? It was really scary to read that. 744 00:40:11,800 --> 00:40:15,497 I mean, it was, we were obviously extremely relieved that he 745 00:40:15,580 --> 00:40:17,477 and his family were okay in general. 746 00:40:17,560 --> 00:40:19,997 I think this is a time, you know, technologically, 747 00:40:20,080 --> 00:40:23,747 politically where unfortunately there's just a lot more 748 00:40:23,830 --> 00:40:26,747 rhetoric and, and words that I think can lead to, 749 00:40:26,830 --> 00:40:29,597 to bad outcomes and bad things happening. 750 00:40:29,680 --> 00:40:31,577 I hope that this is a topic we can all just debate, 751 00:40:31,660 --> 00:40:34,007 you know, as peacefully as possible. 752 00:40:34,090 --> 00:40:38,237 It was scary to me too. I mean, you know, this is like a, a less savory aspect 753 00:40:38,320 --> 00:40:41,417 of the exponential, right? That as AI gets more 754 00:40:41,500 --> 00:40:43,457 and more of, of a big thing like, you know, 755 00:40:43,540 --> 00:40:46,787 it becomes just such a big deal to society. 756 00:40:46,870 --> 00:40:51,347 There's more attention on it. There's been massive backlash against social media. 757 00:40:51,430 --> 00:40:54,550 Countries are starting to ban it. Could that happen to AI? 758 00:40:55,960 --> 00:40:58,097 I think it's, it's absolutely possible. 759 00:40:58,180 --> 00:41:00,617 If the social media companies could go back in time 760 00:41:00,700 --> 00:41:04,577 and see the world that they see today, would they do anything differently? 761 00:41:04,660 --> 00:41:05,987 I like to think the answer to that is yes. 762 00:41:06,070 --> 00:41:09,677 I don't know if we sort of project some of the challenges 763 00:41:09,760 --> 00:41:11,597 that the social media companies have faced 764 00:41:11,680 --> 00:41:15,527 around child welfare, mental health, election integrity, all 765 00:41:15,610 --> 00:41:19,097 of these topics we're really lucky that we're we're second. 766 00:41:19,180 --> 00:41:22,307 We view it as our job to try and proactively think about all 767 00:41:22,390 --> 00:41:24,137 of the things that could go wrong. 768 00:41:24,220 --> 00:41:25,847 Because if we don't, who's going to, 769 00:41:25,930 --> 00:41:28,277 You know, I don't know that they actually set out 770 00:41:28,360 --> 00:41:30,707 to do the right thing or make the world a better place. 771 00:41:30,790 --> 00:41:32,937 And so I don't think if they were going back, 772 00:41:33,020 --> 00:41:34,497 they would even knowing what they do, 773 00:41:34,580 --> 00:41:36,687 and they certainly should do things differently. 774 00:41:36,770 --> 00:41:39,207 I don't know if they actually will, but we can't. 775 00:41:39,290 --> 00:41:43,047 This is why we're trying to get this right the first time instead of waiting 776 00:41:43,130 --> 00:41:48,207 for things to go wrong, then scrambling to justify why it's all okay. 777 00:41:48,290 --> 00:41:52,227 The main way I could see a AI being, you know, banned 778 00:41:52,310 --> 00:41:55,467 or blocked is if something really went wrong and if something really went wrong, 779 00:41:55,550 --> 00:42:00,147 then maybe it deserves to be, You have technologists saying it's gonna be amazing 780 00:42:00,230 --> 00:42:02,007 and others saying it could be awful. 781 00:42:02,090 --> 00:42:04,617 Where are you on that spectrum? 782 00:42:04,700 --> 00:42:07,317 I hope for the best, but plan for the worst. 783 00:42:07,400 --> 00:42:10,167 For me, this is the most important work I've ever done. 784 00:42:10,250 --> 00:42:12,987 For some people it feels like this is the last job 785 00:42:13,070 --> 00:42:16,377 because this is sort of the, it could mean the end of work 786 00:42:16,460 --> 00:42:20,547 and I think for other people it's, you know, like maybe it's not the last job, 787 00:42:20,630 --> 00:42:23,457 but it's existential to get this right. 788 00:42:23,540 --> 00:42:25,677 And so there's just this, this kind of burden. 789 00:42:25,760 --> 00:42:30,657 If the impacts could be as significant as we're talking about, as significant 790 00:42:30,740 --> 00:42:35,427 as Anthropic has warned about, what responsibility do you think Anthropic has 791 00:42:35,510 --> 00:42:41,810 to cushion the blow? What do you owe the people whose lives you've upended? 792 00:42:43,130 --> 00:42:45,747 I think our view has always been, 793 00:42:45,830 --> 00:42:48,567 we ultimately are responsible as an industry, the industry 794 00:42:48,650 --> 00:42:52,857 that's developing this technology for thinking through what 795 00:42:52,940 --> 00:42:55,857 are the risks, what are the bad things that could happen? 796 00:42:55,940 --> 00:42:59,817 And if some of those things come to pass, what is our role in helping to fix them? 797 00:42:59,900 --> 00:43:03,777 That is our job, right? We should not just say, well, we were just trying 798 00:43:03,860 --> 00:43:05,667 to grow the product and suddenly there's an entire 799 00:43:05,750 --> 00:43:09,267 generation of young women who have eating disorders 800 00:43:09,350 --> 00:43:10,977 or who have mental health problems 801 00:43:11,060 --> 00:43:13,167 because whoops, we were just trying to grow the product. 802 00:43:13,250 --> 00:43:16,137 That's not the stance that I think any technology company should take. 803 00:43:16,220 --> 00:43:19,070 I don't think that's the stance that we're trying to take 804 00:43:19,820 --> 00:43:23,487 For a company whose identity is so wrapped up in, We want to do this right, 805 00:43:23,570 --> 00:43:25,467 We exist as a AI safety company. 806 00:43:25,550 --> 00:43:27,837 How can we just help all of this go? Well, 807 00:43:27,920 --> 00:43:30,357 It can be hard to understand why Anthropic is pushing 808 00:43:30,440 --> 00:43:36,267 so hard to advance AI while being so upfront about the dangers. In his essays, 809 00:43:36,350 --> 00:43:38,967 Dario lays out what the end game looks like if everything 810 00:43:39,050 --> 00:43:45,417 goes right with ai, a utopian future where machines and humans work side by side AI, 811 00:43:45,500 --> 00:43:48,657 an inevitable force steered toward prosperity rather than 812 00:43:48,740 --> 00:43:53,097 catastrophe to mitigate the devastation of job loss. 813 00:43:53,180 --> 00:43:56,217 He proposes solutions like universal basic income 814 00:43:56,300 --> 00:43:58,820 and progressive taxation of AI companies. 815 00:44:00,920 --> 00:44:03,897 But as the Amodeis confront the messy realities of power, 816 00:44:03,980 --> 00:44:06,507 politics, and profit, the real test is whether 817 00:44:06,590 --> 00:44:10,040 that founding mission can survive the scale of what they're building. 818 00:44:11,390 --> 00:44:14,007 Google started with the motto, don't be evil. 819 00:44:14,090 --> 00:44:17,240 A founding promise the company quietly retired as it grew. 820 00:44:19,610 --> 00:44:22,167 You are building something incredibly powerful 821 00:44:22,250 --> 00:44:25,197 and stand to gain enormously from it. 822 00:44:25,280 --> 00:44:29,877 Why should we trust you? I think starting from a position of distrust, you know, 823 00:44:29,960 --> 00:44:31,917 if you don't know anything about, if you don't know anything about 824 00:44:32,000 --> 00:44:33,427 philanthropic, it's pretty rational. 825 00:44:33,510 --> 00:44:36,487 I think Silicon Valley has lost a lot of the world's trust 826 00:44:36,570 --> 00:44:38,377 and kind of has to re-earn it, 827 00:44:38,460 --> 00:44:42,817 and the message, you know, we're trying to send is, we're actually different and, 828 00:44:42,900 --> 00:44:45,937 and that has to be earned in things that we actually do. 829 00:44:46,020 --> 00:44:49,387 I understand one of your favorite books is The Making of the Atomic Bomb. 830 00:44:49,470 --> 00:44:52,897 That is correct. Do you see parallels between yourself and Oppenheimer? 831 00:44:52,980 --> 00:44:55,927 The figure I most identified with was Leo Szilard 832 00:44:56,010 --> 00:45:00,390 who was the one who first had the idea that there could be a chain reaction. 833 00:45:01,380 --> 00:45:03,997 Look, my view is we're, we're not gonna get through this 834 00:45:04,080 --> 00:45:06,157 with like larger than life personalities 835 00:45:06,240 --> 00:45:09,907 or like figures who try and be at the center of everything. 836 00:45:09,990 --> 00:45:12,937 In some ways, I actually see Oppenheimer as a failure case, as 837 00:45:13,020 --> 00:45:17,767 what should not happen. There's a lot of powerful actors who have interests here, 838 00:45:17,850 --> 00:45:19,537 and the only way it's gonna end well 839 00:45:19,620 --> 00:45:24,727 for everyone is if there is some, there's basically checks and balances everywhere. 840 00:45:24,810 --> 00:45:28,297 You've said there's roughly a 10 to 25% chance 841 00:45:28,380 --> 00:45:32,917 of civilization collapse, that is not insignificant. 842 00:45:33,000 --> 00:45:38,827 Is there a scenario where it's something that Anthropic built, that caused that? 843 00:45:38,910 --> 00:45:44,707 I mean, I certainly hope not. My view is that that probability comes from the very 844 00:45:44,790 --> 00:45:47,287 straightforward recipe of the technology, the existence 845 00:45:47,370 --> 00:45:49,087 of many countries in the world. 846 00:45:49,170 --> 00:45:52,987 The existence of many companies within an economy and new ones created. 847 00:45:53,070 --> 00:45:56,137 If the void is isn't filled, like that's a dilemma 848 00:45:56,220 --> 00:45:59,227 that we're in half of what we do within the company is try 849 00:45:59,310 --> 00:46:03,547 and, you know, reduce the risk as much as we can, but it's never gonna be zero. 850 00:46:03,630 --> 00:46:06,217 Suppose there are a bunch of, you know, airline companies out there 851 00:46:06,300 --> 00:46:08,737 and you're like, well, I'm gonna make an airline company that's safer. 852 00:46:08,820 --> 00:46:11,047 It can both be the case that, you know, 853 00:46:11,130 --> 00:46:14,977 your airline company is 10 times safer than all the other airline companies. 854 00:46:15,060 --> 00:46:18,187 But if someone comes and asks you, like, can you guarantee 855 00:46:18,270 --> 00:46:20,347 that your airplane will never crash? 856 00:46:20,430 --> 00:46:22,687 I mean, how could you, how could you possibly, 857 00:46:22,770 --> 00:46:25,327 But if there was a 25% chance of an airplane crashing, 858 00:46:25,410 --> 00:46:26,737 you wouldn't get on that plane. 859 00:46:26,820 --> 00:46:28,417 That's right. 25% is too high. 860 00:46:28,500 --> 00:46:30,937 We're trying to make that probability much, much lower. 861 00:46:31,020 --> 00:46:33,210 That's, that is, that is the goal. 862 00:47:01,770 --> 00:47:04,597 How do you find your zen? How do you relax? 863 00:47:04,680 --> 00:47:07,927 You know, it honestly, a lot of it's just exposure to, to it. 864 00:47:08,010 --> 00:47:11,527 You know, I sometimes I'll just like, you know, I'll just take a weekend 865 00:47:11,610 --> 00:47:13,477 and I'll like play some video games. 866 00:47:13,560 --> 00:47:15,637 Sometimes with Daniela Me and my wife go to Italy. 867 00:47:15,720 --> 00:47:20,197 Sometimes we have a horse there, so I'll just, I'll just sit there next. 868 00:47:20,280 --> 00:47:21,487 I'll just, I'll just look at the horse 869 00:47:21,570 --> 00:47:25,147 and I'll be like, you know, Calypso, who's her horse like, 870 00:47:25,230 --> 00:47:29,800 you know, she doesn't know about any of this, like she's just a happy horse.