1 00:00:00,060 --> 00:00:03,720 Can we start with the kind of rough status of how the industry is going? 2 00:00:04,410 --> 00:00:09,020 You and your rivals, how close are we now to artificial general intelligence? 3 00:00:09,540 --> 00:00:13,500 Set the stage for us first and then we can talk about the morality of it all. 4 00:00:13,650 --> 00:00:15,547 Yeah. Yeah. Thanks for. Thanks for having me. 5 00:00:15,630 --> 00:00:19,957 I mean, you know, I've never liked the artificial general intelligence or 6 00:00:20,040 --> 00:00:24,227 superintelligence. Not because I don't think is very powerful. Right. 7 00:00:24,310 --> 00:00:27,187 I'm not I'm not a skeptic. I'm actually extreme in terms of my 8 00:00:27,270 --> 00:00:29,557 views of how powerful the technology is going to be. 9 00:00:29,640 --> 00:00:32,977 But it's the wrong model for thinking about it, that there will be some one 10 00:00:33,060 --> 00:00:35,820 point where we build something completely different. 11 00:00:36,240 --> 00:00:41,017 What we see here, actually, what we've seen for the last ten years, maybe even 12 00:00:41,100 --> 00:00:45,157 15, and you know, me and therapist co-founders were among the first to 13 00:00:45,240 --> 00:00:50,250 document it is this very smooth, exponential, exponential process. 14 00:00:50,550 --> 00:00:54,870 Just like in the nineties, you saw Moore's Law that, you know, the the 15 00:00:54,990 --> 00:00:58,740 amount of computing power would double every 12 months or every 18 months. 16 00:00:58,980 --> 00:01:04,057 We have a moore's Law like law, except it's for intelligence itself, for for 17 00:01:04,140 --> 00:01:07,350 the cognitive ability of the model across many tasks. 18 00:01:07,680 --> 00:01:12,090 And so what we are seeing is that that cognitive ability is, you know, 19 00:01:12,750 --> 00:01:16,457 depending on how you measure it, doubling every, you know, four to 4 to 20 00:01:16,540 --> 00:01:19,477 12 months. And so, you know, we're just climbing 21 00:01:19,560 --> 00:01:22,897 the ladder of cognitive ability. And where we are now is, you know, to 22 00:01:22,980 --> 00:01:27,637 take coding as one example that's been exploding really fast over the last year 23 00:01:27,720 --> 00:01:29,857 or two. I have engineers. 24 00:01:29,940 --> 00:01:34,747 In fact, the team that leads one of our lead products called Code, which is the 25 00:01:34,830 --> 00:01:41,377 way you use our models for for coding. He says he hasn't written any code in 26 00:01:41,460 --> 00:01:45,787 the last two months. He's it's all caught he's he's edited it 27 00:01:45,870 --> 00:01:48,487 he's looked at it but it's all been written by Claude. 28 00:01:48,570 --> 00:01:50,870 Indeed. We recently released this thing called 29 00:01:50,970 --> 00:01:53,520 Coachwork, which was bringing code code to, 30 00:01:54,870 --> 00:01:58,830 you know, non-coding tasks. It really seems to have taken off. 31 00:01:58,980 --> 00:02:02,760 We wrote it in like a week and a half, almost entirely with code code. 32 00:02:03,060 --> 00:02:06,720 So I think we're entering the world where, you know, the junior level 33 00:02:06,900 --> 00:02:11,137 software engineers, maybe many of the tasks of the more senior level software 34 00:02:11,220 --> 00:02:15,997 engineers are starting to be done, you know, most of the way by buying AI 35 00:02:16,080 --> 00:02:20,017 systems now that's going to go further can be more end to end the project to me 36 00:02:20,100 --> 00:02:22,650 more. But I think we're going to be surprised 37 00:02:22,890 --> 00:02:25,117 at how the exponential turns upward. Right? 38 00:02:25,200 --> 00:02:28,777 The whole the whole thing about exponentials is, you know, it looks like 39 00:02:28,860 --> 00:02:32,227 it's going very, very slowly. It speeds up a little bit and then it 40 00:02:32,310 --> 00:02:35,617 just zooms past you. And I think we're I think we're on the 41 00:02:35,700 --> 00:02:37,837 precipice. I think we're a year or two away from 42 00:02:37,920 --> 00:02:40,477 it, really zooming past us. So it's very like going bankrupt. 43 00:02:40,560 --> 00:02:45,787 But when when this happens in terms of the kind of running order does yourself, 44 00:02:45,870 --> 00:02:50,347 this Gemini, this open air, where do you put you all together at the moment 45 00:02:50,430 --> 00:02:51,307 roughly? Yeah. 46 00:02:51,390 --> 00:02:58,087 I mean, you know, so I think I think as the field has developed, you know, I 47 00:02:58,170 --> 00:03:01,177 think it's no longer appropriate to think of it as like, you know, like a 48 00:03:01,260 --> 00:03:04,567 scalar, you know, like runners in a race who are at one position. 49 00:03:04,650 --> 00:03:06,937 The players have gone in different directions. 50 00:03:07,020 --> 00:03:11,730 So some of the other players have gone into very consumer oriented direction. 51 00:03:11,940 --> 00:03:16,597 And that leads you to make your models, you know, super humanly engaging or 52 00:03:16,680 --> 00:03:21,307 super humanly good at recommending shopping or ads or various things like 53 00:03:21,390 --> 00:03:24,457 that. Anthropic has focused, I think, first 54 00:03:24,540 --> 00:03:29,617 and foremost on enterprises, developers. And to the extent we do consumer, we're 55 00:03:29,700 --> 00:03:35,557 very focused on productivity and the kind of high value end of the of the 56 00:03:35,640 --> 00:03:38,617 consumer work. And so that that gives very that gives 57 00:03:38,700 --> 00:03:39,937 very different incentives. Right? 58 00:03:40,020 --> 00:03:43,507 If you if you want to get things done. You know, we talked about AGI in 59 00:03:43,590 --> 00:03:47,227 superintelligence before. One of the things I would say is that, 60 00:03:47,310 --> 00:03:50,017 you know, there are superintelligence is today and they're basically large 61 00:03:50,100 --> 00:03:53,050 corporations, right? They're smarter than any human can be, 62 00:03:53,730 --> 00:03:57,607 you know, solving the problem of, you know, shipping, shipping commerce at the 63 00:03:57,690 --> 00:04:01,410 lowest possible cost or making solar panels the lowest possible cost or 64 00:04:01,590 --> 00:04:04,447 launching rockets at the lowest possible possible cost. 65 00:04:04,530 --> 00:04:09,187 And so there's really high returns to intelligence in this area, which I think 66 00:04:09,270 --> 00:04:13,380 incentivizes us to to kind of to kind of build the right thing. 67 00:04:13,680 --> 00:04:18,007 It's it's also, you know, I think it's I think it's a business that is more 68 00:04:18,090 --> 00:04:22,040 stable than consumer. We don't need you know, we don't need ads. We don't need, 69 00:04:23,820 --> 00:04:29,047 you know, large numbers of free users. We can we can just very directly create 70 00:04:29,130 --> 00:04:33,457 value. Right? There aren't all these weird externalities of, like your prioritizing 71 00:04:33,540 --> 00:04:35,677 engagement. You're prioritizing these other things, 72 00:04:35,760 --> 00:04:39,487 you're generating all this slop. We just make stuff that that people can 73 00:04:39,570 --> 00:04:44,647 use. Year ago here, you what? You said the Chinese were catching up a bit. 74 00:04:44,730 --> 00:04:47,130 Do you think now the Chinese have fallen behind? 75 00:04:47,430 --> 00:04:50,947 You know, I think they never really caught up that much. 76 00:04:51,030 --> 00:04:54,430 So, you know, of course, there was this huge excitement around deep. 77 00:04:54,720 --> 00:04:59,580 Yeah, right. But, you know, the truth was a couple of things. One. 78 00:05:00,300 --> 00:05:02,867 You know, those models are very optimized for the benchmarks. 79 00:05:02,950 --> 00:05:07,680 It's actually very easy to optimize the model for a finite list of benchmarks. 80 00:05:07,950 --> 00:05:11,580 When we go out into the world, right, when we're when we're when we're, 81 00:05:12,630 --> 00:05:16,417 you know, competing against other companies for enterprise contracts, we 82 00:05:16,500 --> 00:05:20,040 see just honestly and candidly, we see Google and we see Openai. 83 00:05:20,340 --> 00:05:22,987 Every once in a while, we see a couple other U.S. 84 00:05:23,070 --> 00:05:28,507 players. I have almost never lost a deal, lost a 85 00:05:28,590 --> 00:05:32,467 contract to to a Chinese model. But now you have the Trump 86 00:05:32,550 --> 00:05:36,150 administration, and I think you've already protested about this, giving 87 00:05:36,300 --> 00:05:39,120 high speed chips and video chips to the Chinese. 88 00:05:39,210 --> 00:05:39,977 That's right. That's right. 89 00:05:40,060 --> 00:05:43,327 The thing that is holding them back and they've said it themselves. 90 00:05:43,410 --> 00:05:47,737 The CEOs of these companies say it's the embargo on chips that's holding us back. 91 00:05:47,820 --> 00:05:52,147 They explicitly say this. And now, indeed, you know, there are 92 00:05:52,230 --> 00:05:57,187 some policies and I hope they change their mind to, you know, to explicitly 93 00:05:57,270 --> 00:06:01,117 send not quite our latest generation of chips, although it was reported that 94 00:06:01,200 --> 00:06:04,950 even that was being considered. But, you know, the generation of chips, 95 00:06:05,190 --> 00:06:08,580 that's that's just one back that's still extremely powerful. 96 00:06:09,270 --> 00:06:14,857 And we are many years ahead of China in terms of our in terms of our ability to 97 00:06:14,940 --> 00:06:17,587 make chips. So I think it would be a big mistake to 98 00:06:17,670 --> 00:06:21,847 ship these chips. You know, the the analogy I thought of, 99 00:06:21,930 --> 00:06:27,360 if you think about the incredible national security implications of 100 00:06:27,630 --> 00:06:31,117 building model, building models that are essentially cognition, that are 101 00:06:31,200 --> 00:06:32,497 essentially intelligence. Right. 102 00:06:32,580 --> 00:06:36,247 I've called where we're going with this, a country of geniuses in the data 103 00:06:36,330 --> 00:06:40,170 center, right. So imagine 100,000, 100 million people 104 00:06:40,410 --> 00:06:44,317 smarter than any Nobel Prize winner. And it's going to be under the control 105 00:06:44,400 --> 00:06:48,967 of one, one country or another. So so I think this is crazy. 106 00:06:49,050 --> 00:06:52,777 I think it's you know, it's a bit like, you know, I don't know, like selling, 107 00:06:52,860 --> 00:06:56,617 selling, you know, nuclear weapons to North Korea and, you know, bragging. 108 00:06:56,700 --> 00:07:00,337 Oh, yeah, But we made that. You know, your friend your friend David 109 00:07:00,420 --> 00:07:05,377 Sachs is basically arming the Chinese. You know, I wouldn't refer to any 110 00:07:05,460 --> 00:07:07,957 particular people, but, you know, I would I would just say that this 111 00:07:08,040 --> 00:07:11,190 particular policy, I think, is is akin to. 112 00:07:11,580 --> 00:07:13,890 Yeah, not not, not, not, I think well-advised. 113 00:07:14,220 --> 00:07:17,467 Another issue in this, you know, could be you could be right all this 114 00:07:17,550 --> 00:07:21,397 technology be going in the right direction but economically we could be 115 00:07:21,480 --> 00:07:24,420 heading towards a bubble. Do you think that's true? 116 00:07:24,600 --> 00:07:25,237 Yeah. Yeah. 117 00:07:25,320 --> 00:07:31,717 So I would separate out the two things. One is the basic exponential, and that's 118 00:07:31,800 --> 00:07:34,777 what we've been talking about. That's been kind of the technological 119 00:07:34,860 --> 00:07:38,587 direction. And there, you know, just like with 120 00:07:38,670 --> 00:07:43,967 Moore's Law, you can never be sure that the technology is sort of fully going to 121 00:07:44,050 --> 00:07:45,247 advanced. It's going to keep going. 122 00:07:45,330 --> 00:07:47,630 Right? It's a fundamentally inductive problem. 123 00:07:47,970 --> 00:07:53,017 But now that I've seen well over a decade of it, I would say I am pretty 124 00:07:53,100 --> 00:07:57,067 confident that it's going to continue, or at least I'm more confident than I've 125 00:07:57,150 --> 00:08:01,297 ever been at any time in the past that it's going to continue to where the 126 00:08:01,380 --> 00:08:05,760 models are basically smarter than humans at almost everything. 127 00:08:06,030 --> 00:08:09,427 And I think there's a good chance that even happens in the next year or two. 128 00:08:09,510 --> 00:08:13,817 Again, it seems far away. But but this property of exponential 129 00:08:13,900 --> 00:08:16,537 that they like, they they they catch you by surprise. 130 00:08:16,620 --> 00:08:20,287 And if it's not a year or two, I think it's pretty likely that it's, you know, 131 00:08:20,370 --> 00:08:22,740 at least less than five years within the 2020s. 132 00:08:22,980 --> 00:08:24,787 I think this moment will come in the 2020. 133 00:08:24,870 --> 00:08:29,277 So so, you know, kind of I'll get into that economic angle. 134 00:08:29,360 --> 00:08:33,780 Yeah, I'll get to the economic diffusion, which I think is complicated, 135 00:08:33,990 --> 00:08:37,087 but I don't want anyone to forget like that. 136 00:08:37,170 --> 00:08:42,360 You know that as long as we have that fundamental technological gain, like 137 00:08:42,630 --> 00:08:46,207 there's going to be many trillions of revenue, maybe many trillions per 138 00:08:46,290 --> 00:08:50,490 company in this in this area, because the economic potential is so great. 139 00:08:50,910 --> 00:08:57,487 Now there's a separate thing, which is that we don't know how fast enterprises 140 00:08:57,570 --> 00:09:02,040 companies are actually going to be able to use this technology. 141 00:09:02,190 --> 00:09:05,077 Right. The the the uses of this, what the 142 00:09:05,160 --> 00:09:09,517 technology is capable of today is probably ten times what the enterprise 143 00:09:09,600 --> 00:09:11,940 of the world are able to are able to deploy. 144 00:09:12,210 --> 00:09:17,227 And I see it with our customers every day where I'll talk to a CEO and they'll 145 00:09:17,310 --> 00:09:21,360 understand and their executive team will understand the power of this technology, 146 00:09:21,630 --> 00:09:25,237 you know, for for automating customer service, for coding, for many other 147 00:09:25,320 --> 00:09:27,637 things. But they have a company of tens of 148 00:09:27,720 --> 00:09:31,897 thousands of people who are perfectly brilliant people but expert in something 149 00:09:31,980 --> 00:09:35,737 that is not A.I. and have to learn to use A.I. 150 00:09:35,820 --> 00:09:39,247 and that you know, different names for it change management, enterprise 151 00:09:39,330 --> 00:09:43,147 transformation, whatever it's called the need to do. 152 00:09:43,230 --> 00:09:45,930 This is very slow. It can take years. 153 00:09:46,290 --> 00:09:50,587 And so we have this powerful technology that I have an incredible amount of 154 00:09:50,670 --> 00:09:53,340 confidence will generate trillions in revenue. 155 00:09:53,580 --> 00:09:58,147 But we don't know exactly when, you know, plus, plus or minus a few years. 156 00:09:58,230 --> 00:10:02,587 We don't know exactly when. And and and in the meantime, companies 157 00:10:02,670 --> 00:10:05,280 have to buy compute to serve all that revenue. 158 00:10:05,550 --> 00:10:09,487 And you don't want to buy too much because, you know, you could financially 159 00:10:09,570 --> 00:10:13,717 overextend yourself and you don't want to buy too little because then you can't 160 00:10:13,800 --> 00:10:18,097 serve all the revenue. And so that's where the kind of economic 161 00:10:18,180 --> 00:10:21,907 and financial problems. So you are involved in building data 162 00:10:21,990 --> 00:10:25,440 centers, spending vast fortunes. You've got the deals with Belvedere. 163 00:10:25,620 --> 00:10:27,757 So you worry that that's going to come back and bite you. 164 00:10:27,840 --> 00:10:32,977 So so we have some advantages in this. There is a stability to enterprise 165 00:10:33,060 --> 00:10:34,987 business that there is not to consumer, right. 166 00:10:35,070 --> 00:10:40,057 Consumers, very fickle enterprise purchasing enterprise predictions are 167 00:10:40,140 --> 00:10:43,207 predictable. Also, enterprise tends to have better 168 00:10:43,290 --> 00:10:46,237 margins. The margin is basically how much buffer 169 00:10:46,320 --> 00:10:50,270 you have between buying too little and buying too much between what you have to 170 00:10:50,370 --> 00:10:53,130 pay for and what you have to support in revenue. 171 00:10:53,340 --> 00:10:57,487 So we have a number of advantages here. But I you know, I won't deny that there 172 00:10:57,570 --> 00:11:02,940 is some inherent risk in any in all of the companies to this process. 173 00:11:03,120 --> 00:11:08,377 Whenever you have an upside whose amount is uncertain or even whose timing is 174 00:11:08,460 --> 00:11:13,417 uncertain, and you have to make financial decisions somewhat blind to 175 00:11:13,500 --> 00:11:18,187 those things, you have to you have to decide early to, you know, to to build 176 00:11:18,270 --> 00:11:21,900 out capital, to build out resources a few years in advance. 177 00:11:22,170 --> 00:11:25,537 There's going to be some inherent risk that's driven by that uncertainty. 178 00:11:25,620 --> 00:11:29,130 And I do think that some companies have probably overbought. 179 00:11:29,430 --> 00:11:31,590 I'm not able to look at their financials. 180 00:11:31,860 --> 00:11:35,557 I just know what they announce. And I'm like, Whoa, I wouldn't 181 00:11:35,640 --> 00:11:39,067 necessarily have done that. I'm pretty happy with the decisions that 182 00:11:39,150 --> 00:11:42,607 we've made. But, you know, and I can't be I can't be 183 00:11:42,690 --> 00:11:47,767 sure about others, but it is entirely consistent that this could be the most 184 00:11:47,850 --> 00:11:51,847 transformative technology in the history of the world, that some companies will 185 00:11:51,930 --> 00:11:55,147 do really well. But but not every company may very 186 00:11:55,230 --> 00:11:57,067 quickly on that. As you said, you own the enterprise 187 00:11:57,150 --> 00:12:00,217 side. On the consumer side, Openai with a big 188 00:12:00,300 --> 00:12:01,987 lead. You know, there seems to be look to 189 00:12:02,070 --> 00:12:05,170 Gemini as an eye catching up. Is that your impression as well? 190 00:12:05,370 --> 00:12:09,180 I mean, you know, we're not in that market, so I so I don't know about that. 191 00:12:09,870 --> 00:12:13,057 Yeah. You know, the statistics would seem to 192 00:12:13,140 --> 00:12:16,897 suggest that it would seem to suggest that, you know, with their kind of 193 00:12:16,980 --> 00:12:21,097 greater distribution, with their greater existing reach, they've been able to 194 00:12:21,180 --> 00:12:23,637 make a lot of progress in. Kind of recent times. 195 00:12:23,720 --> 00:12:26,457 And again, this is why, you know, consumers fickle. 196 00:12:26,540 --> 00:12:31,707 This is why I would be careful if, you know, if I were a consumer company in in 197 00:12:31,790 --> 00:12:36,177 in you know, in kind of you know, what you know in what was on my balance 198 00:12:36,260 --> 00:12:37,357 sheet. Let's jump then. 199 00:12:37,440 --> 00:12:39,927 It's that big question which we originally asked Claude at the 200 00:12:40,010 --> 00:12:45,177 beginning, will I do do do good. Obviously, as you said, it's going to 201 00:12:45,260 --> 00:12:48,800 have a positive effect on GDP, perhaps on your wealth, too. 202 00:12:49,190 --> 00:12:52,227 But what about employment? Last year you predicted the kind of 203 00:12:52,310 --> 00:12:56,637 white collar bloodbath. You said it would wipe out 50% of entry 204 00:12:56,720 --> 00:13:00,410 level jobs. That's now four and a half years away. 205 00:13:00,860 --> 00:13:02,877 Do you still stand by that? Yeah. 206 00:13:02,960 --> 00:13:09,207 So, you know, I you know, my view of of of I there's maybe two axes in terms of 207 00:13:09,290 --> 00:13:13,250 which two think there's good things happening versus bad things happening. 208 00:13:13,550 --> 00:13:16,197 And there's like A.I. is a small deal versus A.I. 209 00:13:16,280 --> 00:13:19,797 is a big deal. You know, my view is on the extreme side 210 00:13:19,880 --> 00:13:25,167 of I is a big deal, but I'm kind of in both side, both both top quadrants where 211 00:13:25,250 --> 00:13:27,297 I think some really good things will happen. 212 00:13:27,380 --> 00:13:33,200 And if we don't act to prevent them, some really bad things will happen. 213 00:13:34,010 --> 00:13:37,077 You know, we you know, I wouldn't be building this technology if I didn't 214 00:13:37,160 --> 00:13:41,697 believe that the good outweighed the bad and that we could mitigate the bad. 215 00:13:41,780 --> 00:13:44,247 That's why I warn about the bad, so that we could We can. 216 00:13:44,330 --> 00:13:45,717 How do you how do you think we mitigate it? 217 00:13:45,800 --> 00:13:48,320 Does that mean things like taxation? Yeah. 218 00:13:48,500 --> 00:13:52,340 Well, let me let me just get to what I think the kind of the situation is. 219 00:13:52,640 --> 00:13:57,087 You know, I think exactly what you said. I think we could have this very unusual 220 00:13:57,170 --> 00:14:03,590 combination of very fast GDP growth and high unemployment or at least 221 00:14:03,890 --> 00:14:09,027 underemployment or, you know, low wage job, a lot of low wage jobs, high 222 00:14:09,110 --> 00:14:11,637 inequality. I don't think that's a macro correct me 223 00:14:11,720 --> 00:14:13,587 if I'm wrong. I don't think that's a macroeconomic 224 00:14:13,670 --> 00:14:17,547 combination we've ever seen before. You think of fast growth, you're like, 225 00:14:17,630 --> 00:14:21,237 well, okay, maybe there's inflation, but you know, you're not going to have high 226 00:14:21,320 --> 00:14:26,007 unemployment when there's fast growth. I think this technology is a bit 227 00:14:26,090 --> 00:14:29,787 different because it's extreme in the way in the way it's going to it's going 228 00:14:29,870 --> 00:14:34,137 to it's going to generate value. But also because it's moving up the 229 00:14:34,220 --> 00:14:37,647 cognitive waterline, there's going to be, unfortunately, a whole class of 230 00:14:37,730 --> 00:14:41,907 people who are who are, I think, across a lot of industries going to have a hard 231 00:14:41,990 --> 00:14:44,967 time coping. And that's really a problem we need to 232 00:14:45,050 --> 00:14:47,997 solve. So to get to the question you just 233 00:14:48,080 --> 00:14:50,430 asked, there's a few things Anthropic is doing. 234 00:14:51,020 --> 00:14:56,337 One is we maintain for almost a year now what we call the anthropic economic 235 00:14:56,420 --> 00:15:00,980 index, which is tracking how our models are used in real time. 236 00:15:01,250 --> 00:15:05,397 And because we see all these conversations, we can use quite itself 237 00:15:05,480 --> 00:15:10,427 to in a privacy preserving manner. Look across all the conversations and 238 00:15:10,510 --> 00:15:15,437 ask questions like, Is someone using this to augment a task to work together 239 00:15:15,520 --> 00:15:18,580 with the model or to delegate or fully automated task? 240 00:15:18,790 --> 00:15:22,667 What are the industries people are using called for statistically by 241 00:15:22,750 --> 00:15:26,957 distribution? What are the subtasks within those industries? Right. 242 00:15:27,040 --> 00:15:30,167 We can get into, you know, a lot of these like lead, you know, these very 243 00:15:30,250 --> 00:15:33,287 fine grained Labor Department statistics and ask all about it. 244 00:15:33,370 --> 00:15:37,037 Which states are using cloud more? What is we can watch the economic 245 00:15:37,120 --> 00:15:41,327 diffusion of cloud in real time. And and so the reason to do all this is 246 00:15:41,410 --> 00:15:45,260 that I don't think you can make good policy if you don't have the right data. 247 00:15:45,610 --> 00:15:49,097 And I worry that the data that the government produces as comprehensive as 248 00:15:49,180 --> 00:15:53,177 it is, just doesn't move fast enough and isn't detailed enough for this. 249 00:15:53,260 --> 00:15:57,130 So so, you know, that is that is kind of our first contribution. 250 00:15:58,000 --> 00:16:02,387 We are thinking increasingly about this kind of I don't know if you want to call 251 00:16:02,470 --> 00:16:05,980 it retraining or ability to help people adapt. 252 00:16:06,310 --> 00:16:10,577 It's it's partly in line with kind of what we do in terms of go to market, 253 00:16:10,660 --> 00:16:12,857 Right. Part of our go to market activity is how 254 00:16:12,940 --> 00:16:15,167 to help you with that, but with great risk, great respect. 255 00:16:15,250 --> 00:16:18,407 I mean, you can do these things, but fundamentally it's a societal problem. 256 00:16:18,490 --> 00:16:21,737 You know, government, if you think about it, what you're describing is a perfect 257 00:16:21,820 --> 00:16:25,060 storm. You have GDP going up, the wealth of 258 00:16:25,240 --> 00:16:27,647 certain people going massively. Absolutely. 259 00:16:27,730 --> 00:16:32,380 And you are saying 50% of entry level jobs go people in real distressed. 260 00:16:32,680 --> 00:16:36,727 You are surely going to see a political. Change coming from this. 261 00:16:36,810 --> 00:16:39,627 And my personal bet would be, yes, you're going to get people trying to 262 00:16:39,710 --> 00:16:44,337 restrain the tech barons, but you would also get people saying, let's have 263 00:16:44,420 --> 00:16:46,400 higher taxes. We need to pay for this. 264 00:16:46,640 --> 00:16:50,997 Do you think higher taxes are coming? So, you know, my view again, you know, I 265 00:16:51,080 --> 00:16:54,987 was going to the kind of voluntary stuff we could do first, but but actually, I 266 00:16:55,070 --> 00:16:58,107 actually do believe that. I think this one is going to be big 267 00:16:58,190 --> 00:17:02,487 enough that, you know, at some point I think everyone's going to come to the 268 00:17:02,570 --> 00:17:08,607 realization that there needs to be some kind of macroeconomic intervention 269 00:17:08,690 --> 00:17:11,217 there. You know, if we if we look at just the 270 00:17:11,300 --> 00:17:18,207 disparities in wealth that we have now, if we look at it as a fraction of GDP, I 271 00:17:18,290 --> 00:17:21,020 believe we've kind of exceeded the Gilded Age already. 272 00:17:21,170 --> 00:17:25,107 And this is mostly without without I. So I think things are going to go even 273 00:17:25,190 --> 00:17:27,717 further. And so, yeah, you know, my my guess is 274 00:17:27,800 --> 00:17:30,087 this is this is not even going to be a partisan thing. 275 00:17:30,170 --> 00:17:32,517 There's a wealth there's a wealth tax now in California. 276 00:17:32,600 --> 00:17:35,760 Are you supportive of that? No, I think it's a great start. 277 00:17:36,170 --> 00:17:40,467 I think that's poorly designed. And, you know, my my my worry would be 278 00:17:40,550 --> 00:17:45,057 if we don't think about these things in a sober way, then we'll get things that 279 00:17:45,140 --> 00:17:48,027 are that are you know, that are that are kind of poorly designed. 280 00:17:48,110 --> 00:17:52,347 So, you know, that would be that would be my message to, you know, the others 281 00:17:52,430 --> 00:17:57,687 in the world who are doing who are doing well in this boom is that, you know, if 282 00:17:57,770 --> 00:18:03,447 we if we don't think proactively about how to make this revolution work for 283 00:18:03,530 --> 00:18:07,500 everyone, we will get these proposals that don't make sense. 284 00:18:08,330 --> 00:18:10,797 Just taking it a bit further, there's you know, people worry about the 285 00:18:10,880 --> 00:18:13,197 economic consequences, but there are other ones. 286 00:18:13,280 --> 00:18:16,767 Not that long ago I went to a panel with, I think a couple of your rivals, a 287 00:18:16,850 --> 00:18:20,877 couple of your peers, where they again, they put forward all the good things I 288 00:18:20,960 --> 00:18:22,377 would do. And then someone said, what about the 289 00:18:22,460 --> 00:18:24,567 risks? And one of them rather casually said, 290 00:18:24,650 --> 00:18:27,327 well, of course there's extinction risk and then moved on to another. 291 00:18:27,410 --> 00:18:31,490 And collectively around the room, people took a deep breath. 292 00:18:31,820 --> 00:18:35,247 And what he was talking about was the possibility of a bad actor getting hold 293 00:18:35,330 --> 00:18:39,107 of it, or the possibility ultimately of things like Claud running things. 294 00:18:39,190 --> 00:18:41,307 What does that fit into your. Yeah, yeah. 295 00:18:41,390 --> 00:18:44,217 You know, I've long been been worried about that as well. 296 00:18:44,300 --> 00:18:47,937 You know, just, just as I said, you know, I'm on the direction of like, this 297 00:18:48,020 --> 00:18:52,130 is a very powerful technology. Therefore the benefits are extreme. 298 00:18:52,280 --> 00:18:55,737 We're going to be able to, you know, do things like really seriously, really 299 00:18:55,820 --> 00:18:59,090 cure cancer or maybe eradicate tropical diseases. 300 00:18:59,450 --> 00:19:03,567 But on the other side, look, we're building cognitive systems that have 301 00:19:03,650 --> 00:19:06,957 their own autonomy, Right. And we really need to think about that. 302 00:19:07,040 --> 00:19:12,807 And lycanthropy has been founded since the beginning with an intention to think 303 00:19:12,890 --> 00:19:13,537 about that. Right. 304 00:19:13,620 --> 00:19:16,617 You know, we publish probably almost every, you know, probably three or four 305 00:19:16,700 --> 00:19:23,187 times a month research on how to control these models, how to make sure that, you 306 00:19:23,270 --> 00:19:26,727 know, they they do what we want them to do and they don't run out of control. 307 00:19:26,810 --> 00:19:31,287 We've pioneered one of my co-founders, Chris Ola, is, you know, kind of 308 00:19:31,370 --> 00:19:34,977 arguably, you know, the inventor of a field called mechanistic 309 00:19:35,060 --> 00:19:38,517 interpretability, which is basically when you look inside the brain, the 310 00:19:38,600 --> 00:19:42,867 artificial brain of of Claude or another A.I. 311 00:19:42,950 --> 00:19:48,590 model and try to trace mechanistically why it's doing exactly what it's doing. 312 00:19:48,800 --> 00:19:54,260 And we've seen things inside the model like, you know, in in lab environments, 313 00:19:54,560 --> 00:19:58,647 you know, sometimes the models will develop the intent to blackmail, the 314 00:19:58,730 --> 00:20:01,317 intent to deceive. And this isn't unique to court. 315 00:20:01,400 --> 00:20:03,550 If anything, this is worse in other models. 316 00:20:03,710 --> 00:20:08,150 These are things that if we don't train the models in the right way can emerge. 317 00:20:08,450 --> 00:20:13,017 And but we've pioneered the science of looking inside them so we can diagnose 318 00:20:13,100 --> 00:20:17,457 them and and, you know, prevent the models from happening, intervene and 319 00:20:17,540 --> 00:20:20,127 retrain the models so they don't behave in this way. 320 00:20:20,210 --> 00:20:24,260 That said, you know, we this is something we have to be careful about. 321 00:20:24,530 --> 00:20:29,390 We've we've, you know, supported various what, you know, light touch, 322 00:20:29,540 --> 00:20:36,327 transparency focused measures for making sure that, you know, all the companies 323 00:20:36,410 --> 00:20:38,817 have to talk about the tests that they run. 324 00:20:38,900 --> 00:20:44,067 We always disclose our tests. We always, you know, basically we try 325 00:20:44,150 --> 00:20:48,417 and test our models to try and really stream them and get them to do the worst 326 00:20:48,500 --> 00:20:52,167 things possible in a test environment so that they never do those things in the 327 00:20:52,250 --> 00:20:56,127 real world. And we generally think every company 328 00:20:56,210 --> 00:20:59,997 should have to disclose or should have to run those tests and should have to 329 00:21:00,080 --> 00:21:03,590 disclose those tests. So this is you spend quite a lot of time 330 00:21:03,680 --> 00:21:05,637 thinking about this. You write pamphlets about it, about the 331 00:21:05,720 --> 00:21:08,787 consequences. There is an impression that most of your 332 00:21:08,870 --> 00:21:12,447 peers are going to head down and they're thinking purely about whether they can 333 00:21:12,530 --> 00:21:15,747 just keep ahead of the other. Do you think your industry is kind of 334 00:21:15,830 --> 00:21:19,497 immature in that way? You know, I can't speak to what the 335 00:21:19,580 --> 00:21:22,640 other players are are doing or why they're doing it. 336 00:21:23,450 --> 00:21:28,310 You know, I think there are at least some other players in in the ecosystem 337 00:21:28,520 --> 00:21:31,587 who I, you know, I think of as responsible who are at least thinking of 338 00:21:31,670 --> 00:21:34,527 things in the right way. I agree there are others who are not. 339 00:21:34,610 --> 00:21:38,407 But. I would say what I have always tried to 340 00:21:38,490 --> 00:21:44,107 do and what Anthropic has always tried to do is, is to set an example and to 341 00:21:44,190 --> 00:21:47,040 try to inspire others to follow the example. 342 00:21:47,220 --> 00:21:50,237 We always publish our work on mechanistic interpretability. 343 00:21:50,320 --> 00:21:54,697 We always publish the tests we run. And honestly, there are a lot of other 344 00:21:54,780 --> 00:21:58,837 companies where, you know, their researchers basically say, Hey, why 345 00:21:58,920 --> 00:22:01,807 can't why can't we do that too? That seems like a responsible thing to 346 00:22:01,890 --> 00:22:03,937 do. That seems like the right way to develop 347 00:22:04,020 --> 00:22:07,387 the technology. And so you can kind of have an effect on 348 00:22:07,470 --> 00:22:11,947 the other players in the ecosystem. 111 very clear zone where you are 349 00:22:12,030 --> 00:22:15,967 different from your peers that you have not very obviously queued up to kiss the 350 00:22:16,050 --> 00:22:18,210 ring of Donald J. Trump. 351 00:22:19,110 --> 00:22:21,217 Are you still? What do you think of the current US 352 00:22:21,300 --> 00:22:24,007 President? You showed no signs of wanting to see 353 00:22:24,090 --> 00:22:26,827 him. Look, so, I mean, you know, I wish I 354 00:22:26,910 --> 00:22:28,327 would. I would really put it in a different 355 00:22:28,410 --> 00:22:33,150 way. I don't think being for or against 356 00:22:33,480 --> 00:22:39,997 administrations or for or against politicians is is the right approach or 357 00:22:40,080 --> 00:22:44,670 that anthropic has anything to say one way or another on those topics. 358 00:22:44,880 --> 00:22:50,617 What I would say is that what Anthropic knows is I Anthropic knows very well the 359 00:22:50,700 --> 00:22:56,137 policy issues around A.I. and our approach, which, you know, I 360 00:22:56,220 --> 00:22:59,347 can't speak for others, but you know, indeed, indeed our approach may have 361 00:22:59,430 --> 00:23:05,077 been different is we think through the issues, we try and get a substantive 362 00:23:05,160 --> 00:23:09,570 view based on those issues, and then we say what we think. 363 00:23:10,020 --> 00:23:15,277 And sometimes we will disagree with the current administration, just as we 364 00:23:15,360 --> 00:23:20,317 sometimes just like with China, with the last administration, and sometimes we 365 00:23:20,400 --> 00:23:22,987 agree. And it's worth highlighting the areas of 366 00:23:23,070 --> 00:23:26,340 agreement like the energy data center buildouts. 367 00:23:26,610 --> 00:23:30,270 We did this health pledge at the White House. 368 00:23:31,170 --> 00:23:35,587 You know, there are a number of areas, you know, when we saw the you know, the 369 00:23:35,670 --> 00:23:40,050 White House's air action plan last summer, actually, we liked most of that. 370 00:23:40,350 --> 00:23:42,700 That was actually a very well-written document. 371 00:23:42,960 --> 00:23:45,697 So this is complex. There's not against and for. 372 00:23:45,780 --> 00:23:52,020 But again, when it comes to things like the chips in China, yeah, we disagree 373 00:23:52,320 --> 00:23:56,670 when it comes to putting a moratorium on state deregulation. 374 00:23:56,910 --> 00:23:59,850 Yeah, we disagree because we disagree on the merits. 375 00:24:00,030 --> 00:24:03,390 It's not about liking or disliking a particular person. 376 00:24:03,660 --> 00:24:07,657 I don't I don't think that that that kind of thinking is going to get us out 377 00:24:07,740 --> 00:24:11,707 of this situation that we're in. We have to think based on substance. 378 00:24:11,790 --> 00:24:14,227 Are you going to see him when he comes to Davos tomorrow? 379 00:24:14,310 --> 00:24:17,047 I may. I think that would be an interesting 380 00:24:17,130 --> 00:24:19,987 change. One final last question. 381 00:24:20,070 --> 00:24:22,387 What happens to entropic? What happens? 382 00:24:22,470 --> 00:24:27,367 Do you have a I think what since we have supposedly a 350 billion implied 383 00:24:27,450 --> 00:24:30,090 valuation, would you consider an IPO this year? 384 00:24:30,420 --> 00:24:35,437 Look, I mean, you know, I we are we are most focused on, you know, just trying 385 00:24:35,520 --> 00:24:39,367 to make the best models and just trying to, you know, build products on top of 386 00:24:39,450 --> 00:24:43,297 those models and just trying to, you know, sell those models to enterprises 387 00:24:43,380 --> 00:24:48,067 in ways that are useful, you know, their their large capital needs for that for 388 00:24:48,150 --> 00:24:50,587 the for the field. So that that should be considered. 389 00:24:50,670 --> 00:24:52,720 But, you know, that's where our focus is. 390 00:24:53,040 --> 00:24:55,640 That implies that you do need a lot of money though. 391 00:24:56,130 --> 00:24:59,330 I mean, you don't need to ask me to know that this is a capital. 392 00:25:00,840 --> 00:25:04,837 So an IPO isn't completely out of the question and it's never completely out 393 00:25:04,920 --> 00:25:07,207 of the question. Those the IPO and Donald Trump with both 394 00:25:07,290 --> 00:25:12,757 possibles in your life. Derek Derek, thank you very much for 395 00:25:12,840 --> 00:25:15,720 talking to us. We now know all about mechanistic 396 00:25:16,320 --> 00:25:20,137 interoperability and we've had a view inside your brain and it's not a bad 397 00:25:20,220 --> 00:25:22,520 one. Thank you. Thank you for having me, John.