1 00:00:00,240 --> 00:00:03,597 One thing that's real is that, you know, every 3 months we make a model that's 2 00:00:03,680 --> 00:00:07,357 smarter than the month than the model we made before. And indeed, the models are 3 00:00:07,440 --> 00:00:10,957 slowly getting to where they're, you know, better than most humans at most 4 00:00:11,040 --> 00:00:14,877 things. Maybe at some point better all humans at than all things. The other 5 00:00:14,960 --> 00:00:19,517 thing I think is real is, you know, the model is not building itself in the 6 00:00:19,600 --> 00:00:23,437 sense that like I have a specific copy of the model that's like making a 7 00:00:23,520 --> 00:00:27,037 specific copy of the the, you know, the the next model. you know, we don't have 8 00:00:27,120 --> 00:00:29,597 our self-replicating, you know, nanobots 9 00:00:29,680 --> 00:00:32,077 or robots or or whatever, but but what 10 00:00:32,160 --> 00:00:34,397 absolutely is happening is that within 11 00:00:34,480 --> 00:00:37,117 Anthropic and elsewhere, we are using 12 00:00:37,200 --> 00:00:40,317 Claude to help build products on top of 13 00:00:40,400 --> 00:00:43,277 Claude and to help train the next Claude 14 00:00:43,360 --> 00:00:47,997 to help serve Claude faster. Um, you know, six months ago, I I made this 15 00:00:48,080 --> 00:00:52,157 prediction that, you know, in in six months, 90% of code would be written by 16 00:00:52,240 --> 00:00:56,957 by by AI models. Some people think that prediction is wrong but within Anthropic 17 00:00:57,040 --> 00:01:01,277 and within a number of companies that we work with that is absolutely true. Um 18 00:01:01,360 --> 00:01:04,717 now 90 you know >> so you're saying that 90% of all code at 19 00:01:04,800 --> 00:01:07,597 anthropics being written by the by the model today 20 00:01:07,680 --> 00:01:10,797 >> on on many teams you know not uniformly every 21 00:01:10,880 --> 00:01:14,477 >> is that something that you've kind of provided something kind of 22 00:01:14,560 --> 00:01:18,397 >> something auditable around that or like shown how that's happened. Yeah, the 23 00:01:18,480 --> 00:01:23,757 teams play an editing and supervisory role, but you know, Claude can both you 24 00:01:23,840 --> 00:01:27,197 can say write this feature and claude will do it and then the human kind of 25 00:01:27,280 --> 00:01:29,517 looks over the code or asks for help 26 00:01:29,600 --> 00:01:31,757 from another another model, another copy 27 00:01:31,840 --> 00:01:36,637 of Claude. Um uh the other thing is sometimes we have these long running 28 00:01:36,720 --> 00:01:40,397 bugs. You know, when we were training our our the most recently released 29 00:01:40,480 --> 00:01:44,237 Claude, it was like, you know, the cluster was broken. There was this bug. 30 00:01:44,320 --> 00:01:48,317 Engineers were spending a few days on it. And, you know, we just we just went 31 00:01:48,400 --> 00:01:50,797 to Claude and we said, I don't know, just kind of play around with the 32 00:01:50,880 --> 00:01:53,997 cluster and see if you can figure out what's wrong. And, you know, Claude 33 00:01:54,080 --> 00:01:57,917 found this just super obscure bug that the engineers had missed. So, this this 34 00:01:58,000 --> 00:01:59,837 was one area where it was like Claude is 35 00:01:59,920 --> 00:02:01,677 like having a very talented teammate. So 36 00:02:01,760 --> 00:02:05,117 if claude is now running writing 90% of 37 00:02:05,200 --> 00:02:07,597 all of the code in enthropic do you need 38 00:02:07,680 --> 00:02:09,997 less engineers if 90% of all the code 39 00:02:10,080 --> 00:02:12,717 when will it be 95% or 99%. 40 00:02:12,800 --> 00:02:17,277 >> Yeah. So you know I I you know I think people can misinterpret it as you know 41 00:02:17,360 --> 00:02:21,357 you know like Claude writing 90% of the code means we're firing 90% of the 42 00:02:21,440 --> 00:02:24,637 software engineers. But you know if you're familiar with the principle of 43 00:02:24,720 --> 00:02:26,237 >> well you have made some very aggressive 44 00:02:26,320 --> 00:02:29,597 comments around firing executives 45 00:02:29,680 --> 00:02:33,517 >> that that so um 46 00:02:33,600 --> 00:02:35,037 >> I just want to put two and two together 47 00:02:35,120 --> 00:02:36,717 to get four. Okay. 48 00:02:36,800 --> 00:02:42,077 >> Um uh so uh um you know if >> one thing I've learned in knowing you 49 00:02:42,160 --> 00:02:45,117 for so long is I have to come back and kind of like get that qualifying 50 00:02:45,200 --> 00:02:49,517 question again. Yes. Because you're go you know you you're you're going fast. 51 00:02:49,600 --> 00:02:54,077 So trying to I'm trying to keep up with you and it's amazing but I want to just 52 00:02:54,160 --> 00:02:55,837 make sure I understand exactly what you're saying. 53 00:02:55,920 --> 00:02:57,437 >> Yes, absolutely. Let me let me let me 54 00:02:57,520 --> 00:03:00,797 take them one by one. So um uh you know 55 00:03:00,880 --> 00:03:04,157 under comparative advantage if Claude is 56 00:03:04,240 --> 00:03:07,517 writing 90% of the code what that means 57 00:03:07,600 --> 00:03:12,317 usually is you need just as many software engineers you might need more 58 00:03:12,400 --> 00:03:16,637 because they can they can then be more leveraged. they can focus on the 10% 59 00:03:16,720 --> 00:03:18,637 that's editing the code or writing the 60 00:03:18,720 --> 00:03:21,517 10% that's the hardest or supervising a 61 00:03:21,600 --> 00:03:25,917 group of a of AI models. And so what happens is, you know, you can just end 62 00:03:26,000 --> 00:03:28,317 up being 10 times more productive 63 00:03:28,400 --> 00:03:31,597 because the remaining 10% is is now a 64 00:03:31,680 --> 00:03:35,437 complement to the to the AI system that's that's getting much better at at 65 00:03:35,520 --> 00:03:37,917 getting much better at what it's producing. So So 66 00:03:38,000 --> 00:03:40,957 >> it's a rebalancing of the work is what you're really saying. 67 00:03:41,040 --> 00:03:45,040 >> It's it's not a replacement. It's on a replacement.