# Dario Amodei — “We are near the end of the exponential”

## Metadata
- Channel: Dwarkesh Patel
- Duration: 142 min
- YouTube: https://www.youtube.com/watch?v=n1E9IZfvGMA

## Transcript

**[00:00] Speaker A:** We talked three years ago. In your view, what has been the biggest update over the last three years? What has been the biggest difference between what it felt like then versus now?  
我们三年前聊过。在你看来,过去三年最大的变化是什么?当时和现在的感觉有什么最大的不同?  
**[00:10] Speaker B:** Broadly speaking, the exponential of the underlying technology has gone about as I expected it to go. There's plus or minus a year or two here and there.  
从大方向来说,底层技术的指数增长基本符合我的预期。在时间上有正负一两年的误差。  
**[00:23] Speaker B:** I don't know that I would have predicted the specific direction of code. But when I look at the exponential, it is roughly what I expected in terms of the march of the models from smart high school student to smart college student to beginning to do PhD and professional stuff, and in the case of code reaching beyond that.  
我不确定自己是否预测到了代码能力发展的具体方向。但当我看指数曲线时,模型从聪明的高中生水平进步到聪明的大学生水平,再到开始做PhD和专业级工作,在代码方面甚至超越了这个水平——这些大致符合我的预期。  
**[00:44] Speaker B:** The frontier is a little bit uneven, but it's roughly what I expected. What has been the most surprising thing is the lack of public recognition of how close we are to the end of the exponential.  
前沿进展有些不均衡,但大体上符合我的预期。最令我惊讶的是,公众并没有意识到我们离指数增长的终点有多近。  
**[01:02] Speaker B:** To me, it is absolutely wild that you have people — within the bubble and outside the bubble — talking about the same tired, old hot-button political issues, when we are near the end of the exponential.  
对我来说,非常疯狂的是,无论是圈内还是圈外的人,在我们接近指数增长终点的时刻,还在讨论那些老掉牙的政治热点话题。  
**[01:19] Speaker A:** I want to understand what that exponential looks like right now. The first question I asked you when we recorded three years ago was, "What's up with scaling and why does it work?"  
我想了解现在这条指数曲线是什么样的。三年前我们录制时我问你的第一个问题是:「扩展定律是怎么回事,为什么会有效?」  
**[01:31] Speaker A:** I have a similar question now, but it feels more complicated. At least from the public's point of view, three years ago there were well-known public trends across many orders of magnitude of compute where you could see how the loss improves.  
我现在有个类似的问题,但感觉更复杂了。至少从公众视角来看,三年前有众所周知的公开趋势,跨越多个数量级的算力你都能看到损失函数如何改善。  
**[01:45] Speaker A:** Now we have RL scaling and there's no publicly known scaling law for it. It's not even clear what the story is. Is this supposed to be teaching the model skills? Is it supposed to be teaching meta-learning? What is the scaling hypothesis at this point?  
现在我们有了RL扩展,但没有公开已知的扩展定律。甚至连故事逻辑都不清楚。这是在教模型技能吗?还是在教元学习?现在的扩展假说到底是什么?  
**[01:59] Speaker B:** I actually have the same hypothesis I had even all the way back in 2017. I think I talked about it last time, but I wrote a doc called "The Big Blob of Compute Hypothesis."  
其实我的假说和2017年时是一样的。我想上次聊过,我写过一份文档叫做「大算力团假说」。  
**[02:12] Speaker B:** It wasn't about the scaling of language models in particular. When I wrote it, GPT-1 had just come out. That was one among many things.  
它并不是专门讲语言模型扩展的。我写这份文档时GPT-1刚刚发布,那只是众多事物之一。  
**[02:22] Speaker B:** Back in those days there was robotics. People tried to work on reasoning as a separate thing from language models, and there was scaling of the kind of RL that happened in AlphaGo and in Dota at OpenAI. People remember StarCraft at DeepMind, AlphaStar.  
那时候有机器人技术。人们试图把推理作为独立于语言模型的东西来研究,还有AlphaGo和OpenAI的Dota中出现的那种RL扩展。人们还记得DeepMind的StarCraft项目AlphaStar。  
**[02:43] Speaker B:** It was written as a more general document. Rich Sutton put out "The Bitter Lesson" a couple years later. The hypothesis is basically the same.  
它是作为一份更通用的文档写的。Rich Sutton几年后发表了「苦涩的教训」。假说基本上是一样的。  
**[02:57] Speaker B:** What it says is that all the cleverness, all the techniques, all the "we need a new method to do something" — that doesn't matter very much. There are only a few things that matter. I think I listed seven of them.  
它说的是,所有的巧思、所有的技术、所有「我们需要新方法来做某事」——这些都不太重要。真正重要的东西只有几样。我记得列出了七个。  
**[03:08] Speaker B:** One is how much raw compute you have. The second is the quantity of data. The third is the quality and distribution of data. It needs to be a broad distribution.  
第一是你有多少原始算力。第二是数据的数量。第三是数据的质量和分布。分布需要足够广泛。  
**[03:20] Speaker B:** The fourth is how long you train for. The fifth is that you need an objective function that can scale to the moon.  
第四是训练时长。第五是你需要一个可以无限扩展的目标函数。  
**[03:27] Speaker B:** The pre-training objective function is one such objective function. Another is the RL objective function that says you have a goal, you're going to go out and reach the goal. Within that, there's objective rewards like...  
预训练目标函数就是这样一个目标函数。另一个是RL目标函数,它告诉你有一个目标,你要去实现这个目标。在其中,有一些客观的奖励,比如……  
**[03:48] Speaker A:** You see in math and coding, and there's more subjective rewards like you see in RLHF or higher-order versions of that.  
在数学和编程中能看到这种情况,还有更主观的奖励,比如 RLHF 或其更高阶的版本中看到的那样。  
**[03:52] Speaker A:** Then the sixth and seventh were things around normalization or conditioning, just getting the numerical stability so that the big blob of compute flows in this laminar way instead of running into problems.  
然后第六和第七个要素是关于归一化或条件化的,就是确保数值稳定性,让这一大团计算以层流方式流动,而不是遇到问题。  
**[04:11] Speaker A:** That was the hypothesis, and it's a hypothesis I still hold.  
这就是当时的假设,也是我至今仍然持有的假设。  
**[04:15] Speaker A:** I don't think I've seen very much that is not in line with it.  
我认为还没有看到太多与之不符的情况。  
**[04:21] Speaker A:** The pre-training scaling laws were one example of what we see there. Those have continued going.  
预训练的规模法则就是我们在那里看到的一个例子。这些规律一直在持续。  
**[04:31] Speaker A:** Now it's been widely reported, we feel good about pre-training.  
现在已经被广泛报道,我们对预训练感到很有信心。  
**[04:35] Speaker A:** It's continuing to give us gains. What has changed is that now we're also seeing the same thing for RL.  
它持续给我们带来收益。变化的是,现在我们也在 RL 中看到了同样的情况。  
**[04:41] Speaker A:** We're seeing a pre-training phase and then an RL phase on top of that.  
我们看到先有一个预训练阶段,然后在此基础上有一个 RL 阶段。  
**[04:46] Speaker A:** With RL, it's actually just the same.  
对于 RL 来说,实际上完全一样。  
**[04:55] Speaker A:** Even other companies have published things in some of their releases that say, "We train the model on math contests — AIME or other things — and how well the model does is log-linear in how long we've trained it."  
甚至其他公司也在他们的一些发布中公开过这样的内容,说「我们在数学竞赛上训练模型——AIME 或其他比赛——模型表现的好坏与训练时间呈对数线性关系」。  
**[05:14] Speaker A:** We see that as well, and it's not just math contests.  
我们也看到了这一点,而且不仅仅是数学竞赛。  
**[05:17] Speaker A:** It's a wide variety of RL tasks. We're seeing the same scaling in RL that we saw for pre-training.  
这是各种各样的 RL 任务。我们在 RL 中看到了与预训练相同的规模效应。  
**[05:27] Speaker B:** You mentioned Rich Sutton and "The Bitter Lesson". I interviewed him last year, and he's actually very non-LLM-pilled.  
你提到了 Rich Sutton 和「痛苦的教训」。我去年采访过他,他实际上非常不看好 LLM。  
**[05:31] Speaker B:** I don't know if this is his perspective, but one way to paraphrase his objection is: Something which possesses the true core of human learning would not require all these billions of dollars of data and compute and these bespoke environments, to learn how to use Excel, how to use PowerPoint, how to navigate a web browser.  
我不知道这是否是他的观点,但可以这样转述他的反对意见:如果真正掌握了人类学习的核心,就不需要投入数十亿美元的数据和算力,以及这些定制化的环境,来学习如何使用 Excel、如何使用 PowerPoint、如何浏览网页。  
**[05:57] Speaker B:** The fact that we have to build in these skills using these RL environments hints that we are actually lacking a core human learning algorithm.  
我们必须使用这些 RL 环境来构建这些技能的事实,暗示我们实际上缺乏核心的人类学习算法。  
**[06:04] Speaker B:** So we're scaling the wrong thing. That does raise the question. Why are we doing all this RL scaling if we think there's something that's going to be human-like in its ability to learn on the fly?  
所以我们在扩展错误的东西。这确实引出了一个问题:如果我们认为会有某种东西能像人类一样即时学习,那我们为什么还要做这么多 RL 扩展呢?  
**[06:16] Speaker A:** I think this puts together several things that should be thought of differently.  
我认为这把几个应该分开思考的东西混在了一起。  
**[06:23] Speaker A:** There is a genuine puzzle here, but it may not matter.  
这里确实有一个真正的谜题,但它可能并不重要。  
**[06:29] Speaker A:** In fact, I would guess it probably doesn't matter. There is an interesting thing. Let me take the RL out of it for a second, because I actually think it's a red herring to say that RL is any different from pre-training in this matter.  
事实上,我猜它可能并不重要。这里有个有趣的点。让我先把 RL 从中拿出来,因为我实际上认为说 RL 在这个问题上与预训练有什么不同是个误导。  
**[06:43] Speaker A:** If we look at pre-training scaling, it was very interesting back in 2017 when Alec Radford was doing GPT-1.  
如果我们看预训练的规模扩展,2017 年 Alec Radford 做 GPT-1 的时候就很有意思。  
**[06:47] Speaker A:** The models before GPT-1 were trained on datasets that didn't represent a wide distribution of text.  
GPT-1 之前的模型是在不能代表广泛文本分布的数据集上训练的。  
**[06:59] Speaker A:** You had very standard language modeling benchmarks. GPT-1 itself was trained on a bunch of fanfiction, I think actually.  
当时有非常标准的语言建模基准。GPT-1 本身是在一堆同人小说上训练的,我记得确实是这样。  
**[07:11] Speaker A:** It was literary text, which is a very small fraction of the text you can get.  
那是文学文本,只占你能获得的文本的很小一部分。  
**[07:17] Speaker A:** In those days it was like a billion words or something, so small datasets representing a pretty narrow distribution of what you can see in the world.  
那时候大概是十亿个词之类的,这么小的数据集代表的是你在世界上能看到的相当狭窄的分布。  
**[07:32] Speaker A:** It didn't generalize well. If you did better on some fanfiction corpus, it wouldn't generalize that well to other tasks.  
它的泛化能力不好。如果你在某个同人小说语料库上表现更好,它也不会很好地泛化到其他任务上。  
**[07:43] Speaker A:** We had all these measures. We had all these measures of how well it did at predicting all these other kinds of texts.  
我们有各种各样的指标。我们有各种指标来衡量它在预测其他各种文本时的表现。  
**[07:55] Speaker A:** It was only when you trained over all the tasks on the internet — when you did a general internet  
只有当你在互联网上的所有任务上进行训练时——当你做了一个通用的互联网  
**[08:01] Speaker A:** Scrape from something like Common Crawl or scraping links in Reddit, which is what we did for GPT-2 — that you started to get generalization.  
从 Common Crawl 这类来源抓取数据,或者像我们为 GPT-2 做的那样抓取 Reddit 链接——这样你才开始获得泛化能力。  
**[08:06] Speaker A:** I think we're seeing the same thing on RL.  
我认为我们在强化学习上也看到了同样的现象。  
**[08:15] Speaker A:** We're starting first with simple RL tasks like training on math competitions, then moving to broader training that involves things like code.  
我们首先从简单的强化学习任务开始,比如在数学竞赛上训练,然后转向更广泛的训练,涉及代码之类的内容。  
**[08:24] Speaker A:** Now we're moving to many other tasks.  
现在我们正在扩展到许多其他任务。  
**[08:31] Speaker A:** I think then we're going to increasingly get generalization.  
我认为这样我们就会越来越多地获得泛化能力。  
**[08:35] Speaker A:** So that kind of takes out the RL versus pre-training side of it.  
所以这在某种程度上解决了强化学习与预训练的对比问题。  
**[08:39] Speaker A:** But there is a puzzle either way, which is that in pre-training we use trillions of tokens.  
但无论如何都存在一个困惑,那就是在预训练中我们使用了数万亿个 token。  
**[08:50] Speaker A:** Humans don't see trillions of words. So there is an actual sample efficiency difference here.  
人类不会看到数万亿个词。所以这里确实存在样本效率的差异。  
**[08:54] Speaker A:** There is actually something different here.  
这里确实有些不同之处。  
**[08:59] Speaker A:** The models start from scratch and they need much more training.  
模型从零开始,它们需要更多的训练。  
**[09:06] Speaker A:** But we also see that once they're trained, if we give them a long context length of a million — the only thing blocking long context is inference — they're very good at learning and adapting within that context.  
但我们也看到,一旦它们训练完成,如果我们给它们一百万的长上下文长度——阻碍长上下文的唯一因素是推理——它们非常擅长在该上下文中学习和适应。  
**[09:17] Speaker A:** So I don't know the full answer to this.  
所以我不知道这个问题的完整答案。  
**[09:24] Speaker A:** I think there's something going on where pre-training is not like the process of humans learning, but it's somewhere between the process of humans learning and the process of human evolution.  
我认为有些现象正在发生,预训练不像人类学习的过程,而是介于人类学习过程和人类进化过程之间。  
**[09:32] Speaker A:** We get many of our priors from evolution.  
我们的许多先验知识来自进化。  
**[09:38] Speaker A:** Our brain isn't just a blank slate. Whole books have been written about this.  
我们的大脑不仅仅是一块白板。关于这个已经有整本书写过了。  
**[09:43] Speaker A:** The language models are much more like blank slates.  
语言模型更像是白板。  
**[09:45] Speaker A:** They literally start as random weights, whereas the human brain starts with all these regions connected to all these inputs and outputs.  
它们字面上是从随机权重开始的,而人类大脑一开始就有所有这些区域连接到所有这些输入和输出。  
**[09:50] Speaker A:** Maybe we should think of pre-training — and for that matter, RL as well — as something that exists in the middle space between human evolution and human on-the-spot learning.  
也许我们应该把预训练——以及强化学习——看作是存在于人类进化和人类即时学习之间的中间空间的某种东西。  
**[10:02] Speaker A:** And we should think of the in-context learning that the models do as something between long-term human learning and short-term human learning.  
而我们应该把模型所做的上下文学习看作是介于人类长期学习和短期学习之间的东西。  
**[10:10] Speaker A:** So there's this hierarchy. There's evolution, there's long-term learning, there's short-term learning, and there's just human reaction.  
所以存在这样一个层次结构。有进化,有长期学习,有短期学习,还有人类的即时反应。  
**[10:17] Speaker A:** The LLM phases exist along this spectrum, but not necessarily at exactly the same points.  
大语言模型的各个阶段存在于这个谱系之中,但不一定恰好在相同的点上。  
**[10:22] Speaker A:** There's no analog to some of the human modes of learning. The LLMs are falling in between the points. Does that make sense?  
有些人类的学习模式没有对应物。大语言模型落在这些点之间。这样说有意义吗?  
**[10:28] Speaker B:** Yes, although some things are still a bit confusing.  
是的,尽管有些事情仍然有点令人困惑。  
**[10:40] Speaker B:** For example, if the analogy is that this is like evolution so it's fine that it's not sample efficient, then if we're going to get super sample-efficient agents from in-context learning, why are we bothering to build all these RL environments?  
例如,如果这个类比是说这就像进化,所以样本效率低也没关系,那么如果我们要从上下文学习中获得超高样本效率的智能体,我们为什么还要费力构建所有这些强化学习环境呢?  
**[10:51] Speaker B:** There are companies whose work seems to be teaching models how to use this API, how to use Slack, how to use whatever. It's confusing to me why there's so much emphasis on that if the kind of agent that can just learn on the fly is emerging or has already emerged.  
有些公司的工作似乎是教模型如何使用这个 API,如何使用 Slack,如何使用各种东西。如果那种可以即时学习的智能体正在出现或已经出现,我就很困惑为什么还有这么多重点放在这上面。  
**[11:00] Speaker A:** I can't speak for the emphasis of anyone else. I can only talk about how we think about it.  
我不能代表其他人的重点。我只能说说我们是怎么想的。  
**[11:11] Speaker A:** The goal is not to teach the model every possible skill within RL, just as we don't do that within pre-training.  
目标不是在强化学习中教会模型所有可能的技能,就像我们在预训练中也不这样做一样。  
**[11:20] Speaker A:** Within pre-training, we're not trying to expose the model to every possible way that words could be put together.  
在预训练中,我们不是试图让模型接触到词语组合的所有可能方式。  
**[11:25] Speaker A:** Rather, the model trains on a lot of things and then reaches generalization across pre-training.  
相反,模型在很多东西上训练,然后在预训练中达到泛化。  
**[11:29] Speaker A:** That was the transition from GPT-1 to GPT-2 that I saw up close. The model reaches a point.  
这就是我近距离看到的从 GPT-1 到 GPT-2 的转变。模型达到一个临界点。  
**[11:53] Speaker A:** These moments where I was like, "Oh yeah, you just give the model a list of numbers — this is the cost of the house, this is the square feet of the house — and the model completes the pattern and does linear regression." Not great, but it does it, and it's never seen that exact thing before.  
有那么几次我就觉得,「哦对了,你只需要给模型一串数字——这个是房价,这个是房子的面积——然后模型就能识别出模式,做线性回归。」虽然效果不算特别好,但它确实做到了,而且它之前从未见过这个具体的任务。  
**[12:08] Speaker A:** So to the extent that we are building these RL environments, the goal is very similar to what was done five or ten years ago with pre-training. We're trying to get a whole bunch of data, not because we want to cover a specific document or a specific skill, but because we want to generalize.  
所以我们现在构建这些强化学习环境,目标其实和五年或十年前做预训练时很相似。我们试图获取大量数据,不是为了覆盖某个特定文档或某项特定技能,而是为了让模型能够泛化。  
**[12:32] Speaker B:** I think the framework you're laying down obviously makes sense. We're making progress toward AGI. Nobody at this point disagrees we're going to achieve AGI this century. The crux is you say we're hitting the end of the exponential. Somebody else looks at this and says, "We've been making progress since 2012, and by 2035 we'll have a human-like agent."  
我觉得你提出的这个框架显然是合理的。我们确实在朝着AGI的方向前进。现在没人会否认我们会在本世纪实现AGI。关键分歧在于,你说我们正在触及指数增长的尽头。而另一些人看到这些进展会说,「我们从2012年就开始取得进展了,到2035年我们就会有类人的智能体。」  
**[13:04] Speaker B:** Obviously we're seeing in these models the kinds of things that evolution did, or that learning within a human lifetime does. I want to understand what you're seeing that makes you think it's one year away and not ten years away.  
显然我们在这些模型中看到了进化所做的事情,或者说人类在一生中学习所做的事情。我想理解的是,你看到了什么,让你认为这件事一年之后就会发生,而不是十年之后。  
**[13:17] Speaker A:** There are two claims you could make here, one stronger and one weaker. Starting with the weaker claim, when I first saw the scaling back in 2019, I wasn't sure. This was a 50/50 thing. I thought I saw something. My claim was that this was much more likely than anyone thinks. Maybe there's a 50% chance this happens.  
这里可以提出两种主张,一个更强,一个较弱。先说较弱的主张,2019年我第一次看到scaling规律时,我并不确定。当时对我来说是五五开。我觉得我看到了一些迹象。我当时的判断是,这件事发生的可能性比任何人想的都要高得多。也许有50%的概率会发生。  
**[13:51] Speaker A:** On the basic hypothesis of, as you put it, within ten years we'll get to what I call a "country of geniuses in a data center", I'm at 90% on that. It's hard to go much higher than 90% because the world is so unpredictable. Maybe the irreducible uncertainty puts us at 95%, where you get to things like multiple companies having internal turmoil, Taiwan gets invaded, all the fabs get blown up by missiles.  
对于你提到的基本假设,也就是十年内我们会实现我所说的「数据中心里的天才之国」,我有90%的把握。很难超过90%,因为这个世界太难预测了。也许不可消除的不确定性让我们最多到95%,剩下5%是那些像多家公司内部出现动荡、台湾遭到入侵、所有芯片厂被导弹炸毁这样的情况。  
**[14:24] Speaker B:** Now you've jinxed us, Dario.  
你这是乌鸦嘴了,Dario。  
**[14:30] Speaker A:** You could construct a 5% world where things get delayed for ten years. There's another 5% which is that I'm very confident on tasks that can be verified. With coding, except for that irreducible uncertainty, I think we'll be there in one or two years. There's no way we will not be there in ten years in terms of being able to do end-to-end coding.  
你可以构想一个5%概率的世界,在那里事情会被推迟十年。另外5%的不确定性在于,我对可验证任务非常有信心。对于编程来说,除了那些不可消除的不确定性,我认为一两年内我们就能做到。在端到端编程能力方面,十年内不可能做不到。  
**[14:58] Speaker A:** My one little bit of fundamental uncertainty, even on long timescales, is about tasks that aren't verifiable: planning a mission to Mars; doing some fundamental scientific discovery like CRISPR; writing a novel. It's hard to verify those tasks. I am almost certain we have a reliable path to get there, but if there's a little bit of uncertainty it's there.  
我唯一的一点根本性不确定,即使放在长时间尺度上,是关于那些不可验证的任务:规划火星任务;做像CRISPR那样的基础科学发现;写小说。这些任务很难验证。我几乎确信我们有可靠的路径达到那里,但如果有一点点不确定性的话,就在这里。  
**[15:34] Speaker A:** On the ten-year timeline I'm at 90%, which is about as certain as you can be. I think it's crazy to say that this won't happen by 2035. In some sane world, it would be outside the mainstream.  
对于十年时间线,我有90%的把握,这基本上是你能达到的最高确定性了。我认为说2035年之前不会发生是很疯狂的。在一个理性的世界里,这种观点应该是非主流的。  
**[15:48] Speaker B:** But the emphasis on verification hints to me a lack of belief that these models are generalized. If you think about humans, we're both good at things for which we get verifiable reward and things for which we don't.  
但你对可验证性的强调,让我觉得你似乎不太相信这些模型已经实现了泛化。想想人类,我们既擅长那些能得到可验证反馈的事情,也擅长那些得不到可验证反馈的事情。  
**[16:03] Speaker A:** No, this is why I'm almost sure. We already see substantial generalization from things that verify to things that don't. We're already seeing that. But it seems like you were emphasizing—  
不,这正是我几乎确信的原因。我们已经看到了从可验证任务到不可验证任务的大量泛化。我们已经在看到这种现象了。但你好像在强调——  
**[16:15] Speaker A:** This as a spectrum which will split apart which domains in which we see more progress.  
这是一个光谱,它会分裂开来,显示我们在哪些领域看到更多进展。  
**[16:21] Speaker B:** That doesn't seem like how humans get better. The world in which we don't get there is the world in which we do all the verifiable things. Many of them generalize, but we don't fully get there.  
这似乎不像人类变得更好的方式。我们无法达到那里的世界,是我们只做所有可验证事情的世界。其中许多会泛化,但我们并没有完全到达那里。  
**[16:34] Speaker B:** We don't fully color in the other side of the box. It's not a binary thing.  
我们没有完全填满盒子的另一边。这不是一个非此即彼的事情。  
**[16:40] Speaker B:** Even if generalization is weak and you can only do verifiable domains, it's not clear to me you could automate software engineering in such a world.  
即使泛化能力很弱,你只能做可验证的领域,我也不清楚在这样的世界里你能否自动化软件工程。  
**[16:49] Speaker A:** You are a software engineer in some sense, but part of being a software engineer for you involves writing long memos about your grand vision.  
从某种意义上说你是一名软件工程师,但对你来说,作为软件工程师的一部分涉及撰写关于你宏大愿景的长篇备忘录。  
**[16:58] Speaker B:** I don't think that's part of the job of SWE. That's part of the job of the company, not SWE specifically.  
我不认为那是 SWE 工作的一部分。那是公司工作的一部分,而不是 SWE 特有的。  
**[17:04] Speaker A:** But SWE does involve design documents and other things like that.  
但 SWE 确实涉及设计文档和其他类似的事情。  
**[17:10] Speaker B:** The models are already pretty good at writing comments.  
模型在编写注释方面已经相当不错了。  
**[17:14] Speaker B:** Again, I'm making much weaker claims here than I believe, to distinguish between two things.  
再次强调,我在这里提出的主张比我实际相信的要弱得多,是为了区分两件事。  
**[17:24] Speaker B:** We're already almost there for software engineering.  
我们在软件工程方面已经快到那里了。  
**[17:28] Speaker A:** By what metric? There's one metric which is how many lines of code are written by AI.  
按什么标准?有一个标准是 AI 写了多少行代码。  
**[17:32] Speaker A:** If you consider other productivity improvements in the history of software engineering, compilers write all the lines of software.  
如果你考虑软件工程历史上的其他生产力改进,编译器会写所有的软件代码行。  
**[17:36] Speaker A:** There's a difference between how many lines are written and how big the productivity improvement is. "We're almost there" meaning…  
写了多少行代码和生产力提升有多大之间是有区别的。「我们快到那里了」的意思是……  
**[17:47] Speaker A:** How big is the productivity improvement, not just how many lines are written by AI?  
生产力提升有多大,而不仅仅是 AI 写了多少行?  
**[17:52] Speaker B:** I actually agree with you on this. I've made a series of predictions on code and software engineering.  
在这一点上我实际上同意你的看法。我对代码和软件工程做了一系列预测。  
**[17:57] Speaker B:** I think people have repeatedly misunderstood them.  
我认为人们一再误解了它们。  
**[18:03] Speaker B:** Let me lay out the spectrum. About eight or nine months ago, I said the AI model will be writing 90% of the lines of code in three to six months.  
让我阐述一下这个光谱。大约八九个月前,我说 AI 模型将在三到六个月内编写 90% 的代码行。  
**[18:16] Speaker B:** That happened, at least at some places. It happened at Anthropic, happened with many people downstream using our models.  
这已经发生了,至少在一些地方。它在 Anthropic 发生了,在许多使用我们模型的下游用户那里也发生了。  
**[18:21] Speaker B:** But that's actually a very weak criterion.  
但这实际上是一个非常弱的标准。  
**[18:27] Speaker B:** People thought I was saying that we won't need 90% of the software engineers. Those things are worlds apart.  
人们以为我是在说我们不需要 90% 的软件工程师。这些事情相差十万八千里。  
**[18:32] Speaker B:** The spectrum is: 90% of code is written by the model, 100% of code is written by the model. That's a big difference in productivity.  
光谱是:90% 的代码由模型编写,100% 的代码由模型编写。这在生产力上是一个巨大的差异。  
**[18:41] Speaker B:** 90% of the end-to-end SWE tasks — including things like compiling, setting up clusters and environments, testing features, writing memos — are done by the models.  
90% 的端到端 SWE 任务——包括编译、设置集群和环境、测试功能、编写备忘录等——都由模型完成。  
**[18:54] Speaker B:** 100% of today's SWE tasks are done by the models.  
今天 100% 的 SWE 任务都由模型完成。  
**[19:02] Speaker B:** Even when that happens, it doesn't mean software engineers are out of a job.  
即使发生这种情况,也不意味着软件工程师会失业。  
**[19:06] Speaker B:** There are new higher-level things they can do, where they can manage.  
他们可以做新的更高层次的事情,他们可以管理。  
**[19:10] Speaker B:** Then further down the spectrum, there's 90% less demand for SWEs, which I think will happen but this is a spectrum.  
然后在光谱的更远端,对 SWE 的需求减少 90%,我认为这会发生,但这是一个光谱。  
**[19:15] Speaker B:** I wrote about it in "The Adolescence of Technology" where I went through this kind of spectrum with farming.  
我在《技术的青春期》中写过这个问题,我在那里用农业经历了这种光谱。  
**[19:26] Speaker A:** I actually totally agree with you on that.  
在这一点上我完全同意你的看法。  
**[19:29] Speaker A:** These are very different benchmarks from each other, but we're proceeding through them super fast.  
这些是彼此非常不同的基准,但我们正在超快地完成它们。  
**[19:32] Speaker A:** Part of your vision is that going from 90 to 100 is going to happen fast, and that it leads to huge productivity improvements.  
你愿景的一部分是,从 90 到 100 的过程会很快发生,并且会带来巨大的生产力提升。  
**[19:45] Speaker A:** But what I notice is that even in greenfield projects people start with Claude Code or something, people report starting a lot of projects… Do we see in the world out there  
但我注意到的是,即使在全新项目中,人们也会从 Claude Code 或类似工具开始,人们报告说启动了很多项目……我们在外面的世界中是否看到  
**[19:54] Speaker A:** A renaissance of software, all these new features that wouldn't exist otherwise? At least so far, it doesn't seem like we see that. So that does make me wonder.  
软件的复兴,所有这些原本不会存在的新功能?至少到目前为止,我们似乎并没有看到这种情况。所以这确实让我困惑。  
**[20:02] Speaker A:** Even if I never had to intervene with Claude Code, the world is complicated. Jobs are complicated.  
即使我从不需要干预 Claude Code,现实世界也是复杂的。工作也是复杂的。  
**[20:09] Speaker A:** Closing the loop on self-contained systems, whether it's just writing software or something, how much broader gains would we see just from that?  
在自包含系统上闭环,无论是写软件还是其他什么,仅仅从这一点我们能看到多大的收益?  
**[20:20] Speaker A:** Maybe that should dilute our estimation of the "country of geniuses".  
也许这应该削弱我们对「天才之国」的估计。  
**[20:24] Speaker B:** I simultaneously agree with you that it's a reason why these things don't happen instantly, but at the same time, I think the effect is gonna be very fast.  
我一方面同意你的观点,这确实是为什么这些事情不会立即发生的原因,但同时,我认为这个效应会非常快。  
**[20:41] Speaker B:** You could have these two poles. One is that AI is not going to make progress. It's slow. It's going to take forever to diffuse within the economy.  
你可以看到两个极端。一个是 AI 不会取得进展。它很慢。要在经济中扩散需要永远的时间。  
**[20:52] Speaker B:** Economic diffusion has become one of these buzzwords that's a reason why we're not going to make AI progress, or why AI progress doesn't matter.  
经济扩散已经成为这些流行词之一,用来解释为什么我们不会在 AI 上取得进展,或者为什么 AI 进展无关紧要。  
**[21:00] Speaker B:** The other axis is that we'll get recursive self-improvement, the whole thing. Can't you just draw an exponential line on the curve?  
另一个极端是我们会实现递归自我改进,整个过程。你不就是在曲线上画一条指数线吗?  
**[21:08] Speaker B:** We're going to have Dyson spheres around the sun so many nanoseconds after we get recursive. I'm completely caricaturing the view here, but there are these two extremes.  
我们会在实现递归后的几纳秒内就在太阳周围建造戴森球。我在这里完全是在讽刺这种观点,但确实存在这两个极端。  
**[21:23] Speaker B:** But what we've seen from the beginning, at least if you look within Anthropic, there's this bizarre 10x per year growth in revenue that we've seen. So in 2023, it was zero to $100 million.  
但从一开始我们所看到的,至少如果你看 Anthropic 内部,我们看到了这种惊人的每年十倍收入增长。所以在2023年,是从零到一亿美元。  
**[21:38] Speaker B:** In 2024, it was $100 million to $1 billion. In 2025, it was $1 billion to $9-10 billion.  
2024年是从一亿美元到十亿美元。2025年是从十亿美元到九十亿到一百亿美元。  
**[21:46] Speaker A:** You guys should have just bought a billion dollars of your own products so you could just…  
你们应该直接买十亿美元自己的产品,这样你们就可以……  
**[21:50] Speaker B:** And the first month of this year, that exponential is... You would think it would slow down, but we added another few billion to revenue in January.  
而今年的第一个月,这个指数级增长……你会以为它会放缓,但我们在一月份又增加了几十亿美元的收入。  
**[22:05] Speaker B:** Obviously that curve can't go on forever. The GDP is only so large.  
显然这条曲线不可能永远持续下去。GDP 总共就那么大。  
**[22:10] Speaker B:** I would even guess that it bends somewhat this year, but that is a fast curve. That's a really fast curve. I would bet it stays pretty fast even as the scale goes to the entire economy.  
我甚至猜测今年会有所放缓,但这仍然是一条快速的曲线。这是一条非常快的曲线。我敢打赌即使规模扩展到整个经济体,它仍会保持相当快的速度。  
**[22:25] Speaker B:** So I think we should be thinking about this middle world where things are extremely fast, but not instant, where they take time because of economic diffusion, because of the need to close the loop.  
所以我认为我们应该考虑这个中间状态的世界,事物发展极快,但不是瞬间完成,它们需要时间,因为经济扩散,因为需要闭环。  
**[22:39] Speaker B:** Because it's fiddly: "I have to do change management within my enterprise... I set this up, but I have to change the security permissions on this in order to make it actually work... I had this old piece of software that checks the model before it's compiled and released and I have to rewrite it. Yes, the model can do that, but I have to tell the model to do that. It has to take time to do that."  
因为这很繁琐:「我必须在企业内部做变更管理……我设置好了这个,但我必须更改安全权限才能让它真正工作……我有这个旧软件在模型编译和发布前检查它,我必须重写它。是的,模型可以做到,但我必须告诉模型去做。这需要时间来完成。」  
**[23:10] Speaker B:** So I think everything we've seen so far is compatible with the idea that there's one fast exponential that's the capability of the model. Then there's another fast exponential that's downstream of that, which is the diffusion of the model into the economy.  
所以我认为到目前为止我们看到的一切都符合这样的想法:有一条快速的指数曲线,那就是模型的能力。然后还有另一条快速的指数曲线,是前者的下游,也就是模型向经济中的扩散。  
**[23:26] Speaker B:** Not instant, not slow, much faster than any previous technology, but it has its limits.  
不是瞬间的,也不慢,比以往任何技术都快得多,但它有其局限性。  
**[23:37] Speaker B:** When I look inside Anthropic, when I look at our customers: fast adoption, but not infinitely fast.  
当我审视 Anthropic 内部,当我看我们的客户时:采用速度很快,但不是无限快。  
**[23:44] Speaker A:** Can I try a hot take on you? Yeah.  
我能说个激进的观点吗?可以。  
**[23:45] Speaker A:** I feel like diffusion is cope that people say. When the model isn't able to do something, they're like, "oh, but it's a diffusion issue." But then you should use the comparison to humans.  
我觉得扩散是人们用来自我安慰的说法。当模型做不到某事时,他们就说「哦,但这是扩散问题」。但你应该用与人类的对比来看。  
**[23:56] Speaker A:** You would think that the inherent advantages that AIs have would make diffusion a much easier problem for new AIs getting onboarded than new humans getting onboarded.  
你可能会觉得,AI 本身具有的固有优势应该会让新 AI 的扩散和上手比新员工要容易得多。  
**[24:06] Speaker A:** An AI can read your entire Slack and your drive in minutes.  
AI 可以在几分钟内读完你所有的 Slack 消息和云端文件。  
**[24:08] Speaker A:** They can share all the knowledge that the other copies of the same instance have.  
它们可以共享同一实例的其他副本所拥有的全部知识。  
**[24:12] Speaker A:** You don't have this adverse selection problem when you're hiring AI, so you can just hire copies of a vetted AI model. Hiring a human is so much more of a hassle.  
雇佣 AI 时不存在逆向选择问题,你只需要雇佣经过审核的 AI 模型的副本就行了。而雇佣人类要麻烦得多。  
**[24:20] Speaker A:** People hire humans all the time. We pay humans upwards of $50 trillion in wages because they're useful, even though in principle it would be much easier to integrate AIs into the economy than it is to hire humans.  
但人们仍然一直在雇佣人类。我们每年支付给人类超过 50 万亿美元的工资,因为他们有用,尽管理论上把 AI 整合到经济中应该比雇佣人类容易得多。  
**[24:29] Speaker A:** The diffusion doesn't really explain.  
光用扩散速度并不能真正解释这个现象。  
**[24:34] Speaker A:** I think diffusion is very real and doesn't exclusively have to do with limitations on the AI models.  
我认为扩散问题是真实存在的,而且并不完全是因为 AI 模型本身的局限性。  
**[24:41] Speaker A:** Again, there are people who use diffusion as kind of a buzzword to say this isn't a big deal. I'm not talking about that.  
有些人把「扩散」当成流行词,用来说这不是什么大事。我说的不是这个意思。  
**[24:49] Speaker A:** I'm not talking about how AI will diffuse at the speed of previous technologies.  
我也不是说 AI 会以过去技术的扩散速度来普及。  
**[24:58] Speaker A:** I think AI will diffuse much faster than previous technologies have, but not infinitely fast.  
我认为 AI 的扩散会比以往的技术快得多,但不会快到瞬间完成。  
**[25:04] Speaker A:** I'll just give an example of this. There's Claude Code. Claude Code is extremely easy to set up.  
我举个例子。我们有 Claude Code 这个产品。Claude Code 设置起来非常简单。  
**[25:10] Speaker A:** If you're a developer, you can just start using Claude Code.  
如果你是开发者,你可以直接开始使用 Claude Code。  
**[25:14] Speaker A:** There is no reason why a developer at a large enterprise should not be adopting Claude Code as quickly as an individual developer or developer at a startup.  
理论上,大企业的开发者采用 Claude Code 的速度应该和独立开发者或创业公司的开发者一样快,没有理由更慢。  
**[25:25] Speaker A:** We do everything we can to promote it. We sell Claude Code to enterprises.  
我们尽一切努力推广它。我们向企业销售 Claude Code。  
**[25:31] Speaker A:** Big enterprises, big financial companies, big pharmaceutical companies, all of them are adopting Claude Code much faster than enterprises typically adopt new technology.  
大型企业、大型金融公司、大型制药公司,它们采用 Claude Code 的速度都比企业通常采用新技术的速度快得多。  
**[25:38] Speaker A:** But again, it takes time.  
但即便如此,这仍然需要时间。  
**[25:46] Speaker A:** Any given feature or any given product, like Claude Code or Cowork, will get adopted by the individual developers who are on Twitter all the time, by the Series A startups, many months faster than they will get adopted by a large enterprise that does food sales.  
任何特定功能或产品,比如 Claude Code 或 Cowork,会被那些天天刷 Twitter 的独立开发者、A 轮创业公司采用,而这比一家做食品销售的大型企业采用要快上好几个月。  
**[26:11] Speaker A:** There are just a number of factors. You have to go through legal, you have to provision it for everyone.  
这里面有很多因素。你得走法务流程,得为所有人配置权限。  
**[26:14] Speaker A:** It has to pass security and compliance.  
还得通过安全和合规审查。  
**[26:20] Speaker A:** The leaders of the company who are further away from the AI revolution are forward-looking, but they have to say, 'Oh, it makes sense for us to spend 50 million.'  
公司领导层离 AI 革命比较远,他们虽然有前瞻性,但也得想明白:「我们花 5000 万美元是有意义的。」  
**[26:31] Speaker A:** This is what this Claude Code thing is. This is why it helps our company.  
「这个 Claude Code 是做什么的,它为什么能帮到我们公司。」  
**[26:35] Speaker A:** This is why it makes us more productive. Then they have to explain to the people two levels below.  
「它为什么能提高我们的生产力。」然后他们还得向下两级的人解释清楚。  
**[26:37] Speaker A:** They have to say, 'Okay, we have 3,000 developers. Here's how we're going to roll it out to our developers.'  
他们得说:「好,我们有 3000 名开发者,我们要这样向他们推广。」  
**[26:45] Speaker A:** We have conversations like this every day. We are doing everything we can to make Anthropic's revenue grow 20 or 30x a year instead of 10x a year.  
我们每天都在进行这样的对话。我们尽一切努力让 Anthropic 的收入每年增长 20 倍或 30 倍,而不只是 10 倍。  
**[26:57] Speaker A:** Again, many enterprises are just saying, 'This is so productive. We're going to take shortcuts in our usual procurement process.'  
确实,很多企业都在说:「这太高效了,我们要在常规采购流程上走捷径。」  
**[27:05] Speaker A:** They're moving much faster than when we tried to sell them just the ordinary API, which many of them use.  
它们的推进速度比我们向他们销售普通 API 时快得多,虽然很多企业也在用我们的 API。  
**[27:08] Speaker A:** Claude Code is a more compelling product, but it's not an infinitely compelling product.  
Claude Code 是个更有吸引力的产品,但它也不是无限吸引人的产品。  
**[27:13] Speaker A:** I don't think even AGI or powerful AI or 'country of geniuses in a data center' will be an infinitely compelling product.  
我认为即使是 AGI、强大的 AI,或者说「数据中心里的天才国度」,也不会是一个能瞬间征服所有人的产品。  
**[27:22] Speaker A:** It will be a compelling product enough maybe to get 3-5x, or 10x, a year of growth, even when you're in the hundreds of billions of dollars, which is extremely hard to do and has never been done in history before, but not infinitely fast.  
这将是一个足够有吸引力的产品,也许能实现每年3到5倍、甚至10倍的增长,即使在规模已经达到数千亿美元的情况下——这极其困难,历史上从未有过先例——但不会是无限快速的增长。  
**[27:32] Speaker A:** I buy that it would be a slight slowdown.  
我认可会有轻微的减速。  
**[27:36] Speaker A:** Maybe this is not your claim, but sometimes people talk about this like, "Oh, the capabilities are there, but because of diffusion... otherwise we're basically at AGI".  
也许这不是你的观点,但有时人们会这样说:「哦,能力已经具备了,只是因为扩散速度的问题……否则我们基本上已经达到AGI了」。  
**[27:46] Speaker A:** I don't believe we're basically at AGI. I think if you had the "country of geniuses in a data center"... If we had the "country of geniuses in a data center", we would know it.  
我不认为我们基本上已经达到AGI。我认为如果你真的拥有「数据中心里的天才之国」……如果我们真有「数据中心里的天才之国」,我们会知道的。  
**[27:53] Speaker A:** We would know it if you had the "country of geniuses in a data center". Everyone in this room would know it.  
如果你真有「数据中心里的天才之国」,我们会知道的。这个房间里的每个人都会知道。  
**[28:01] Speaker A:** Everyone in Washington would know it. People in rural parts might not know it, but we would know it.  
Washington的每个人都会知道。偏远地区的人可能不知道,但我们会知道。  
**[28:07] Speaker A:** We don't have that now. That is very clear.  
我们现在没有那样的东西。这一点非常清楚。  
**[29:42] Speaker B:** Coming back to concrete prediction... Because there are so many different things to disambiguate, it can be easy to talk past each other when we're talking about capabilities.  
回到具体的预测……因为有太多不同的东西需要区分,当我们谈论能力时,很容易出现各说各话的情况。  
**[29:50] Speaker B:** For example, when I interviewed you three years ago, I asked you a prediction about what we should expect three years from now. You were right. You said, "We should expect systems which, if you talk to them for the course of an hour, it's hard to tell them apart from a generally well-educated human."  
例如,三年前我采访你时,我问你对三年后的预测。你说对了。你说:「我们应该期待这样的系统,如果你和它们交谈一个小时,很难将它们与受过良好教育的人类区分开来」。  
**[30:04] Speaker B:** I think you were right about that.  
我认为你在这一点上是对的。  
**[30:07] Speaker B:** I think spiritually I feel unsatisfied because my internal expectation was that such a system could automate large parts of white-collar work.  
但我在精神上感到不满足,因为我内心的期待是这样的系统能够自动化大部分白领工作。  
**[30:13] Speaker B:** So it might be more productive to talk about the actual end capabilities you want from such a system.  
所以,讨论你希望这样的系统具备的实际最终能力可能更有成效。  
**[30:21] Speaker A:** I will basically tell you where I think we are. Let me ask a very specific question so that we can figure out exactly what kinds of capabilities we should think about soon.  
我基本上会告诉你我认为我们处于什么位置。让我问一个非常具体的问题,这样我们就能弄清楚应该很快考虑哪些能力。  
**[30:32] Speaker B:** Maybe I'll ask about it in the context of a job I understand well, not because it's the most relevant job, but just because I can evaluate the claims about it. Take video editors. I have video editors.  
也许我会在一个我很了解的工作背景下来问这个问题,不是因为这是最相关的工作,而是因为我能够评估关于它的说法。就拿视频编辑来说吧,我有视频编辑。  
**[30:42] Speaker B:** Part of their job involves learning about our audience's preferences, learning about my preferences and tastes, and the different trade-offs we have.  
他们工作的一部分涉及了解我们受众的偏好,了解我的偏好和品味,以及我们面临的各种权衡。  
**[30:50] Speaker B:** They're, over the course of many months, building up this understanding of context.  
在几个月的时间里,他们逐渐建立起对这些背景的理解。  
**[30:55] Speaker B:** The skill and ability they have six months into the job, a model that can pick up that skill on the job on the fly, when should we expect such an AI system?  
他们在工作六个月后具备的技能和能力,一个能够在工作中即时学会这种技能的模型,我们什么时候应该期待这样的AI系统?  
**[31:04] Speaker A:** I guess what you're talking about is that we're doing this interview for three hours.  
我想你说的是我们正在进行这个三小时的访谈。  
**[31:09] Speaker A:** Someone's going to come in, someone's going to edit it.  
会有人进来,会有人编辑它。  
**[31:11] Speaker A:** They're going to be like, "Oh, I don't know, Dario scratched his head and we could edit that out."  
他们会说:「哦,我不知道,Dario挠了挠头,我们可以把那个剪掉」。  
**[31:19] Speaker A:** "Magnify that." "There was this long discussion that is less interesting to people. There's another thing that's more interesting to people, so let's make this edit."  
「放大那个」。「这段冗长的讨论对观众来说不太有意思。还有另一个东西对观众更有意思,所以我们来做这个剪辑」。  
**[31:27] Speaker A:** I think the "country of geniuses in a data center" will be able to do that.  
我认为「数据中心里的天才之国」将能够做到这一点。  
**[31:33] Speaker A:** The way it will be able to do that is it will have general control of a computer screen.  
它能够做到这一点的方式是,它将拥有对计算机屏幕的通用控制能力。  
**[31:38] Speaker A:** You'll be able to feed this in.  
你将能够把这些内容输入进去。  
**[31:43] Speaker A:** It'll be able to also use the computer screen to go on the web, look at all your previous interviews, look at what people are saying on Twitter in response to your interviews, talk to you, ask you questions, talk to your staff, look at the history of edits.  
它还能够使用计算机屏幕上网,查看你所有以前的访谈,查看人们在Twitter上对你访谈的评论,与你交谈,向你提问,与你的员工交谈,查看编辑历史。  
**[31:59] Speaker A:** That you did, and from that, do the job. I think that's dependent on several things.  
你之前做的事情,然后基于此来完成工作。我认为这取决于几个因素。  
**[32:06] Speaker A:** I think this is one of the things that's actually blocking deployment: getting to the point on computer use where the models are really masters at using the computer.  
我认为这是实际阻碍部署的问题之一:要让模型在计算机使用方面达到真正精通的程度。  
**[32:16] Speaker A:** We've seen this climb in benchmarks, and benchmarks are always imperfect measures.  
我们看到基准测试分数在不断攀升,当然基准测试总是不完美的衡量标准。  
**[32:20] Speaker A:** But I think when we first released computer use a year and a quarter ago, OSWorld was at maybe 15%. I don't remember exactly, but we've climbed from that to 65-70%.  
但我记得一年零三个月前我们刚发布 computer use 功能时,OSWorld 的分数大概是15%。我记不太清了,但我们已经从那时攀升到了65-70%。  
**[32:40] Speaker A:** There may be harder measures as well, but I think computer use has to pass a point of reliability.  
可能还有更难的衡量标准,但我认为 computer use 必须达到一定的可靠性水平。  
**[32:46] Speaker B:** Can I just follow up on that before you move on to the next point?  
在你继续下一个点之前,我能先追问一下这个问题吗?  
**[32:50] Speaker B:** For years, I've been trying to build different internal LLM tools for myself. Often I have these text-in, text-out tasks, which should be dead center in the repertoire of these models. Yet I still hire humans to do them.  
多年来,我一直在尝试为自己构建各种内部 LLM 工具。我经常有一些文本输入、文本输出的任务,这些本应是这些模型的核心能力范围。但我还是雇人来做这些事。  
**[33:03] Speaker B:** If it's something like "identify what the best clips would be in this transcript", maybe the LLMs do a seven-out-of-ten job on them. But there's not this ongoing way I can engage with them to help them get better at the job the way I could with a human employee.  
比如「找出这份文字稿中最好的片段」这样的任务,LLM 可能能做到七分的水平。但我没法像对待人类员工那样持续与它们互动,帮助它们在工作中不断进步。  
**[33:16] Speaker B:** That missing ability, even if you solve computer use, would still block my ability to offload an actual job to them.  
这种缺失的能力,即使你解决了 computer use 的问题,仍然会阻碍我把真正的工作交给它们。  
**[33:20] Speaker A:** This gets back to what we were talking about before with learning on the job. It's very interesting. I think with the coding agents, I don't think people would say that learning on the job is what is preventing the coding agents from doing everything end to end.  
这又回到我们之前讨论的在工作中学习的问题。这很有意思。我认为对于编程 agent 来说,人们不会说在工作中学习的能力是阻碍编程 agent 端到端完成所有任务的原因。  
**[33:39] Speaker A:** They keep getting better. We have engineers at Anthropic who don't write any code.  
它们一直在变得更好。我们 Anthropic 有些工程师已经不写任何代码了。  
**[33:46] Speaker A:** When I look at the productivity, to your previous question, we have folks who say, "This GPU kernel, this chip, I used to write it myself. I just have Claude do it." There's this enormous improvement in productivity.  
回到你之前的生产力问题,我们有人说「这个 GPU kernel,这个芯片,我以前都是自己写的。现在我就让 Claude 来做。」生产力有了巨大的提升。  
**[34:04] Speaker A:** When I see Claude Code, familiarity with the codebase or a feeling that the model hasn't worked at the company for a year, that's not high up on the list of complaints I see.  
当我观察 Claude Code 时,对代码库的熟悉程度,或者那种模型好像没在公司工作过一年的感觉,这些并不是我看到的主要抱怨。  
**[34:18] Speaker A:** I think what I'm saying is that we're kind of taking a different path.  
我想说的是,我们正在走一条不同的路径。  
**[34:22] Speaker B:** Don't you think with coding that's because there is an external scaffold of memory which exists instantiated in the codebase? I don't know how many other jobs have that.  
你不觉得编程之所以如此,是因为存在一个外部的记忆支架,它实例化在代码库中?我不知道还有多少其他工作具备这种条件。  
**[34:28] Speaker B:** Coding made fast progress precisely because it has this unique advantage that other economic activity doesn't.  
编程之所以进展迅速,正是因为它具有其他经济活动所不具备的这种独特优势。  
**[34:37] Speaker A:** But when you say that, what you're implying is that by reading the codebase into the context, I have everything that the human needed to learn on the job.  
但你这么说的时候,你暗示的是,通过把代码库读入上下文,我就拥有了人类在工作中需要学习的所有东西。  
**[34:48] Speaker A:** So that would be an example of—whether it's written or not, whether it's available or not—a case where everything you needed to know you got from the context window.  
所以这就是一个例子——无论它是否被写下来,是否可获取——你需要知道的一切都能从上下文窗口中获得。  
**[35:00] Speaker A:** What we think of as learning—"I started this job, it's going to take me six months to understand the code base"—the model just did it in the context.  
我们所认为的学习——「我刚开始这份工作,理解代码库需要六个月」——模型只是在上下文中就完成了。  
**[35:05] Speaker A:** I honestly don't know how to think about this because there are people who qualitatively report what you're saying.  
说实话我不知道该如何看待这个问题,因为确实有人在定性地报告你说的这种情况。  
**[35:16] Speaker A:** I'm sure you saw last year, there was a major study where they had experienced developers try to close pull requests in repositories that they were familiar with. Those developers reported an uplift. They reported that they felt more productive with the use of these models.  
我相信你去年看到过,有一项重要研究,让有经验的开发者尝试在他们熟悉的代码仓库中关闭 pull request。那些开发者报告说有提升。他们说使用这些模型后感觉更高效了。  
**[35:31] Speaker A:** But in fact, if you look at their output and how much was actually merged back in,  
但事实上,如果你看他们的产出以及实际有多少被合并回去,  
**[35:35] Speaker A:** There was a 20% downlift. They were less productive as a result of using these models.  
生产力下降了20%。使用这些模型反而降低了他们的生产效率。  
**[35:37] Speaker A:** So I'm trying to square the qualitative feeling that people feel with these models versus, one, on a macro level, where is this renaissance of software?  
所以我在试图理解人们对这些模型的主观感受，与以下问题之间的矛盾：第一，从宏观层面看，软件行业的复兴在哪里？  
**[35:44] Speaker A:** And then two, when people do these independent evaluations, why are we not seeing the productivity benefits we would expect?  
第二，当人们进行这些独立评估时，为什么我们没有看到预期的生产力提升？  
**[35:53] Speaker B:** Within Anthropic, this is just really unambiguous. We're under an incredible amount of commercial pressure and make it even harder for ourselves because we have all this safety stuff we do that I think we do more than other companies.  
在 Anthropic 内部，这一点非常明确。我们面临着巨大的商业压力，而且因为我们做了大量安全方面的工作——我认为比其他公司做得更多——这让我们的处境更加艰难。  
**[36:03] Speaker B:** The pressure to survive economically while also keeping our values is just incredible. We're trying to keep this 10x revenue curve going.  
在保持我们价值观的同时实现经济生存的压力是巨大的。我们正在努力维持这条10倍收入增长曲线。  
**[36:18] Speaker B:** There is zero time for bullshit. There is zero time for feeling like we're productive when we're not.  
我们没有时间搞虚的。我们没有时间自欺欺人地觉得自己很高效。  
**[36:23] Speaker B:** These tools make us a lot more productive. Why do you think we're concerned about competitors using the tools?  
这些工具确实让我们的生产力大幅提升。你觉得我们为什么会担心竞争对手使用这些工具？  
**[36:34] Speaker B:** Because we think we're ahead of the competitors. We wouldn't be going through all this trouble if this were secretly reducing our productivity.  
因为我们认为自己领先于竞争对手。如果这些工具实际上在降低我们的生产力，我们不会费这么大劲。  
**[36:43] Speaker B:** We see the end productivity every few months in the form of model launches. There's no kidding yourself about this.  
我们每隔几个月就能通过模型发布看到最终的生产力成果。这方面没法自欺欺人。  
**[36:54] Speaker B:** The models make you more productive. One, people feeling like they're productive is qualitatively predicted by studies like this.  
这些模型确实提升了生产力。第一，人们感觉自己更高效这一点，在类似研究中已经定性地预测到了。  
**[37:00] Speaker A:** But two, if I just look at the end output, obviously you guys are making fast progress.  
但第二，如果我只看最终产出，显然你们确实在快速进步。  
**[37:04] Speaker A:** But the idea was supposed to be that with recursive self-improvement, you make a better AI, the AI helps you build a better next AI, et cetera, et cetera.  
但原本的设想是通过递归自我改进，你造出更好的 AI，这个 AI 帮你构建下一代更好的 AI，如此循环往复。  
**[37:14] Speaker A:** What I see instead—if I look at you, OpenAI, DeepMind—is that people are just shifting around the podium every few months.  
但我看到的情况是——如果看你们、OpenAI、DeepMind——大家只是每隔几个月在领奖台上轮换位置。  
**[37:22] Speaker A:** Maybe you think that stops because you've won or whatever. But why are we not seeing the person with the best coding model have this lasting advantage if in fact there are these enormous productivity gains from the last coding model?  
也许你认为这种情况会因为你们获胜而停止。但如果最新的编程模型真的带来了巨大的生产力提升，为什么我们没有看到拥有最佳编程模型的公司获得持久优势？  
**[37:38] Speaker B:** I think my model of the situation is that there's an advantage that's gradually growing. I would say right now the coding models give maybe, I don't know, a 15-20% total factor speed up. That's my view. Six months ago, it was maybe 5%.  
我对这种情况的理解是，优势在逐渐增长。我认为目前编程模型带来的全要素生产率提升大概是，我也不确定，15-20%。这是我的看法。六个月前大概只有5%。  
**[38:01] Speaker B:** So it didn't matter. 5% doesn't register. It's now just getting to the point where it's one of several factors that kind of matters.  
所以当时没什么影响。5%根本不明显。现在才刚刚达到一个程度，成为几个重要因素之一。  
**[38:06] Speaker B:** That's going to keep speeding up. I think six months ago, there were several companies that were at roughly the same point because this wasn't a notable factor, but I think it's starting to speed up more and more.  
而且这个提升速度会持续加快。我认为六个月前，有几家公司处于大致相同的水平，因为这还不是一个显著因素，但我认为现在开始加速得越来越明显。  
**[38:25] Speaker B:** I would also say there are multiple companies that write models that are used for code and we're not perfectly good at preventing some of these other companies from using our models internally.  
我还要说的是，有多家公司在开发用于编程的模型，而且我们并不能完全阻止其中一些公司在内部使用我们的模型。  
**[38:41] Speaker B:** So I think everything we're seeing is consistent with this kind of snowball model. Again, my theme in all of this is all of this is soft takeoff, soft, smooth exponentials, although the exponentials are relatively steep.  
所以我认为我们看到的一切都符合这种滚雪球模型。再次强调，我对这一切的主题是：这都是软起飞，平缓流畅的指数增长，尽管这些指数曲线相对陡峭。  
**[39:00] Speaker B:** So we're seeing this snowball gather momentum where it's like 10%, 20%, 25%, 40%. As you go, Amdahl's law, you have to get all the things that are preventing you from closing the loop out of the way.  
所以我们看到这个雪球在积累动能，从10%、20%、25%到40%。随着进展，根据 Amdahl 定律，你必须把所有阻碍你形成闭环的因素都清除掉。  
**[39:17] Speaker B:** But this is one of the biggest priorities within Anthropic. Stepping back, before in the stack we were talking about when do we get this on-the-job learning?  
但这是 Anthropic 内部最重要的优先事项之一。退一步说，之前在讨论技术栈时，我们谈到什么时候能实现这种在岗学习？  
**[39:29] Speaker A:** It seems like the point you were making on the coding thing is that we actually don't need on-the-job learning. You can have tremendous productivity improvements, you can have potentially trillions of dollars of revenue for AI companies, without this basic human ability to learn on the job.  
你在编程这件事上的观点似乎是,我们其实不需要在岗学习能力。即使没有人类这种基本的在工作中学习的能力,AI 公司也能获得巨大的生产力提升,也能创造数万亿美元的潜在收入。  
**[39:40] Speaker A:** Maybe that's not your claim, you should clarify. But in most domains of economic activity, people say, "I hired somebody, they weren't that useful for the first few months, and then over time they built up the context, understanding."  
也许这不是你的观点,你应该澄清一下。但在大多数经济活动领域,人们会说:「我雇了一个人,头几个月他们并不是很有用,然后随着时间推移,他们积累了上下文和理解力。」  
**[39:58] Speaker A:** It's actually hard to define what we're talking about here. But they got something and then now they're a powerhouse and they're so valuable to us.  
其实很难定义我们这里在讨论的是什么。但他们掌握了某种东西,然后现在变成了一个强大的生产力,对我们来说非常有价值。  
**[40:05] Speaker A:** If AI doesn't develop this ability to learn on the fly, I'm a bit skeptical that we're going to see huge changes to the world without that ability.  
如果 AI 不发展出这种即时学习的能力,我对没有这种能力就能给世界带来巨大变革这一点持怀疑态度。  
**[40:12] Speaker B:** I think two things here. There's the state of the technology right now. Again, we have these two stages.  
我认为这里有两点。首先是技术的现状。我们还是有这两个阶段。  
**[40:22] Speaker B:** We have the pre-training and RL stage where you throw a bunch of data and tasks into the models and then they generalize. So it's like learning, but it's like learning from more data and not learning over one human or one model's lifetime.  
我们有预训练和强化学习阶段,在这个阶段你把大量数据和任务喂给模型,然后它们进行泛化。所以这像是学习,但这是从更多数据中学习,而不是在一个人或一个模型的生命周期内学习。  
**[40:38] Speaker B:** So again, this is situated between evolution and human learning. But once you learn all those skills, you have them.  
所以这又是介于进化和人类学习之间的。但一旦你学会了所有这些技能,你就拥有了它们。  
**[40:45] Speaker B:** Just like with pre-training, just how the models know more, if I look at a pre-trained model, it knows more about the history of samurai in Japan than I do. It knows more about baseball than I do. It knows more about low-pass filters and electronics, all of these things. Its knowledge is way broader than mine.  
就像预训练一样,模型知道的更多,如果我看一个预训练模型,它对日本武士历史的了解比我多。它对棒球的了解比我多。它对低通滤波器和电子学的了解,所有这些东西,它的知识面比我广得多。  
**[41:08] Speaker B:** So I think even just that may get us to the point where the models are better at everything.  
所以我认为仅凭这一点就可能让我们达到模型在所有方面都更优秀的程度。  
**[41:18] Speaker B:** We also have, again, just with scaling the kind of existing setup, the in-context learning. I would describe it as kind of like human on-the-job learning, but a little weaker and a little short term.  
我们还有,通过扩展现有的设置,上下文学习。我会把它描述为有点像人类的在岗学习,但稍微弱一些,也更短期一些。  
**[41:27] Speaker B:** You look at in-context learning and if you give the model a bunch of examples it does get it. There's real learning that happens in context.  
你看上下文学习,如果你给模型一堆例子,它确实能掌握。在上下文中确实发生了真正的学习。  
**[41:38] Speaker B:** A million tokens is a lot. That can be days of human learning.  
一百万个 token 是很多的。这可能相当于人类好几天的学习量。  
**[41:42] Speaker B:** If you think about the model reading a million words, how long would it take me to read a million? Days or weeks at least. So you have these two things.  
如果你想想模型阅读一百万个词,我读一百万个词需要多长时间?至少好几天或几周。所以你有这两样东西。  
**[41:57] Speaker B:** I think these two things within the existing paradigm may just be enough to get you the "country of geniuses in a data center". I don't know for sure, but I think they're going to get you a large fraction of it.  
我认为在现有范式内,这两样东西可能就足以让你得到「数据中心里的天才之国」。我不能确定,但我认为它们能让你获得很大一部分。  
**[42:04] Speaker B:** There may be gaps, but I certainly think that just as things are, this is enough to generate trillions of dollars of revenue. That's one.  
可能还有差距,但我确实认为就目前的情况而言,这足以产生数万亿美元的收入。这是第一点。  
**[42:10] Speaker B:** Two, is this idea of continual learning, this idea of a single model learning on the job. I think we're working on that too.  
第二点,是持续学习的想法,也就是单个模型在工作中学习的想法。我认为我们也在研究这个。  
**[42:24] Speaker B:** There's a good chance that in the next year or two, we also solve that. Again, I think you get most of the way there without it.  
很有可能在未来一两年内,我们也能解决这个问题。但我再说一遍,我认为没有它你也能达到大部分目标。  
**[42:36] Speaker B:** The trillions of dollars a year market, maybe all of the national security implications and the safety implications that I wrote about in "Adolescence of Technology" can happen without it.  
每年数万亿美元的市场,也许我在《技术的青春期》中写到的所有国家安全影响和安全影响,都可以在没有它的情况下发生。  
**[42:49] Speaker B:** But we, and I imagine others, are working on it. There's a good chance that we will get there within the next year or two.  
但我们,我想其他人也是,正在研究这个问题。很有可能我们会在未来一两年内实现它。  
**[42:57] Speaker B:** There are a bunch of ideas. I won't go into all of them in detail, but  
有很多想法。我不会详细讨论所有这些,但  
**[43:07] Speaker A:** One is just to make the context longer. There's nothing preventing longer contexts from working. You just have to train at longer contexts and then learn to serve them at inference.  
一种方向就是让上下文变得更长。更长的上下文本身没有什么技术障碍,你只需要在更长的上下文上训练模型,然后学会如何在推理时提供服务就行了。  
**[43:14] Speaker A:** Both of those are engineering problems that we are working on, and I would assume others are working on them as well.  
这两个都是工程问题,我们正在解决,我想其他公司应该也在做同样的事情。  
**[43:22] Speaker B:** This context length increase—it seemed like there was a period from 2020 to 2023 where from GPT-3 to GPT-4 Turbo, there was an increase from 2,000 context lengths to 128K.  
关于上下文长度的增长,从2020年到2023年这段时间,从 GPT-3 到 GPT-4 Turbo,上下文长度从2000增加到了128K。  
**[43:31] Speaker B:** I feel like for the two-ish years since then, we've been in the same-ish ballpark.  
但我感觉从那之后的大约两年时间里,我们基本还是在这个范围内徘徊。  
**[43:37] Speaker B:** When context lengths get much longer than that, people report qualitative degradation in the ability of the model to consider that full context.  
当上下文长度远超这个范围时,人们反映模型处理完整上下文的能力出现了质量下降。  
**[43:47] Speaker B:** So I'm curious what you're internally seeing that makes you think "10 million contexts, 100 million contexts to get six months of human learning and building context."  
所以我很好奇你们内部看到了什么,让你觉得「1000万上下文、1亿上下文,才能达到人类六个月的学习和积累上下文」。  
**[43:54] Speaker A:** This isn't a research problem. This is an engineering and inference problem.  
这不是一个研究问题,这是一个工程和推理问题。  
**[43:58] Speaker A:** If you want to serve long context, you have to store your entire KV cache.  
如果你想提供长上下文服务,就必须存储整个 KV cache。  
**[44:06] Speaker A:** It's difficult to store all the memory in the GPUs, to juggle the memory around.  
要把所有这些内存都存在 GPU 里、在各处调度内存,是很困难的。  
**[44:11] Speaker A:** I don't even know the details. At this point, this is at a level of detail that I'm no longer able to follow, although I knew it in the GPT-3 era—"These are the weights, these are the activations you have to store..."  
我甚至不知道具体细节了。现在这些细节已经到了我跟不上的程度,虽然在 GPT-3 时代我还知道「这些是权重,这些是你要存储的激活值……」  
**[44:21] Speaker A:** But these days the whole thing is flipped because we have MoE models and all of that.  
但现在整个局面都变了,因为我们有了 MoE 模型之类的东西。  
**[44:26] Speaker A:** Regarding this degradation you're talking about, without getting too specific, there's two things.  
关于你提到的性能下降问题,不说得太具体的话,有两件事。  
**[44:34] Speaker A:** There's the context length you train at, and there's a context length that you serve at.  
一个是你训练时用的上下文长度,另一个是你服务时提供的上下文长度。  
**[44:41] Speaker A:** If you train at a small context length and then try to serve at a long context length, maybe you get these degradations.  
如果你在短上下文上训练,然后试图在长上下文下提供服务,可能就会出现这些性能下降。  
**[44:49] Speaker A:** It's better than nothing—you might still offer it—but you get these degradations.  
这总比没有好,你可能仍然会提供这种服务,但确实会有性能下降。  
**[44:52] Speaker A:** Maybe it's harder to train at a long context length.  
也许在长上下文上训练本身就更难。  
**[44:56] Speaker B:** I want to, at the same time, ask about maybe some rabbit holes.  
我想同时问一下,是否可能陷入一些兔子洞。  
**[45:01] Speaker B:** Wouldn't you expect that if you had to train on longer context length, that would mean that you're able to get less samples in for the same amount of compute?  
你不觉得如果必须在更长的上下文上训练,那意味着在同样的算力下你能处理的样本数会更少吗?  
**[45:10] Speaker B:** Maybe it's not worth diving deep on that. I want to get an answer to the bigger picture question.  
也许不值得深入探讨这个。我想得到更宏观问题的答案。  
**[45:14] Speaker B:** I don't feel a preference for a human editor that's been working for me for six months versus an AI that's been working with me for six months—what year do you predict that that will be the case?  
当我对一个为我工作了六个月的人类编辑和一个与我协作了六个月的 AI 没有偏好时,你预测这会发生在哪一年?  
**[45:33] Speaker A:** My guess for that is there's a lot of problems where basically we can do this when we have the "country of geniuses in a data center."  
我的猜测是,有很多这样的问题,基本上当我们拥有「数据中心里的天才之国」时就能做到这一点。  
**[45:38] Speaker A:** My picture for that, if you made me guess, is one to two years, maybe one to three years. It's really hard to tell. I have a strong view—99%, 95%—that all this will happen in 10 years.  
如果让我猜的话,我的预期是一到两年,也许是一到三年。真的很难说。我有一个强烈的看法——99%、95% 的把握——这一切会在10年内发生。  
**[45:54] Speaker A:** I think that's just a super safe bet.  
我认为这是一个非常保险的预测。  
**[46:00] Speaker A:** I have a hunch—this is more like a 50/50 thing—that it's going to be more like one to two, maybe more like one to three.  
我有一种直觉——这更像是五五开——会更接近一到两年,也许更像一到三年。  
**[46:04] Speaker A:** So one to three years—country of geniuses, and the slightly less economically valuable task of editing videos.  
所以是一到三年——天才之国,以及经济价值稍低一些的视频编辑任务。  
**[46:10] Speaker B:** It seems pretty economically valuable, let me tell you.  
让我告诉你,这看起来经济价值还挺高的。  
**[46:14] Speaker A:** It's just there are a lot of use cases like that. There are a lot of similar ones.  
只是有很多这样的使用场景,有很多类似的情况。  
**[46:17] Speaker B:** So you're predicting that within one to three years.  
所以你预测是在一到三年内。  
**[46:23] Speaker A:** And then, generally, Anthropic has predicted that by late '26 or early '27 we will have AI systems that "have the ability to navigate interfaces available to humans doing digital work today, intellectual capabilities matching or exceeding that of Nobel Prize winners, and the ability to interface with the physical world."  
总的来说,Anthropic 预测到 2026 年底或 2027 年初,我们将拥有这样的 AI 系统:「能够操作人类现在用于数字工作的界面,拥有匹敌甚至超越诺贝尔奖得主的智力能力,并且能够与物理世界交互。」  
**[46:38] Speaker B:** You gave an interview two months ago with DealBook where you were emphasizing your company's more responsible compute scaling as compared to your competitors.  
两个月前你接受 DealBook 采访时,强调你们公司在算力扩张方面比竞争对手更负责任。  
**[46:48] Speaker B:** I'm trying to square these two views. If you really believe that we're going to have a country of geniuses, you want as big a data center as you can get. There's no reason to slow down.  
我试图理解这两种观点怎么协调。如果你真的相信我们将拥有一个「天才之国」,那你会想要尽可能大的数据中心,没理由放慢脚步。  
**[47:00] Speaker B:** The TAM of a Nobel Prize winner, that can actually do everything a Nobel Prize winner can do, is trillions of dollars. So I'm trying to square this conservatism, which seems rational if you have more moderate timelines, with your stated views about progress.  
一个真正能做到诺贝尔奖得主所能做的一切事情的 AI,其总体可获市场规模是数万亿美元。所以我想理解这种保守态度——如果时间线更温和的话似乎合理——如何与你对进展的公开观点相符。  
**[47:16] Speaker A:** It actually all fits together. We go back to this fast, but not infinitely fast, diffusion. Let's say that we're making progress at this rate. The technology is making progress this fast.  
其实这一切都能自洽。我们回到那个「快速但并非无限快」的扩散过程。假设我们正以这样的速度取得进展,技术正以这样的速度发展。  
**[47:29] Speaker A:** I have very high conviction that we're going to get there within a few years. I have a hunch that we're going to get there within a year or two.  
我非常确信我们会在几年内达到那个目标。我有种直觉,我们会在一两年内就达到。  
**[47:41] Speaker A:** So there's a little uncertainty on the technical side, but pretty strong confidence that it won't be off by much. What I'm less certain about is, again, the economic diffusion side.  
所以技术层面有些不确定性,但我很有信心偏差不会太大。我不太确定的是,经济扩散这一面。  
**[47:51] Speaker A:** I really do believe that we could have models that are a country of geniuses in the data center in one to two years.  
我真的相信,我们可能在一到两年内就能在数据中心里拥有相当于一个天才之国的模型。  
**[48:03] Speaker A:** One question is: How many years after that do the trillions in revenue start rolling in? I don't think it's guaranteed that it's going to be immediate.  
问题在于:那之后多少年,数万亿美元的收入才会开始滚滚而来?我不认为保证会立即发生。  
**[48:19] Speaker A:** It could be one year, it could be two years, I could even stretch it to five years although I'm skeptical of that. So we have this uncertainty. Even if the technology goes as fast as I suspect that it will, we don't know exactly how fast it's going to drive revenue.  
可能是一年,可能是两年,甚至我可以拉长到五年,虽然我对此持怀疑态度。所以我们面临这种不确定性。即使技术发展像我预期的那样快,我们也不确切知道它会以多快的速度带动收入增长。  
**[48:41] Speaker A:** We know it's coming, but with the way you buy these data centers, if you're off by a couple years, that can be ruinous. It is just like how I wrote in "Machines of Loving Grace."  
我们知道它会到来,但以购买这些数据中心的方式来看,如果时间偏差几年,可能是灾难性的。就像我在「Machines of Loving Grace」里写的那样。  
**[48:50] Speaker A:** I said I think we might get this powerful AI, this "country of genius in the data center." That description you gave comes from "Machines of Loving Grace." I said we'll get that in 2026, maybe 2027. Again, that is my hunch. I wouldn't be surprised if I'm off by a year or two, but that is my hunch.  
我说过我认为我们可能会得到这种强大的 AI,这个「数据中心里的天才之国」。你提到的那个描述就来自「Machines of Loving Grace」。我说过我们会在 2026 年得到它,也许是 2027 年。再次强调,这是我的直觉。如果偏差一两年我也不会惊讶,但这就是我的直觉。  
**[49:08] Speaker A:** Let's say that happens. That's the starting gun. How long does it take to cure all the diseases? That's one of the ways that drives a huge amount of economic value. You cure every disease. There's a question of how much of that goes to the pharmaceutical company or the AI company, but there's an enormous consumer surplus because—assuming we can get access for everyone, which I care about greatly—we cure all of these diseases.  
假设那真的发生了,那就是发令枪响。治愈所有疾病需要多长时间?这是带动巨大经济价值的方式之一。你治愈了所有疾病。有个问题是这些价值有多少归制药公司还是 AI 公司,但会产生巨大的消费者剩余,因为——假设我们能让所有人都获得,这是我非常关心的——我们治愈了所有这些疾病。  
**[49:34] Speaker A:** How long does it take? You have to do the biological discovery, you have to manufacture the new drug, you have to go through the regulatory process. We saw this with vaccines and COVID. We got the vaccine out to everyone, but it took a year and a half.  
这需要多长时间?你必须进行生物学发现,必须生产新药,必须通过监管流程。我们在疫苗和 COVID 上见识过这个过程。我们把疫苗推广到所有人,但花了一年半时间。  
**[49:52] Speaker A:** My question is: How long does it take to get the cure for everything—which AI is the genius that can in theory invent—out to everyone? How long from when that AI first exists?  
我的问题是:把治愈一切疾病的方法——AI 这个天才理论上能发明出来——推广到所有人需要多长时间?从那个 AI 首次出现开始算起要多久?  
**[50:03] Speaker A:** In the lab to when diseases have actually been cured for everyone?  
从实验室研发出疗法,到疾病真正在所有人群中被治愈,需要多久?  
**[50:09] Speaker A:** We've had a polio vaccine for 50 years. We're still trying to eradicate it in the most remote corners of Africa.  
我们有小儿麻痹症疫苗已经50年了,但我们仍在努力在非洲最偏远的角落根除这种疾病。  
**[50:14] Speaker A:** The Gates Foundation is trying as hard as they can. Others are trying as hard as they can. But that's difficult.  
Gates 基金会在竭尽全力,其他机构也在竭尽全力,但这很困难。  
**[50:20] Speaker A:** Again, I don't expect most of the economic diffusion to be as difficult as that. That's the most difficult case. But there's a real dilemma here.  
当然,我不认为大多数经济扩散会像那样困难。那是最困难的情况。但这里确实存在一个真实的困境。  
**[50:32] Speaker A:** Where I've settled on it is that it will be faster than anything we've seen in the world, but it still has its limits.  
我得出的结论是,它的扩散速度会比我们在世界上见过的任何事物都快,但仍然有其局限性。  
**[50:39] Speaker A:** So when we go to buying data centers, again, the curve I'm looking at is we've had a 10x a year increase every year.  
所以当我们考虑购买数据中心时,我看到的增长曲线是:我们每年都有10倍的年增长。  
**[50:47] Speaker A:** At the beginning of this year, we're looking at $10 billion in annualized revenue.  
今年年初,我们的年化收入是100亿美元。  
**[50:54] Speaker A:** We have to decide how much compute to buy. It takes a year or two to actually build out the data centers, to reserve the data center.  
我们必须决定购买多少算力。实际建设数据中心、预留数据中心需要一到两年的时间。  
**[51:02] Speaker A:** Basically I'm saying, in 2027, how much compute do I get?  
基本上我在问:2027年,我能获得多少算力?  
**[51:10] Speaker A:** I could assume that the revenue will continue growing 10x a year, so it'll be $100 billion at the end of 2026 and $1 trillion at the end of 2027.  
我可以假设收入继续以每年10倍的速度增长,那么2026年底会是1000亿美元,2027年底会是1万亿美元。  
**[51:24] Speaker A:** Actually it would be $5 trillion dollars of compute because it would be $1 trillion a year for five years.  
实际上需要5万亿美元的算力,因为那是每年1万亿美元,持续五年。  
**[51:31] Speaker A:** I could buy $1 trillion of compute that starts at the end of 2027.  
我可以购买1万亿美元的算力,在2027年底开始启用。  
**[51:39] Speaker A:** If my revenue is not $1 trillion dollars, if it's even $800 billion, there's no force on earth, there's no hedge on earth that could stop me from going bankrupt if I buy that much compute.  
如果我的收入不是1万亿美元,哪怕是8000亿美元,如果我购买那么多算力,世界上没有任何力量,没有任何对冲手段能阻止我破产。  
**[51:49] Speaker A:** Even though a part of my brain wonders if it's going to keep growing 10x, I can't buy $1 trillion a year of compute in 2027.  
尽管我脑子里有一部分在想它是否会继续保持10倍增长,但我不能在2027年购买每年1万亿美元的算力。  
**[51:56] Speaker A:** If I'm just off by a year in that rate of growth, or if the growth rate is 5x a year instead of 10x a year, then you go bankrupt.  
如果我对增长速度的判断哪怕只偏差一年,或者增长率是每年5倍而不是10倍,那你就会破产。  
**[52:03] Speaker A:** So you end up in a world where you're supporting hundreds of billions, not trillions.  
所以你最终会处于一个支持数千亿美元而非数万亿美元的世界。  
**[52:07] Speaker A:** You accept some risk that there's so much demand that you can't support the revenue, and you accept some risk that you got it wrong and it's still slow.  
你要接受一些风险,即需求太大以至于你无法支撑收入增长,同时也要接受一些风险,即你判断错了而增长仍然缓慢。  
**[52:17] Speaker A:** When I talked about behaving responsibly, what I meant actually was not the absolute amount.  
当我谈到负责任的行为时,我实际指的不是绝对金额。  
**[52:25] Speaker A:** I think it is true we're spending somewhat less than some of the other players.  
确实,我们的支出比其他一些参与者要少一些。  
**[52:33] Speaker A:** It's actually the other things, like have we been thoughtful about it or are we YOLOing and saying, we're going to do $100 billion here or $100 billion there?  
实际上是其他方面,比如我们是否经过深思熟虑,还是在随意冒险说「我们要在这里投1000亿美元或在那里投1000亿美元」?  
**[52:38] Speaker A:** I get the impression that some of the other companies have not written down the spreadsheet, that they don't really understand the risks they're taking.  
我感觉其他一些公司并没有做详细的电子表格分析,他们并不真正理解自己承担的风险。  
**[52:43] Speaker A:** They're just doing stuff because it sounds cool. We've thought carefully about it.  
他们只是因为听起来很酷就去做。我们则经过了仔细思考。  
**[52:51] Speaker A:** We're an enterprise business. Therefore, we can rely more on revenue. It's less fickle than consumer.  
我们是企业业务,因此可以更依赖收入。它比消费者业务更稳定,不那么善变。  
**[52:55] Speaker A:** We have better margins, which is the buffer between buying too much and buying too little.  
我们有更好的利润率,这是购买过多和购买过少之间的缓冲。  
**[53:01] Speaker A:** I think we bought an amount that allows us to capture pretty strong upside worlds.  
我认为我们购买的数量能让我们抓住相当强劲的上行空间。  
**[53:05] Speaker A:** It won't capture the full 10x a year. Things would have to go pretty badly for us to be in financial trouble.  
它无法完全捕捉每年10倍的增长。但情况得变得相当糟糕,我们才会陷入财务困境。  
**[53:09] Speaker A:** So we've thought carefully and we've made that balance. That's what I mean when I say that we're being responsible.  
所以我们经过仔细思考并做了这种平衡。这就是我说我们负责任的意思。  
**[53:19] Speaker A:** So it seems like it's possible that we actually just have different definitions of the country of a genius in a data center.  
所以看起来我们可能对「数据中心里的天才之国」有不同的定义。  
**[53:56] Speaker A:** Because when I think of actual human geniuses, an actual country of human geniuses in a data center,  
因为当我想到真正的人类天才,想到数据中心里有一整个国家的人类天才时,  
**[54:02] Speaker A:** I would happily buy $5 trillion worth of compute to run an actual country of human geniuses in a data center.  
我会非常乐意花5万亿美元购买算力来运行这样一个数据中心里的天才国度。  
**[54:08] Speaker A:** Let's say JPMorgan or Moderna or whatever doesn't want to use them.  
假设 JPMorgan 或 Moderna 或其他公司不想使用他们。  
**[54:11] Speaker A:** I've got a country of geniuses.  
我有一整个国家的天才啊。  
**[54:14] Speaker A:** They'll start their own company. If they can't start their own company and they're bottlenecked by clinical trials…  
他们可以自己创办公司。如果他们无法创办自己的公司,又受限于临床试验……  
**[54:18] Speaker A:** It is worth stating that with clinical trials, most clinical trials fail because the drug doesn't work. There's no efficacy.  
值得说明的是,对于临床试验,大多数临床试验失败是因为药物本身无效,没有疗效。  
**[54:22] Speaker B:** I make exactly that point in "Machines of Loving Grace". I say the clinical trials are going to go much faster than we're used to, but not infinitely fast.  
我在「Machines of Loving Grace」中正是提出了这一点,我说临床试验会比我们习惯的速度快得多,但不会无限快。  
**[54:30] Speaker A:** Okay, and then suppose it takes a year for the clinical trials to work out so that you're getting revenue from that and can make more drugs.  
好的,那么假设临床试验需要一年时间才能完成,这样你才能从中获得收入并制造更多药物。  
**[54:39] Speaker A:** Okay, well, you've got a country of geniuses and you're an AI lab.  
好吧,你有一整个国家的天才,而且你是一家 AI 实验室。  
**[54:44] Speaker A:** You could use many more AI researchers.  
你可以使用更多 AI 研究人员。  
**[54:50] Speaker A:** You also think there are these self-reinforcing gains from smart people working on AI tech.  
你也认为聪明人研究 AI 技术会产生这种自我强化的收益。  
**[54:56] Speaker A:** You can have the data center working on AI progress.  
你可以让数据中心致力于推进 AI 的发展。  
**[55:01] Speaker A:** Are there substantially more gains from buying $1 trillion a year of compute versus $300 billion a year of compute?  
每年购买1万亿美元的算力相比每年3000亿美元的算力,是否会带来显著更多的收益?  
**[55:07] Speaker B:** If your competitor is buying a trillion, yes there is.  
如果你的竞争对手购买了1万亿,那确实会有。  
**[55:09] Speaker A:** Well, no, there's some gain, but then again, there's this chance that they go bankrupt before.  
嗯,不,会有一些收益,但话说回来,也有他们在此之前就破产的风险。  
**[55:17] Speaker A:** Again, if you're off by only a year, you destroy yourselves. That's the balance.  
再说一遍,如果你仅仅偏差一年,就会毁掉自己。这就是需要平衡的地方。  
**[55:23] Speaker B:** We're buying a lot. We're buying a hell of a lot.  
我们买了很多。我们买了非常多。  
**[55:30] Speaker B:** We're buying an amount that's comparable to what the biggest players in the game are buying.  
我们购买的规模与这个领域最大玩家的购买量相当。  
**[55:39] Speaker B:** But if you're asking me, "Why haven't we signed $10 trillion of compute starting in mid-2027?"...  
但如果你问我「为什么我们没有签约从2027年中开始的10万亿美元算力?」……  
**[55:44] Speaker B:** First of all, it can't be produced. There isn't that much in the world.  
首先,这根本生产不出来。世界上没有那么多。  
**[55:50] Speaker B:** But second, what if the country of geniuses comes, but it comes in mid-2028 instead of mid-2027? You go bankrupt.  
其次,如果天才国度确实到来,但它是在2028年中而不是2027年中到来呢?你就会破产。  
**[55:56] Speaker A:** So if your projection is one to three years, it seems like you should want $10 trillion of compute by 2029 at the latest?  
所以如果你的预测是一到三年,似乎你应该最迟在2029年前想要10万亿美元的算力?  
**[56:11] Speaker A:** Even in the longest version of the timelines you state, the compute you are ramping up to build doesn't seem in accordance.  
即使按照你所说的最长时间线版本,你正在扩建的算力规模似乎也不相符。  
**[56:16] Speaker B:** What makes you think that?  
你为什么这么认为?  
**[56:21] Speaker A:** Human wages, let's say, are on the order of $50 trillion a year—  
比如说,人类工资总额大约是每年50万亿美元量级——  
**[56:27] Speaker B:** So I won't talk about Anthropic in particular, but if you talk about the industry, the amount of compute the industry is building this year is probably, call it, 10-15 gigawatts.  
我不具体谈 Anthropic,但如果说整个行业,今年行业正在建设的算力规模大概是,姑且说,10到15吉瓦。  
**[56:48] Speaker B:** It goes up by roughly 3x a year. So next year's 30-40 gigawatts. 2028 might be 100 gigawatts. 2029 might be like 300 gigawatts.  
每年大约增长3倍。所以明年是30到40吉瓦。2028年可能是100吉瓦。2029年可能会达到300吉瓦左右。  
**[57:03] Speaker B:** I'm doing the math in my head, but each gigawatt costs maybe $10 billion, on the order of $10-15 billion a year.  
我在心算,但每吉瓦的成本大约是100亿美元,大概每年在100到150亿美元的量级。  
**[57:14] Speaker B:** You put that all together and you're getting about what you described. You're getting exactly that. You're getting multiple trillions a year by 2028 or 2029.  
你把这些加在一起,就得到了你所描述的数字。完全就是那样。到2028或2029年,每年会达到数万亿美元。  
**[57:23] Speaker B:** You're getting exactly what you predict. That's for the industry.  
这正是你预测的。这是整个行业的数据。  
**[57:26] Speaker A:** That's for the industry, that's right. Suppose Anthropic's compute keeps 3x-ing a year, and then by 2027-28, you have 10 gigawatts.  
那是整个行业的,没错。假设 Anthropic 的算力持续每年3倍增长,那么到2027-28年,你们会有10吉瓦。  
**[57:34] Speaker A:** Multiply that by, as you say, $10 billion. So then it's like $100 billion a year.  
按你说的乘以100亿美元。那么每年就是大约1000亿美元。  
**[57:40] Speaker A:** But then you're saying the TAM by 2028 is $200 billion.  
但你又说到2028年的 TAM 是2000亿美元。  
**[57:43] Speaker B:** Again, I don't want to give exact numbers for Anthropic, but these numbers are too small.  
再说一次,我不想给出 Anthropic 的确切数字,但这些数字都太小了。  
**[57:48] Speaker A:** Okay, interesting. You've told investors that you plan to be profitable starting in 2028.  
好的,很有意思。你告诉投资者,你们计划从2028年开始实现盈利。  
**[58:49] Speaker A:** This is the year when we're potentially getting the country of geniuses as a data center.  
这一年我们可能会拥有相当于一个天才之国规模的数据中心。  
**[58:55] Speaker A:** This is now going to unlock all this progress in medicine and health and new technologies.  
这将释放医学、健康和新技术领域的大量进展。  
**[59:02] Speaker A:** Wouldn't this be exactly the time where you'd want to reinvest in the business and build bigger "countries" so they can make more discoveries?  
这难道不正是你们想要再投资于业务、建造更大的「国家」以实现更多发现的时候吗?  
**[59:16] Speaker B:** Profitability is this kind of weird thing in this field.  
在这个领域,盈利能力是一个有点奇怪的概念。  
**[59:21] Speaker B:** I don't think in this field profitability is actually a measure of spending down versus investing in the business.  
我认为在这个领域,盈利能力实际上并不是衡量减少开支和投资业务之间取舍的指标。  
**[59:32] Speaker B:** Let's just take a model of this.  
我们来建立一个模型。  
**[59:36] Speaker B:** I actually think profitability happens when you underestimated the amount of demand you were going to get, and loss happens when you overestimated the amount of demand you were going to get, because you're buying the data centers ahead of time.  
我实际上认为,盈利发生在你低估了需求量的时候,而亏损发生在你高估了需求量的时候,因为你是提前购买数据中心的。  
**[59:46] Speaker B:** Think about it this way. Again, these are stylized facts. These numbers are not exact. I'm just trying to make a toy model here.  
这样想吧。再说一遍,这些是简化的假设。这些数字并不精确。我只是在这里构建一个简单模型。  
**[59:56] Speaker B:** Let's say half of your compute is for training and half of your compute is for inference.  
假设你一半的算力用于训练,一半用于推理。  
**[01:00:02] Speaker B:** The inference has some gross margin that's more than 50%.  
推理部分有超过50%的毛利率。  
**[01:00:07] Speaker B:** So what that means is that if you were in steady state, you build a data center and if you knew exactly the demand you were getting, you would get a certain amount of revenue.  
这意味着,如果你处于稳定状态,建造了一个数据中心,并且准确知道你会获得的需求量,你就会获得一定数量的收入。  
**[01:00:23] Speaker B:** Let's say you pay $100 billion a year for compute. On $50 billion a year you support $150 billion of revenue.  
假设你每年为算力支付1000亿美元。其中500亿美元支撑1500亿美元的收入。  
**[01:00:28] Speaker B:** The other $50 billion is used for training.  
另外500亿美元用于训练。  
**[01:00:36] Speaker B:** Basically you're profitable and you make $50 billion of profit.  
基本上你是盈利的,可以赚500亿美元利润。  
**[01:00:40] Speaker B:** Those are the economics of the industry today, or not today but where we're projecting forward in a year or two.  
这就是这个行业的经济状况,或者说不是现在,而是我们对未来一两年的预测。  
**[01:00:45] Speaker B:** The only thing that makes that not the case is if you get less demand than $50 billion.  
唯一使情况不同的是,如果你获得的需求少于500亿美元。  
**[01:00:49] Speaker B:** Then you have more than 50% of your data center for research and you're not profitable.  
那么你就会有超过50%的数据中心用于研究,而你就不盈利了。  
**[01:00:57] Speaker B:** So you train stronger models, but you're not profitable.  
所以你训练更强大的模型,但你不盈利。  
**[01:01:01] Speaker B:** If you get more demand than you thought, then research gets squeezed, but you're kind of able to support more inference and you're more profitable.  
如果你获得的需求比预期多,那么研究就会被压缩,但你可以支撑更多推理,从而更加盈利。  
**[01:01:16] Speaker B:** Maybe I'm not explaining it well, but the thing I'm trying to say is that you decide the amount of compute first.  
也许我没解释清楚,但我想说的是,你首先决定算力的数量。  
**[01:01:19] Speaker B:** Then you have some target desire of inference versus training, but that gets determined by demand.  
然后你对推理和训练的比例有一些目标期望,但这是由需求决定的。  
**[01:01:24] Speaker B:** It doesn't get determined by you.  
不是由你决定的。  
**[01:01:28] Speaker A:** What I'm hearing is the reason you're predicting profit is that you are systematically underinvesting in compute?  
我听到的是,你预测盈利的原因是你在系统性地对算力投资不足?  
**[01:01:37] Speaker B:** No, no, no. I'm saying it's hard to predict.  
不不不。我是说这很难预测。  
**[01:01:43] Speaker B:** These things about 2028 and when it will happen, that's our attempt to do the best we can with investors.  
关于2028年以及何时实现的这些事情,是我们尽力向投资者给出的最佳预测。  
**[01:01:46] Speaker B:** All of this stuff is really uncertain because of the cone of uncertainty.  
所有这些事情都非常不确定,因为存在不确定性锥。  
**[01:01:50] Speaker B:** We could be profitable in 2026 if the revenue grows fast enough.  
如果收入增长足够快,我们可能在2026年就能盈利。  
**[01:01:58] Speaker B:** If we overestimate or underestimate the next year, that could swing wildly.  
如果我们高估或低估了明年的情况,结果可能会大幅波动。  
**[01:02:04] Speaker B:** What I'm trying to get at is that you have a model in your head of a business that invests, invests, invests, gets scale and then becomes profitable.  
我想说的是,你脑海中有一个商业模型,就是不断投资、投资、投资,获得规模,然后实现盈利。  
**[01:02:09] Speaker B:** There's a single point at which things turn around.  
有一个单一的转折点。  
**[01:02:14] Speaker B:** I don't think the economics of this industry work that way.  
我认为这个行业的经济逻辑不是那样运作的。  
**[01:02:16] Speaker A:** I see. So if I'm understanding correctly, you're saying that because of the discrepancy...  
我明白了。所以如果我理解正确的话,你是说因为这种差异...  
**[01:02:24] Speaker A:** Between the amount of compute we should have gotten and the amount of compute we got, we were sort of forced to make profit. But that doesn't mean we're going to continue making profit. We're going to reinvest the money  
我们应该获得的算力和实际获得的算力之间存在差距,这在某种程度上迫使我们盈利。但这并不意味着我们会继续保持盈利。我们会把这些钱再投资出去  
**[01:02:33] Speaker A:** because now AI has made so much progress and we want a bigger country of geniuses. So revenue is high, but losses are also high.  
因为现在AI已经取得了很大进展,我们想要一个更大的天才国度。所以收入很高,但亏损也很高。  
**[01:02:44] Speaker A:** If every year we predict exactly what the demand is going to be, we'll be profitable every year.  
如果我们每年都能准确预测需求会是多少,那我们每年都会盈利。  
**[01:02:50] Speaker A:** Because spending 50% of your compute on research, roughly, plus a gross margin that's higher than 50% and correct demand prediction leads to profit. That's the profitable business model that I think is kind of there, but obscured by these building ahead and prediction errors.  
因为大约把50%的算力用于研究,加上高于50%的毛利率,再加上准确的需求预测,就能实现盈利。我认为这个盈利的商业模式其实是存在的,只是被提前建设和预测误差所掩盖了。  
**[01:03:13] Speaker B:** I guess you're treating the 50% as a sort of given constant, whereas in fact, if AI progress is fast and you can increase the progress by scaling up more, you should just have more than 50% and not make profit.  
我想你是把这50%当作一个既定的常数,但实际上,如果AI进展很快,而且你可以通过加大投入来加速进展,那你就应该投入超过50%,而不是追求盈利。  
**[01:03:24] Speaker A:** But here's what I'll say. You might want to scale it up more. Remember the log returns to scale.  
但我要说的是这个。你可能想要进一步扩大规模。记住对数收益规律。  
**[01:03:34] Speaker A:** If 70% would get you a very little bit of a smaller model through a factor of 1.4x... That extra $20 billion, each dollar there is worth much less to you because of the log-linear setup.  
如果投入70%只能让你获得一个稍小一点的模型,通过1.4倍的因子... 那额外的200亿美元,由于对数线性的设定,每一美元对你的价值都要小得多。  
**[01:03:51] Speaker A:** So you might find that it's better to invest that $20 billion in serving inference or in hiring engineers who are better at what they're doing.  
所以你可能会发现,把这200亿美元投资在推理服务上,或者招聘更优秀的工程师,会是更好的选择。  
**[01:04:05] Speaker A:** So the reason I said 50%... That's not exactly our target. It's not exactly going to be 50%. It'll probably vary over time. What I'm saying is the log-linear return, what it leads to is you spend of order one fraction of the business. Like not 5%, not 95%. Then you get diminishing returns.  
所以我说50%的原因是... 这并不完全是我们的目标。不会正好是50%。它可能会随时间变化。我想说的是,对数线性收益导致的结果是,你会花费业务中相当大的一部分。比如不是5%,也不是95%。然后你就会遇到收益递减。  
**[01:04:28] Speaker B:** I feel strange that I'm convincing Dario to believe in AI progress or something.  
我觉得很奇怪,我竟然在说服Dario相信AI进展之类的。  
**[01:04:34] Speaker B:** Okay, you don't invest in research because it has diminishing returns, but you invest in the other things you mentioned.  
好吧,你不投资研究是因为它有收益递减,但你会投资你提到的其他事情。  
**[01:04:37] Speaker A:** I think profit at a sort of macro level— Again, I'm talking about diminishing returns, but after you're spending $50 billion a year.  
我认为在宏观层面上的盈利—— 再说一次,我在谈收益递减,但那是在你每年花费500亿美元之后。  
**[01:04:46] Speaker A:** This is a point I'm sure you would make, but diminishing returns on a genius could be quite high. More generally,  
这一点我确信你也会提出,但天才的收益递减点可能相当高。更广泛地说,  
**[01:04:54] Speaker A:** what is profit in a market economy? Profit is basically saying other companies in the market can do more things with this money than I can.  
在市场经济中利润是什么?利润基本上是在说,市场上的其他公司能用这笔钱做比我更多的事情。  
**[01:05:02] Speaker A:** Put aside Anthropic. I don't want to give information about Anthropic. That's why I'm giving these stylized numbers. But let's just derive the equilibrium of the industry.  
抛开Anthropic不谈。我不想透露Anthropic的信息。这就是为什么我在用这些简化的数字。但让我们来推导一下这个行业的均衡状态。  
**[01:05:10] Speaker A:** Why doesn't everyone spend 100% of their compute on training and not serve any customers? It's because if they didn't get any revenue, they couldn't raise money, they couldn't do compute deals, they couldn't buy more compute the next year.  
为什么不是每个人都把100%的算力用于训练而不服务任何客户?因为如果他们没有任何收入,就无法融资,无法达成算力交易,无法在第二年购买更多算力。  
**[01:05:27] Speaker A:** So there's going to be an equilibrium where every company spends less than 100% on training and certainly less than 100% on inference.  
所以会存在一个均衡点,每家公司在训练上的投入都会少于100%,在推理上的投入当然也少于100%。  
**[01:05:38] Speaker A:** It should be clear why you don't just serve the current models and never train another model, because then you don't have any demand because you'll fall behind. So there's some equilibrium. It's not gonna be 10%, it's not gonna be 90%. Let's just say as a stylized fact, it's 50%.  
应该很清楚为什么你不能只提供当前模型而永远不训练新模型,因为那样你就会落后,就不会有任何需求。所以存在某个均衡点。它不会是10%,也不会是90%。作为一个简化的事实,我们就说它是50%。  
**[01:05:55] Speaker A:** That's what I'm getting at. I think we're gonna be in a position where that equilibrium of how much you spend on training is less than the gross margins that you're able to get on compute.  
这就是我想说的。我认为我们会处于这样一个位置:你在训练上花费的均衡比例会低于你在算力上能够获得的毛利率。  
**[01:06:08] Speaker A:** So the underlying economics are profitable. The problem is you have this hellish demand prediction problem when you're buying the next year of compute and you might guess under and be very profitable but have no compute for research.  
所以底层的经济模型是盈利的。问题在于,当你购买下一年的算力时,会面临一个极其棘手的需求预测问题,你可能会低估需求,结果是非常盈利但没有算力用于研究。  
**[01:06:21] Speaker A:** Or you might guess over and you are not profitable and you have all the compute for research in the world. Does that make sense?  
或者你可能会高估需求,结果是不盈利,但拥有世界上所有可用于研究的算力。这样说清楚了吗?  
**[01:06:36] Speaker A:** Just as a dynamic model of the industry?  
作为这个行业的动态模型来理解?  
**[01:06:42] Speaker B:** Maybe stepping back, I'm not saying I think the "country of geniuses" is going to come in two years and therefore you should buy this compute.  
也许退一步说,我并不是说我认为「天才之国」会在两年内到来,因此你应该购买这些算力。  
**[01:06:47] Speaker B:** To me, the end conclusion you're arriving at makes a lot of sense.  
对我来说,你得出的最终结论很有道理。  
**[01:06:51] Speaker B:** But that's because it seems like "country of geniuses" is hard and there's a long way to go.  
但那是因为「天才之国」似乎很难实现,还有很长的路要走。  
**[01:06:57] Speaker B:** So stepping back, the thing I'm trying to get at is more that it seems like your worldview is compatible with somebody who says, "We're like 10 years away from a world in which we're generating trillions of dollars of value."  
所以退一步说,我想要表达的更多是,你的世界观似乎与那些认为「我们距离创造数万亿美元价值的世界还有大约10年」的人是一致的。  
**[01:07:07] Speaker A:** That's just not my view. So I'll make another prediction. It is hard for me to see that there won't be trillions of dollars in revenue before 2030.  
那不是我的观点。所以我再做一个预测。我很难想象在2030年之前不会有数万亿美元的收入。  
**[01:07:20] Speaker A:** I can construct a plausible world.  
我可以构想一个合理的情景。  
**[01:07:26] Speaker A:** It takes maybe three years. That would be the end of what I think is plausible.  
可能需要三年时间。那将是我认为合理的最长时限。  
**[01:07:31] Speaker A:** Like in 2028, we get the real "country of geniuses in the data center".  
比如在2028年,我们实现了真正的「数据中心里的天才之国」。  
**[01:07:36] Speaker A:** The revenue's going into the low hundreds of billions by 2028, and then the country of geniuses accelerates it to trillions.  
到2028年收入达到数千亿美元的低端,然后天才之国将其加速到数万亿。  
**[01:07:46] Speaker A:** We're basically on the slow end of diffusion.  
我们基本上处于扩散的慢速端。  
**[01:07:52] Speaker A:** It takes two years to get to the trillions. That would be the world where it takes until 2030.  
需要两年时间达到数万亿。那就是要到2030年的情景。  
**[01:07:59] Speaker A:** I suspect even composing the technical exponential and diffusion exponential, we'll get there before 2030.  
我怀疑即使叠加技术指数增长和扩散指数增长,我们也会在2030年之前达到那个目标。  
**[01:08:05] Speaker B:** So you laid out a model where Anthropic makes profit because it seems like fundamentally we're in a compute-constrained world.  
所以你阐述了一个模型,在这个模型中Anthropic能够盈利,因为从根本上看我们处在一个算力受限的世界。  
**[01:08:14] Speaker B:** So eventually we keep growing compute—  
所以最终我们持续增长算力——  
**[01:08:21] Speaker A:** I think the way the profit comes is... Again, let's just abstract the whole industry here. Let's just imagine we're in an economics textbook.  
我认为利润产生的方式是……再次强调,让我们把整个行业抽象化。让我们想象一下我们在经济学教科书里。  
**[01:08:27] Speaker A:** We have a small number of firms. Each can invest a limited amount.  
我们有少数几家公司。每家都只能投资有限的金额。  
**[01:08:33] Speaker A:** Each can invest some fraction in R&D. They have some marginal cost to serve.  
每家都可以投资一定比例在研发上。它们都有一定的边际服务成本。  
**[01:08:38] Speaker A:** The gross profit margins on that marginal cost are very high because inference is efficient.  
基于边际成本的毛利率非常高,因为推理是高效的。  
**[01:08:47] Speaker A:** There's some competition, but the models are also differentiated.  
存在一些竞争,但模型之间也有差异化。  
**[01:08:52] Speaker A:** Companies will compete to push their research budgets up.  
公司会竞相提高它们的研究预算。  
**[01:08:55] Speaker A:** But because there's a small number of players, we have the... What is it called?  
但由于参与者数量少,我们有那个……叫什么来着?  
**[01:09:00] Speaker A:** The Cournot equilibrium, I think, is what the small number of firm equilibrium is.  
Cournot均衡,我想,就是少数企业均衡的那个概念。  
**[01:09:05] Speaker A:** The point is it doesn't equilibrate to perfect competition with zero margins.  
重点是它不会达到零利润率的完全竞争均衡。  
**[01:09:15] Speaker A:** If there's three firms in the economy and all are kind of independently behaving rationally, it doesn't equilibrate to zero.  
如果经济中有三家公司,并且都各自独立地理性行事,它不会达到零利润的均衡。  
**[01:09:20] Speaker B:** Help me understand that, because right now we do have three leading firms and they're not making profit. So what is changing?  
帮我理解一下,因为现在我们确实有三家领先公司,但它们并没有盈利。那么是什么在改变?  
**[01:09:33] Speaker A:** Again, the gross margins right now are very positive.  
再次强调,目前的毛利率是非常正向的。  
**[01:09:38] Speaker A:** What's happening is a combination of two things. One is that we're still in the exponential scale-up phase of compute. A model gets trained. Let's say a model got trained that cost $1 billion last year.  
正在发生的是两件事的组合。一是我们仍然处于算力的指数扩张阶段。一个模型被训练出来。假设去年训练了一个花费10亿美元的模型。  
**[01:10:02] Speaker A:** Then this year it produced $4 billion of revenue and cost $1 billion to inference from. Again, I'm using stylized numbers here, but that would be 75% gross margins and this 25% tax.  
然后今年它产生了40亿美元的收入,推理成本是10亿美元。再次强调,我这里用的是简化的数字,但那将是75%的毛利率和25%的成本占比。  
**[01:10:12] Speaker A:** So that model as a whole makes $2 billion.  
所以这个模型整体赚了20亿美元。  
**[01:10:23] Speaker A:** But at the same time, we're spending $10 billion to train the next model because there's an exponential scale-up. So the company loses money. Each model makes money, but the company loses money.  
但与此同时,我们要花100亿美元来训练下一个模型,因为存在指数级的扩张。所以公司是亏损的。每个模型本身都赚钱,但公司整体是亏损的。  
**[01:10:31] Speaker A:** The equilibrium I'm talking about is an equilibrium where we have the "country of geniuses in a data center", but that model training scale-up has equilibrated more. Maybe it's still going up. We're still trying to predict the demand, but it's more leveled out.  
我所说的均衡状态是指,我们已经拥有了「数据中心里的天才国度」,但模型训练的规模扩张已经趋于平衡。也许仍在增长,我们还在努力预测需求,但增速已经趋于平稳了。  
**[01:10:49] Speaker B:** I'm confused about a couple of things there. Let's start with the current world.  
我对其中几点有些困惑。我们先从当前的情况说起吧。  
**[01:10:56] Speaker B:** In the current world, you're right that, as you said before, if you treat each individual model as a company, it's profitable.  
在当前的情况下,你说得对,正如你之前所说,如果把每个单独的模型看作一家公司,它是盈利的。  
**[01:11:05] Speaker B:** But of course, a big part of the production function of being a frontier lab is training the next model, right?  
但显然,作为前沿实验室,生产函数的重要组成部分就是训练下一个模型,对吧?  
**[01:11:11] Speaker A:** Yes, that's right.  
是的,没错。  
**[01:11:13] Speaker B:** If you didn't do that, then you'd make profit for two months and then you wouldn't have margins because you wouldn't have the best model.  
如果不这样做,那你只能盈利两个月,然后就没有利润空间了,因为你不再拥有最好的模型。  
**[01:11:19] Speaker A:** But at some point that reaches the biggest scale that it can reach. And then in equilibrium, we have algorithmic improvements, but we're spending roughly the same amount to train the next model as we spend to train the current model.  
但在某个时刻,规模会达到它能达到的最大值。然后在均衡状态下,我们仍有算法改进,但训练下一个模型的花费大致和训练当前模型的花费相当。  
**[01:11:37] Speaker B:** At some point you run out of money in the economy.  
到某个时刻,经济体中的资金就会耗尽。  
**[01:11:37] Speaker A:** A fixed lump of labor fallacy… The economy is going to grow, right? That's one of your predictions. We're going to have the data centers in space.  
固定劳动总量谬误啊……经济会增长的,对吧?这是你的预测之一。我们会在太空建数据中心。  
**[01:11:44] Speaker B:** Yes, but this is another example of the theme I was talking about.  
是的,但这又是我刚才讨论的主题的一个例子。  
**[01:11:47] Speaker A:** The economy will grow much faster with AI than I think it ever has before. Right now the compute is growing 3x a year.  
有了AI,经济增长速度会比以往任何时候都快得多。现在算力每年增长3倍。  
**[01:11:59] Speaker A:** I don't believe the economy is gonna grow 300% a year.  
我不认为经济会每年增长300%。  
**[01:12:03] Speaker A:** I said this in "Machines of Loving Grace", I think we may get 10-20% per year growth in the economy, but we're not gonna get 300% growth in the economy.  
我在《Machines of Loving Grace》中说过,我认为经济可能实现每年10-20%的增长,但不会达到300%的增长。  
**[01:12:13] Speaker A:** So I think in the end, if compute becomes the majority of what the economy produces, it's gonna be capped by that.  
所以我认为最终,如果算力成为经济产出的主要部分,它就会受到这个上限的制约。  
**[01:12:18] Speaker B:** So let's assume a model where compute stays capped.  
那我们假设一个算力保持上限的模型。  
**[01:12:22] Speaker B:** The world where frontier labs are making money is one where they continue to make fast progress. Because fundamentally your margin is limited by how good the alternative is.  
前沿实验室能赚钱的世界,是它们持续快速进步的世界。因为从根本上说,你的利润空间受限于替代方案有多好。  
**[01:12:34] Speaker B:** So you are able to make money because you have a frontier model. If you didn't have a frontier model you wouldn't be making money.  
所以你能赚钱是因为你有前沿模型。如果你没有前沿模型,你就赚不到钱。  
**[01:12:39] Speaker B:** So this model requires there never to be a steady state. Forever and ever you keep making more algorithmic progress.  
所以这个模型要求永远不会有稳态。你要永远不断地取得更多算法进步。  
**[01:12:45] Speaker A:** I don't think that's true. I mean, I feel like we're in an economics class.  
我不认为是这样。我是说,我感觉我们像在上经济学课。  
**[01:12:51] Speaker A:** Do you know the Tyler Cowen quote?  
你知道Tyler Cowen的那句话吗?  
**[01:12:59] Speaker B:** We never stop talking about economics.  
我们永远在谈论经济学。  
**[01:12:59] Speaker A:** We never stop talking about economics.  
我们永远在谈论经济学。  
**[01:13:03] Speaker A:** So no, I don't think this field's going to be a monopoly.  
所以不,我不认为这个领域会成为垄断。  
**[01:13:12] Speaker A:** All my lawyers never want me to say the word "monopoly".  
我所有的律师都不想让我说「垄断」这个词。  
**[01:13:15] Speaker A:** But I don't think this field's going to be a monopoly.  
但我不认为这个领域会成为垄断。  
**[01:13:17] Speaker A:** You do get industries in which there are a small number of players. Not one, but a small number of players.  
确实有些行业只有少数玩家。不是一个,而是少数几个玩家。  
**[01:13:21] Speaker A:** Ordinarily, the way you get monopolies like Facebook or Meta—I always call them Facebook—is these kinds of network effects.  
通常来说,像Facebook或Meta——我总是叫它们Facebook——这样的垄断形成方式是通过网络效应。  
**[01:13:37] Speaker A:** The way you get industries in which there are a small number of players is very high costs of entry.  
一个行业之所以只有少数几个参与者,是因为进入成本非常高。  
**[01:13:41] Speaker A:** Cloud is like this. I think cloud is a good example of this. There are three, maybe four players within cloud. I think that's the same for AI, three, maybe four.  
云计算就是这样。我认为云计算是一个很好的例子。云计算领域有三家,也许四家参与者。我认为 AI 也一样,三家,也许四家。  
**[01:13:56] Speaker A:** The reason is that it's so expensive. It requires so much expertise and so much capital to run a cloud company. You have to put up all this capital.  
原因是成本太高了。运营一家云计算公司需要大量的专业知识和资本。你必须投入所有这些资本。  
**[01:14:08] Speaker A:** In addition to putting up all this capital, you have to get all of this other stuff that requires a lot of skill to make it happen.  
除了投入所有这些资本之外,你还需要掌握许多其他需要大量技能才能实现的东西。  
**[01:14:11] Speaker A:** So if you go to someone and you're like, 'I want to disrupt this industry, here's $100 billion.' You're like, 'Okay, I'm putting in $100 billion and also betting that you can do all these other things that these people have been doing.'  
所以如果你去找某个人说「我想颠覆这个行业,这里有 1000 亿美元」。你会想「好吧,我投入 1000 亿美元,同时还要赌你能做到这些人一直在做的所有其他事情」。  
**[01:14:26] Speaker A:** Only to decrease the profit. The effect of your entering is that profit margins go down.  
结果只是降低了利润。你进入市场的效果就是利润率下降。  
**[01:14:29] Speaker A:** So we have equilibria like this all the time in the economy where we have a few players. Profits are not astronomical. Margins are not astronomical, but they're not zero.  
所以在经济中我们经常看到这样的均衡,只有少数几个参与者。利润不是天文数字,利润率也不是天文数字,但也不是零。  
**[01:14:39] Speaker A:** That's what we see on cloud. Cloud is very undifferentiated. Models are more differentiated than cloud.  
这就是我们在云计算上看到的。云计算产品差异化程度很低。模型的差异化程度比云计算高。  
**[01:14:51] Speaker A:** Everyone knows Claude is good at different things than GPT is good at, than Gemini is good at.  
每个人都知道 Claude 擅长的事情与 GPT 擅长的不同,与 Gemini 擅长的也不同。  
**[01:14:58] Speaker A:** It's not just that Claude's good at coding, GPT is good at math and reasoning.  
不仅仅是 Claude 擅长编程、GPT 擅长数学和推理这么简单。  
**[01:15:05] Speaker A:** It's more subtle than that. Models are good at different types of coding. Models have different styles. I think these things are actually quite different from each other, and so I would expect more differentiation than you see in cloud.  
比这更微妙。模型擅长不同类型的编程。模型有不同的风格。我认为这些东西实际上彼此差异很大,所以我预期会有比云计算更高的差异化。  
**[01:15:15] Speaker A:** Now, there actually is one counter-argument.  
不过,确实有一个反驳论点。  
**[01:15:26] Speaker A:** That counter-argument is if the process of producing models, if AI models can do that themselves, then that could spread throughout the economy.  
这个反驳论点是,如果生产模型的过程,如果 AI 模型自己能做到这一点,那么这种能力可能会扩散到整个经济中。  
**[01:15:37] Speaker A:** But that is not an argument for commoditizing AI models in general.  
但这并不是一个让 AI 模型普遍商品化的论据。  
**[01:15:41] Speaker A:** That's kind of an argument for commoditizing the whole economy at once.  
这更像是一个让整个经济同时商品化的论据。  
**[01:15:45] Speaker A:** I don't know what quite happens in that world where basically anyone can do anything, anyone can build anything, and there's no moat around anything at all.  
我不太清楚在那个世界会发生什么,在那里基本上任何人都能做任何事,任何人都能构建任何东西,而且任何东西周围都没有护城河。  
**[01:15:53] Speaker A:** I don't know, maybe we want that world. Maybe that's the end state here.  
我不知道,也许我们想要那样的世界。也许那就是最终状态。  
**[01:15:58] Speaker A:** Maybe when AI models can do everything, if we've solved all the safety and security problems, that's one of the mechanisms for the economy just flattening itself again.  
也许当 AI 模型能做所有事情时,如果我们已经解决了所有安全和保障问题,那就是经济再次自我扁平化的机制之一。  
**[01:16:09] Speaker A:** But that's kind of far post-'country of geniuses in the data center.'  
但那是在「数据中心里的天才之国」之后很远的事了。  
**[01:16:17] Speaker B:** Maybe a finer way to put that potential point is: 1) it seems like AI research is especially loaded on raw intellectual power, which will be especially abundant in the world of AGI.  
也许更精确地表达这个潜在观点是:1) AI 研究似乎特别依赖原始智力,而这在 AGI 的世界里将特别丰富。  
**[01:16:32] Speaker B:** And 2) if you just look at the world today, there are very few technologies that seem to be diffusing as fast as AI algorithmic progress. So that does hint that this industry is sort of structurally diffusive.  
2) 如果你只看今天的世界,很少有技术的扩散速度能像 AI 算法进步那样快。所以这确实暗示这个行业在结构上就具有扩散性。  
**[01:16:50] Speaker A:** I think coding is going fast, but I think AI research is a superset of coding and there are aspects of it that are not going fast.  
我认为编程进展很快,但我认为 AI 研究是编程的超集,它有些方面进展并不快。  
**[01:17:00] Speaker A:** But I do think, again, once we get coding, once we get AI models going fast, then that will speed up the ability of AI models to do everything else.  
但我确实认为,再说一次,一旦我们掌握了编程,一旦 AI 模型进展快速,那将加快 AI 模型做其他所有事情的能力。  
**[01:17:07] Speaker A:** So while coding is going fast now, I think once the AI models are building the next AI models and building everything else, the whole economy will kind of go at the same pace.  
所以虽然现在编程进展很快,但我认为一旦 AI 模型在构建下一代 AI 模型并构建其他所有东西,整个经济都会以相同的速度发展。  
**[01:17:17] Speaker A:** I am worried geographically, though. I'm a little worried that just proximity to AI, having heard about AI, may be one differentiator.  
不过我从地理角度有些担忧。我有点担心仅仅是接近 AI、听说过 AI,可能就会成为一个差异化因素。  
**[01:17:34] Speaker A:** So when I said the 10-20% growth rate, a worry I have is that the growth rate could be like 50% in Silicon Valley and parts of the world that are socially connected to Silicon Valley, and not that much faster than its current pace elsewhere.  
所以当我说到10-20%的增长率时,我担心的是增长率可能在硅谷以及与硅谷有社会联系的世界其他地区达到50%,而在其他地方并不会比目前的速度快多少。  
**[01:17:50] Speaker A:** I think that'd be a pretty messed up world. So one of the things I think about a lot is how to prevent that.  
我觉得那会是一个相当糟糕的世界。所以我经常思考的一件事就是如何防止这种情况发生。  
**[01:17:57] Speaker B:** Do you think that once we have this country of geniuses in a data center, that robotics is sort of quickly solved afterwards? Because it seems like a big problem with robotics is that a human can learn how to teleoperate current hardware, but current AI models can't, at least not in a way that's super productive.  
你认为一旦我们在数据中心拥有这个天才之国,机器人技术会很快被解决吗?因为机器人技术的一个大问题似乎是人类可以学会远程操作现有硬件,但目前的AI模型做不到,至少不能以一种超高效的方式做到。  
**[01:18:12] Speaker B:** And so if we have this ability to learn like a human, shouldn't it solve robotics immediately as well?  
所以如果我们拥有像人类一样学习的能力,难道不应该也能立即解决机器人技术吗?  
**[01:18:19] Speaker A:** I don't think it's dependent on learning like a human. It could happen in different ways.  
我不认为这取决于像人类那样学习。它可以通过不同的方式实现。  
**[01:18:21] Speaker A:** Again, we could have trained the model on many different video games, which are like robotic controls, or many different simulated robotics environments, or just train them to control computer screens, and they learn to generalize.  
再说一次,我们可以在许多不同的视频游戏上训练模型,这些游戏就像机器人控制一样,或者在许多不同的模拟机器人环境中训练,或者只是训练它们控制计算机屏幕,然后它们学会泛化。  
**[01:18:34] Speaker A:** So it will happen... it's not necessarily dependent on human-like learning. Human-like learning is one way it could happen.  
所以这会实现的……它不一定依赖于类人的学习方式。类人学习只是实现它的一种方式。  
**[01:18:41] Speaker A:** If the model's like, "Oh, I pick up a robot, I don't know how to use it, I learn," that could happen because we discovered continual learning.  
如果模型能够说「哦,我拿起一个机器人,我不知道怎么用,然后我学会了」,那可能是因为我们发现了持续学习。  
**[01:18:50] Speaker A:** That could also happen because we trained the model on a bunch of environments and then generalized, or it could happen because the model learns that in the context length.  
这也可能是因为我们在大量环境中训练模型然后它泛化了,或者可能是因为模型在上下文窗口中学会了这一点。  
**[01:18:58] Speaker A:** It doesn't actually matter which way. If we go back to the discussion we had an hour ago, that type of thing can happen in several different ways.  
实际上用哪种方式并不重要。如果我们回顾一个小时前的讨论,那种事情可以通过几种不同的方式实现。  
**[01:19:10] Speaker A:** But I do think when for whatever reason the models have those skills, then robotics will be revolutionized—both the design of robots, because the models will be much better than humans at that, and also the ability to control robots.  
但我确实认为,无论出于什么原因,当模型拥有这些技能时,机器人技术就会发生革命——无论是机器人的设计,因为模型在这方面会比人类强得多,还是控制机器人的能力。  
**[01:19:28] Speaker A:** So we'll get better at building the physical hardware, building the physical robots, and we'll also get better at controlling it.  
所以我们会更擅长制造物理硬件、制造实体机器人,同时也会更擅长控制它。  
**[01:19:32] Speaker A:** Now, does that mean the robotics industry will also be generating trillions of dollars of revenue? My answer there is yes, but there will be the same extremely fast, but not infinitely fast diffusion.  
那么,这是否意味着机器人行业也会产生数万亿美元的收入呢?我的答案是肯定的,但会有同样的扩散速度——极快但不是无限快。  
**[01:19:40] Speaker A:** So will robotics be revolutionized? Yeah, maybe tack on another year or two. That's the way I think about these things.  
所以机器人技术会被彻底改变吗?会的,可能再加上一两年时间。这就是我思考这些事情的方式。  
**[01:19:52] Speaker B:** Makes sense. There's a general skepticism about extremely fast progress. Here's my view. It sounds like you are going to solve continual learning one way or another within a matter of years.  
有道理。人们普遍对极快的进展持怀疑态度。我的看法是这样的。听起来你们无论如何都会在几年内解决持续学习问题。  
**[01:20:02] Speaker B:** But just as people weren't talking about continual learning a couple of years ago, and then we realized, "Oh, why aren't these models as useful as they could be right now, even though they are clearly passing the Turing test and are experts in so many different domains? Maybe it's this thing."  
但就像几年前人们还没有讨论持续学习,然后我们意识到「哦,为什么这些模型现在还没有达到它们应有的有用程度,尽管它们明显通过了图灵测试并且在很多不同领域都是专家?也许问题就在这里。」  
**[01:20:14] Speaker B:** Then we solve this thing and we realize, actually, there's another thing that human intelligence can do that's a basis of human labor that these models can't do.  
然后我们解决了这个问题,又意识到,实际上还有另一个人类智能可以做的、作为人类劳动基础的东西,而这些模型做不到。  
**[01:20:24] Speaker B:** So why not think there will be more things like this, where we've found more pieces of human intelligence?  
那么为什么不认为会有更多这样的情况,我们会发现人类智能的更多组成部分呢?  
**[01:20:28] Speaker A:** Well, to be clear, I think continual learning, as I've said before, might not be a barrier at all. I think we may just get there by pre-training generalization and RL generalization.  
嗯,要说清楚的是,我认为持续学习,正如我之前所说的,可能根本不是一个障碍。我认为我们可能只需通过预训练泛化和强化学习泛化就能达到目标。  
**[01:20:40] Speaker A:** I think there just might not be such a thing at all. In fact, I would point to the history  
我认为可能根本就不存在这样的东西。事实上,我会指向历史  
**[01:20:51] Speaker A:** In ML, of people coming up with things that are barriers that end up kind of dissolving within the big blob of compute. People talked about, "How do your models keep track of nouns and verbs?" "They can understand syntactically, but they can't understand semantically? It's only statistical correlations."  
在机器学习领域,人们总是提出各种障碍,但这些障碍最终都会在庞大的算力面前消解。人们曾质疑:「你的模型怎么追踪名词和动词?」「它们能理解句法,但不能理解语义?这只是统计相关性而已。」  
**[01:21:16] Speaker A:** "You can understand a paragraph, you can't understand a word. There's reasoning, you can't do reasoning." But then suddenly it turns out you can do code and math very well.  
「你能理解段落,但不能理解单词。推理方面,你做不了推理。」但突然之间,结果证明模型可以很好地处理代码和数学。  
**[01:21:23] Speaker A:** So I think there's actually a stronger history of some of these things seeming like a big deal and then kind of dissolving. Some of them are real. The need for data is real, maybe continual learning is a real thing.  
所以我认为,这些问题看起来很重大但最终消解的历史其实更为显著。当然有些问题是真实存在的。对数据的需求是真实的,持续学习可能也是一个真实的问题。  
**[01:21:42] Speaker A:** But again, I would ground us in something like code. I think we may get to the point in a year or two where the models can just do SWE end-to-end.  
但话说回来,我会把我们拉回到代码这样的具体事物上。我认为在一两年内,我们可能会达到这样的程度:模型可以端到端地完成软件工程工作。  
**[01:21:50] Speaker A:** That's a whole task. That's a whole sphere of human activity that we're just saying models can do now.  
这是一整个任务。这是人类活动的整个领域,而我们现在说模型可以做到了。  
**[01:21:56] Speaker B:** When you say end-to-end, do you mean setting technical direction, understanding the context of the problem, et cetera?  
当你说端到端时,你是指设定技术方向、理解问题的上下文等等这些吗?  
**[01:22:06] Speaker A:** Yes. I mean all of that.  
是的。我指的就是所有这些。  
**[01:22:06] Speaker B:** Interesting. I feel like that is AGI-complete, which maybe is internally consistent. But it's not like saying 90% of code or 100% of code.  
有意思。我觉得那就是AGI完备的程度了,这可能在内部逻辑上是一致的。但这不同于说完成90%的代码或100%的代码。  
**[01:22:17] Speaker A:** No, I gave this spectrum: 90% of code, 100% of code, 90% of end-to-end SWE, 100% of end-to-end SWE. New tasks are created for SWEs. Eventually those get done as well.  
不,我给出了这样一个谱系:90%的代码、100%的代码、90%的端到端软件工程、100%的端到端软件工程。会为软件工程师创造新的任务。最终这些任务也会被完成。  
**[01:22:31] Speaker A:** It's a long spectrum there, but we're traversing the spectrum very quickly.  
这是一个很长的谱系,但我们正在非常快速地穿越这个谱系。  
**[01:22:35] Speaker B:** I do think it's funny that I've seen a couple of podcasts you've done where the hosts will be like, "But Dwarkesh wrote the essay about the continuous learning thing." It always makes me crack up because you've been an AI researcher for 10 years.  
我确实觉得挺有意思的,我看过你做的几期播客,主持人会说「但是Dwarkesh写了关于持续学习的那篇文章。」这总是让我觉得很好笑,因为你已经做了10年的AI研究员了。  
**[01:22:48] Speaker B:** I'm sure there's some feeling of, "Okay, so a podcaster wrote an essay, and every interview I get asked about it."  
我相信你肯定有这样的感觉:「好吧,一个播客主持人写了篇文章,然后我每次采访都被问到这个。」  
**[01:22:53] Speaker A:** The truth of the matter is that we're all trying to figure this out together. There are some ways in which I'm able to see things that others aren't.  
事实是我们都在一起努力弄清楚这些事情。在某些方面,我能看到别人看不到的东西。  
**[01:23:04] Speaker A:** These days that probably has more to do with seeing a bunch of stuff within Anthropic and having to make a bunch of decisions than I have any great research insight that others don't.  
如今这可能更多是因为我在Anthropic内部看到很多东西并且需要做很多决策,而不是说我有什么别人没有的重大研究洞见。  
**[01:23:13] Speaker A:** I'm running a 2,500 person company. It's actually pretty hard for me to have concrete research insight, much harder than it would have been 10 years ago or even two or three years ago.  
我在管理一个2500人的公司。对我来说,要有具体的研究洞见其实很难,比10年前甚至两三年前要难得多。  
**[01:23:27] Speaker B:** As we go towards a world of a full drop-in remote worker replacement, does an API pricing model still make the most sense? If not, what is the correct way to price AGI, or serve AGI?  
当我们走向一个完全替代远程工作者的世界时,API定价模式还是最合理的吗?如果不是,那么为AGI定价或提供AGI服务的正确方式是什么?  
**[01:23:45] Speaker A:** I think there's going to be a bunch of different business models here, all at once, that are going to be experimented with. I actually do think that the API model is more durable than many people think.  
我认为这里会同时出现很多不同的商业模式,都会被尝试。我确实认为API模式比很多人想的更持久。  
**[01:23:59] Speaker A:** One way I think about it is if the technology is advancing quickly, if it's advancing exponentially, what that means is there's always a surface area of new use cases that have been developed in the last three months.  
我思考这个问题的一个角度是,如果技术在快速发展,如果是指数级发展,那就意味着在过去三个月里总是会有新的用例表面不断被开发出来。  
**[01:24:20] Speaker A:** Any kind of product surface you put in place is always at risk of sort of becoming irrelevant. Any given product surface probably makes sense for a range of capabilities of the model.  
你设置的任何一种产品界面都总是面临着变得不再相关的风险。任何特定的产品界面可能只对模型的某个能力范围有意义。  
**[01:24:32] Speaker A:** The chatbot is already running into limitations where making it smarter doesn't really help the average consumer that much. But I don't think that's a limitation of AI models. I don't think that's evidence that the models are good enough and them getting better doesn't matter to the economy.  
聊天机器人已经遇到了一些局限性,让它变得更聪明对普通消费者其实帮助不大。但我不认为这是AI模型本身的局限。我也不认为这能证明模型已经足够好了,或者说模型继续进步对经济无关紧要。  
**[01:24:51] Speaker A:** It doesn't matter to that particular product. So I think the value of the API is that the API always offers an opportunity, very close to the bare metal, to build on what the latest thing is.  
这只是对那个特定产品不重要而已。所以我认为API的价值在于,API总能提供一个机会,非常接近底层,让你基于最新的技术去构建。  
**[01:25:06] Speaker A:** There's always going to be this front of new startups and new ideas that weren't possible a few months ago and are possible because the model is advancing.  
总会有一波新的创业公司和新想法涌现,这些在几个月前还不可能实现,但因为模型的进步而成为可能。  
**[01:25:19] Speaker A:** I actually predict that it's going to exist alongside other models, but we're always going to have the API business model because there's always going to be a need for a thousand different people to try experimenting with the model in a different way.  
我其实预测它会和其他模型并存,但我们始终会保留API商业模式,因为总会有成千上万的人需要用不同的方式来尝试和实验这个模型。  
**[01:25:34] Speaker A:** 100 of them become startups and ten of them become big successful startups. Two or three really end up being the way that people use the model of a given generation.  
其中100个会成为创业公司,10个会成为大型成功的创业公司。最终真正有两三个会成为人们使用某一代模型的主要方式。  
**[01:25:45] Speaker A:** So I basically think it's always going to exist. At the same time, I'm sure there's going to be other models as well.  
所以我基本上认为API会一直存在。与此同时,我也确信会有其他模式出现。  
**[01:25:55] Speaker A:** Not every token that's output by the model is worth the same amount.  
模型输出的每个token的价值并不相同。  
**[01:26:00] Speaker A:** Think about what is the value of the tokens that the model outputs when someone calls them up and says, "My Mac isn't working," or something, the model's like, "restart it."  
想想看,当有人打电话问「我的Mac不能用了」之类的问题,模型回答「重启一下」,这时模型输出的token价值是多少。  
**[01:26:16] Speaker A:** Someone hasn't heard that before, but the model said that 10 million times. Maybe that's worth like a dollar or a few cents or something.  
也许对某个人来说这是第一次听到这个建议,但模型已经说过1000万次了。也许这值一美元或几美分吧。  
**[01:26:26] Speaker A:** Whereas if the model goes to one of the pharmaceutical companies and it says, "Oh, you know, this molecule you're developing, you should take the aromatic ring from that end of the molecule and put it on that end of the molecule. If you do that, wonderful things will happen."  
而如果模型对某家制药公司说:「你们正在开发的这个分子,应该把芳香环从分子的这一端移到那一端。如果这样做的话,会产生奇妙的效果。」  
**[01:26:46] Speaker A:** Those tokens could be worth tens of millions of dollars.  
那些token可能价值数千万美元。  
**[01:26:52] Speaker A:** So I think we're definitely going to see business models that recognize that.  
所以我认为我们肯定会看到能识别这种差异的商业模式。  
**[01:26:56] Speaker A:** At some point we're going to see "pay for results" in some form, or we may see forms of compensation that are like labor, that kind of work by the hour.  
在某个时候我们会看到某种形式的「按结果付费」,或者我们可能会看到类似劳动力的补偿形式,那种按小时计费的方式。  
**[01:27:06] Speaker A:** I don't know. I think because it's a new industry, a lot of things are going to be tried. I don't know what will turn out to be the right thing.  
我不知道。我觉得因为这是一个新兴行业,会尝试很多不同的东西。我也不知道最终什么会是正确的方式。  
**[01:27:19] Speaker B:** I take your point that people will have to try things to figure out what is the best way to use this blob of intelligence.  
我理解你的观点,人们需要不断尝试才能找出使用这团智能的最佳方式。  
**[01:27:24] Speaker B:** But what I find striking is Claude Code. I don't think in the history of startups there has been a single application that has been as hotly competed in as coding agents.  
但让我感到惊讶的是Claude Code。我认为在创业公司历史上,没有哪个单一应用像代码智能体这样竞争如此激烈。  
**[01:27:42] Speaker B:** Claude Code is a category leader here. That seems surprising to me. It doesn't seem intrinsically that Anthropic had to build this.  
Claude Code是这个领域的领导者。这让我感到意外。从本质上看,并不是Anthropic必须要做这个。  
**[01:27:49] Speaker B:** I wonder if you have an accounting of why it had to be Anthropic or how Anthropic ended up building an application in addition to the model underlying it that was successful.  
我想知道你能否解释一下为什么必须是Anthropic,或者Anthropic是如何在底层模型之外又成功构建了一个应用的。  
**[01:27:58] Speaker A:** So it actually happened in a pretty simple way, which is that we had our own coding models, which were good at coding.  
其实发生的过程很简单,就是我们有自己的代码模型,这些模型很擅长编程。  
**[01:28:09] Speaker A:** Around the beginning of 2025, I said, "I think the time has come where you can have nontrivial acceleration of your own research if you're an AI company by using these models."  
在2025年初左右,我说:「我认为时机已经成熟,如果你是一家AI公司,使用这些模型可以显著加速你自己的研究。」  
**[01:28:21] Speaker A:** Of course, you need an interface, you need a harness to use them. So I encouraged people internally. I didn't say this is one thing that you have to use.  
当然,你需要一个界面,需要一个工具来使用它们。所以我在内部鼓励大家。我没有说这是你们必须使用的东西。  
**[01:28:31] Speaker A:** I just said people should experiment with this. I think it might have been originally called Claude CLI, and then the name eventually got changed to Claude Code.  
我刚才说过大家应该试试这个东西。我记得它最初好像叫 Claude CLI,后来名字改成了 Claude Code。  
**[01:28:42] Speaker A:** Internally, it was the thing that everyone was using and it was seeing fast internal adoption.  
在公司内部,这是每个人都在用的工具,而且内部采用速度非常快。  
**[01:28:48] Speaker A:** I looked at it and I said, "Probably we should launch this externally, right?"  
我看到这个情况就说:「我们或许应该把它对外发布,对吧?」  
**[01:28:53] Speaker A:** It's seen such fast adoption within Anthropic. Coding is a lot of what we do.  
它在 Anthropic 内部的采用速度如此之快。编程占了我们工作的很大一部分。  
**[01:28:59] Speaker A:** We have an audience of many, many hundreds of people that's in some ways at least representative of the external audience. So it looks like we already have product-market fit.  
我们内部有几百人的用户群体,这在某种程度上至少能代表外部受众。所以看起来我们已经有了产品市场契合度。  
**[01:29:08] Speaker A:** Let's launch this thing. And then we launched it. I think just the fact that we ourselves are kind of developing the model and we ourselves know what we most need to use the model,  
那就发布吧。然后我们就发布了。我觉得正是因为我们自己在开发这个模型,我们自己最清楚需要用模型来做什么,  
**[01:29:21] Speaker A:** I think it's kind of creating this feedback loop.  
我觉得这形成了一种反馈循环。  
**[01:29:21] Speaker B:** I see. In the sense that you, let's say a developer at Anthropic is like, "Ah, it would be better if it was better at this X thing."  
我明白了。也就是说,比如 Anthropic 的一个开发者会想:「啊,要是它在 X 这方面做得更好就好了。」  
**[01:29:31] Speaker B:** Then you bake that into the next model that you build.  
然后你们就把这个需求融入到下一个版本的模型中。  
**[01:29:35] Speaker A:** That's one version of it, but then there's just the ordinary product iteration.  
这是一个方面,但同时还有常规的产品迭代。  
**[01:29:41] Speaker A:** We have a bunch of coders within Anthropic, they use Claude Code every day and so we get fast feedback.  
我们在 Anthropic 内部有一批程序员,他们每天都用 Claude Code,所以我们能快速获得反馈。  
**[01:29:47] Speaker A:** That was more important in the early days.  
这在早期阶段尤其重要。  
**[01:29:50] Speaker A:** Now, of course, there are millions of people using it, and so we get a bunch of external feedback as well.  
现在当然有数百万人在使用它,所以我们也收到了大量外部反馈。  
**[01:29:53] Speaker A:** But it's just great to be able to get kind of fast internal feedback.  
但能够快速获得内部反馈这一点真的很棒。  
**[01:29:58] Speaker A:** I think this is the reason why we launched a coding model and didn't launch a pharmaceutical company.  
我觉得这就是为什么我们推出了编程模型而不是去创办制药公司的原因。  
**[01:30:10] Speaker A:** My background's in biology, but we don't have any of the resources that are needed to launch a pharmaceutical company.  
我的背景是生物学,但我们没有创办制药公司所需的任何资源。  
**[01:31:24] Speaker B:** Let me now ask you about making AI go well.  
现在让我来问问关于如何让 AI 朝好的方向发展的问题。  
**[01:31:24] Speaker B:** It seems like whatever vision we have about how AI goes well has to be compatible with two things:  
看起来,无论我们对 AI 如何良好发展有什么愿景,都必须与两件事相容:  
**[01:31:30] Speaker B:** 1) the ability to build and run AIs is diffusing extremely rapidly and 2) the population of AIs, the amount we have and their intelligence, will also increase very rapidly.  
第一,构建和运行 AI 的能力正在极快速地扩散;第二,AI 的数量、我们拥有的 AI 以及它们的智能水平,也都会非常快速地增长。  
**[01:31:44] Speaker B:** That means that lots of people will be able to build huge populations of misaligned AIs, or AIs which are just companies which are trying to increase their footprint or have weird psyches like Sydney Bing, but now they're superhuman.  
这意味着很多人将能够构建大量未对齐的 AI,或者那些只是作为公司试图扩大影响力的 AI,或者有着像 Sydney Bing 那样奇怪心理的 AI,但现在它们是超人类水平的。  
**[01:31:57] Speaker B:** What is a vision for a world in which we have an equilibrium that is compatible with lots of different AIs, some of which are misaligned, running around?  
在一个有许多不同的 AI、其中一些是未对齐的 AI 到处运行的世界中,什么样的愿景能让我们达成一个与之相容的平衡?  
**[01:32:06] Speaker A:** I think in "The Adolescence of Technology", I was skeptical of the balance of power.  
我记得在「技术的青春期」那篇文章中,我对权力平衡持怀疑态度。  
**[01:32:13] Speaker A:** But the thing I was specifically skeptical of is you have three or four of these companies all building models that are derived from the same thing, that they would check each other.  
但我具体怀疑的是,你有三四家公司都在构建源自同一技术的模型,它们会相互制衡。  
**[01:32:36] Speaker A:** Or even that any number of them would check each other.  
或者说,甚至任何数量的它们会相互制衡。  
**[01:32:40] Speaker A:** We might live in an offense-dominant world where one person or one AI model is smart enough to do something that causes damage for everything else.  
我们可能生活在一个进攻占主导的世界里,一个人或一个 AI 模型足够聪明,能做出对其他所有东西造成损害的事情。  
**[01:32:47] Speaker A:** In the short run, we have a limited number of players now.  
在短期内,我们现在的参与者数量是有限的。  
**[01:32:54] Speaker A:** So we can start within the limited number of players.  
所以我们可以从这有限数量的参与者入手。  
**[01:32:56] Speaker A:** We need to put in place the safeguards.  
我们需要建立安全保障措施。  
**[01:33:03] Speaker A:** We need to make sure everyone does the right alignment work.  
我们需要确保每个人都做好对齐工作。  
**[01:33:05] Speaker A:** We need to make sure everyone has bioclassifiers. Those are the immediate things we need to do.  
我们需要确保每个人都有生物分类器。这些是我们需要立即做的事情。  
**[01:33:11] Speaker A:** I agree that that doesn't solve the problem in the long run, particularly if the ability of  
我同意这在长期来看并不能解决问题,特别是如果  
**[01:33:16] Speaker A:** AI models to make other AI models proliferate, then the whole thing can become harder to solve.  
如果用 AI 模型来制造其他 AI 模型的情况扩散开来,那么整个问题就会变得更难解决。  
**[01:33:26] Speaker A:** I think in the long run we need some architecture of governance that preserves human freedom, but also allows us to govern a very large number of human systems, AI systems, hybrid human-AI companies or economic units.  
我认为从长远来看,我们需要某种治理架构,既能保护人类自由,又能让我们管理数量庞大的人类系统、AI 系统、以及人机混合的公司或经济单元。  
**[01:33:52] Speaker A:** So we're gonna need to think about: how do we protect the world against bioterrorism? How do we protect the world against mirror life?  
所以我们需要思考:如何保护世界免受生物恐怖主义的威胁?如何保护世界免受镜像生命的威胁?  
**[01:34:11] Speaker A:** Probably we're gonna need some kind of AI monitoring system that monitors for all of these things. But then we need to build this in a way that preserves civil liberties and our constitutional rights.  
我们可能需要某种 AI 监控系统来监测所有这些威胁。但同时我们需要以一种能保护公民自由和宪法权利的方式来构建这个系统。  
**[01:34:24] Speaker A:** So I think just as anything else, it's a new security landscape with a new set of tools and a new set of vulnerabilities.  
所以我认为,就像其他事物一样,这是一个全新的安全格局,有一套新的工具和一套新的漏洞。  
**[01:34:34] Speaker A:** My worry is, if we had 100 years for this to happen all very slowly, we'd get used to it. We've gotten used to the presence of explosives in society or the presence of various new weapons or the presence of video cameras.  
我担心的是,如果这一切能在 100 年内非常缓慢地发生,我们会逐渐适应。我们已经适应了社会中炸药的存在、各种新武器的存在、或者监控摄像头的存在。  
**[01:34:58] Speaker A:** We would get used to it over 100 years and we'd develop governance mechanisms. We'd make our mistakes. My worry is just that this is happening all so fast.  
在 100 年的时间里我们会逐渐适应,并发展出治理机制。我们会犯错,然后吸取教训。我担心的只是这一切发生得太快了。  
**[01:35:03] Speaker A:** So maybe we need to do our thinking faster about how to make these governance mechanisms work.  
所以也许我们需要更快地思考如何让这些治理机制运作起来。  
**[01:35:07] Speaker B:** It seems like in an offense-dominant world, over the course of the next century—the idea is that AI is making the progress that would happen over the next century happen in some period of five to ten years—we would still need the same mechanisms, or balance of power would be similarly intractable, even if humans were the only game in town.  
在一个进攻占优势的世界里,在接下来的一个世纪过程中——也就是说 AI 把原本需要一个世纪才能实现的进步压缩到五到十年内完成——即使只有人类在场,我们仍然需要同样的机制,或者说权力平衡仍然会同样棘手。  
**[01:35:29] Speaker B:** I guess we have the advice of AI. But it fundamentally doesn't seem like a totally different ball game here.  
我想我们可以听取 AI 的建议。但这从根本上看起来并不像是完全不同的游戏规则。  
**[01:35:36] Speaker B:** If checks and balances were going to work, they would work with humans as well. If they aren't going to work, they wouldn't work with AIs as well.  
如果制衡机制能够奏效,那么在人类身上就能奏效。如果不能奏效,在 AI 身上也不会奏效。  
**[01:35:41] Speaker B:** So maybe this just dooms human checks and balances as well.  
所以也许这也宣告了人类制衡机制的终结。  
**[01:35:47] Speaker A:** Again, I think there's some way to make this happen. The governments of the world may have to work together to make it happen.  
再说一次,我认为总有办法实现这一目标。世界各国政府可能必须合作才能实现。  
**[01:35:58] Speaker A:** We may have to talk to AIs about building societal structures in such a way that these defenses are possible. I don't know. I don't want to say this is so far ahead in time, but it's so far ahead in technological ability that may happen over a short period of time, that it's hard for us to anticipate it in advance.  
我们可能需要和 AI 讨论如何构建社会结构,使这些防御措施成为可能。我不知道。我不想说这在时间上有多遥远,但这在技术能力上确实遥遥领先,而且可能在短时间内发生,以至于我们很难提前预见。  
**[01:36:21] Speaker B:** Speaking of governments getting involved, on December 26, the Tennessee legislature introduced a bill which said, "It would be an offense for a person to knowingly train artificial intelligence to provide emotional support, including through open-ended conversations with a user."  
说到政府介入,12 月 26 日,Tennessee 州议会提出了一项法案,称「故意训练人工智能提供情感支持,包括通过与用户的开放式对话,将构成违法行为」。  
**[01:36:39] Speaker B:** Of course, one of the things that Claude attempts to do is be a thoughtful, knowledgeable friend.  
当然,Claude 试图做的事情之一就是成为一个体贴、博学的朋友。  
**[01:36:48] Speaker B:** In general, it seems like we're going to have this patchwork of state laws. A lot of the benefits that normal people could experience as a result of AI are going to be curtailed, especially when we get into the kinds of things you discuss in "Machines of Loving Grace": biological freedom, mental health improvements, et cetera.  
总的来说,我们似乎会面临一个州法律的拼凑局面。普通人本可以从 AI 中获得的许多好处将会被削减,尤其是当我们涉及到你在「Machines of Loving Grace」中讨论的那些内容时:生物自由、心理健康改善等等。  
**[01:37:02] Speaker B:** It seems easy to imagine worlds in which these get whac-a-moled away by different laws, whereas bills like this don't seem to address the actual existential threats that you're concerned about.  
很容易想象这样的世界:这些好处被各种不同的法律像打地鼠一样逐个消灭,而像这样的法案似乎并没有解决你所担心的真正的存在性威胁。  
**[01:37:15] Speaker A:** I'm curious to understand, in the context of things like this, Anthropic's position against the federal moratorium on state AI laws.  
我很好奇想了解一下,在这样的背景下,Anthropic 对联邦暂停各州 AI 法律这一立场的态度。  
**[01:37:20] Speaker B:** There are many different things going on at once. I think that particular law is dumb.  
有很多不同的事情同时在发生。我认为那个特定的法律很愚蠢。  
**[01:37:28] Speaker B:** It was clearly made by legislators who just probably had little idea what AI models could do and not do.  
很明显,制定这个法律的立法者可能对 AI 模型能做什么、不能做什么几乎没什么概念。  
**[01:37:38] Speaker B:** They're like, "AI models serving us, that just sounds scary. I don't want that to happen."  
他们就像是,「AI 模型为我们服务,听起来就很吓人。我不想让这种事发生。」  
**[01:37:41] Speaker B:** So we're not in favor of that. But that wasn't the thing that was being voted on.  
所以我们不支持那个法律。但那并不是正在被投票表决的东西。  
**[01:37:47] Speaker B:** The thing that was being voted on is: we're going to ban all state regulation of AI for 10 years with no apparent plan to do any federal regulation of AI, which would take Congress to pass, which is a very high bar.  
正在被投票表决的是:我们要禁止各州对 AI 进行任何监管长达 10 年,同时没有任何明确的联邦 AI 监管计划,而联邦监管需要国会通过,门槛非常高。  
**[01:38:05] Speaker B:** So the idea that we'd ban states from doing anything for 10 years… People said they had a plan for the federal government, but there was no actual proposal on the table.  
所以禁止各州做任何事情长达 10 年这个想法……有人说他们有联邦层面的计划,但实际上桌面上并没有任何具体提案。  
**[01:38:11] Speaker B:** There was no actual attempt. Given the serious dangers that I lay out in "Adolescence of Technology" around things like biological weapons and bioterrorism autonomy risk, and the timelines we've been talking about—10 years is an eternity—I think that's a crazy thing to do.  
根本没有实际的尝试。考虑到我在「Adolescence of Technology」中阐述的严重危险,比如生物武器和生物恐怖主义自主性风险,还有我们一直在讨论的时间线——10 年简直是一个永恒——我认为这样做太疯狂了。  
**[01:38:36] Speaker B:** So if that's the choice, if that's what you force us to choose, then we're going to choose not to have that moratorium.  
所以如果这就是选择,如果你们强迫我们做这个选择,那我们会选择不要那个暂停令。  
**[01:38:42] Speaker B:** I think the benefits of that position exceed the costs, but it's not a perfect position if that's the choice.  
我认为这个立场的好处大于代价,但如果这就是选择的话,它并不是一个完美的立场。  
**[01:38:47] Speaker B:** Now, I think the thing that we should do, the thing that I would support, is the federal government should step in, not saying "states you can't regulate", but "Here's what we're going to do, and states you can't differ from this."  
现在,我认为我们应该做的、我会支持的,是联邦政府应该介入,不是说「各州你们不能监管」,而是「这是我们要做的,各州不能偏离这个标准。」  
**[01:39:02] Speaker B:** I think preemption is fine in the sense of saying that the federal government says, "Here is our standard. This applies to everyone. States can't do something different."  
我认为联邦优先权是可以的,也就是联邦政府说,「这是我们的标准。适用于所有人。各州不能做不同的事情。」  
**[01:39:08] Speaker B:** That would be something I would support if it would be done in the right way.  
如果以正确的方式来做,这是我会支持的。  
**[01:39:12] Speaker B:** But this idea of states, "You can't do anything and we're not doing anything either," that struck us as very much not making sense.  
但这种对各州说「你们什么都不能做,而我们也什么都不做」的想法,在我们看来完全说不通。  
**[01:39:22] Speaker B:** I think it will not age well, it is already starting to not age well with all the backlash that you've seen.  
我认为这个立场经不起时间考验,从你看到的所有反弹来看,它已经开始显得过时了。  
**[01:39:29] Speaker B:** Now, in terms of what we would want, the things we've talked about are starting with transparency standards in order to monitor some of these autonomy risks and bioterrorism risks.  
现在,就我们想要什么而言,我们谈到的事情是从透明度标准开始,以便监测一些自主性风险和生物恐怖主义风险。  
**[01:39:46] Speaker B:** As the risks become more serious, as we get more evidence for them, then I think we could be more aggressive in some targeted ways and say, "Hey, AI bioterrorism is really a threat. Let's pass a law that forces people to have classifiers."  
随着风险变得更严重,随着我们获得更多证据,我认为我们可以在一些有针对性的方面更积极主动,比如说,「嘿,AI 生物恐怖主义真的是个威胁。我们来通过一项法律,强制人们使用分类器。」  
**[01:40:04] Speaker B:** I could even imagine… It depends. It depends how serious the threat it ends up being.  
我甚至可以想象……这取决于具体情况。取决于威胁最终有多严重。  
**[01:40:07] Speaker B:** We don't know for sure. We need to pursue this in an intellectually honest way where we say that ahead of time, the risk has not emerged yet.  
我们无法确定。我们需要以智识诚实的方式推进这件事,提前说明风险尚未出现。  
**[01:40:12] Speaker B:** But I could certainly imagine, with the pace that things are going at, a world where later this year we say, "Hey, this AI bioterrorism stuff is really serious. We should do something about it. We should put it in a federal standard.  
但我完全可以想象,按照目前的发展速度,在今年晚些时候我们会说,「嘿,这个 AI 生物恐怖主义的事情真的很严重。我们应该对此采取行动。我们应该把它纳入联邦标准。  
**[01:40:27] Speaker B:** If the federal government won't act, we should put it in a state standard." I could totally see that.  
如果联邦政府不行动,我们应该把它纳入州标准。」我完全能看到这种情况发生。  
**[01:40:31] Speaker A:** I'm concerned about a world where if you just consider the pace of progress you're expecting, the life cycle of legislation...  
我担心的是,如果你只考虑你所预期的进展速度,还有立法的生命周期……  
**[01:40:42] Speaker B:** The benefits are, as you say because of diffusion lag, slow enough that I really do think this patchwork of state  
好处是,正如你所说,由于扩散滞后,速度足够慢,所以我确实认为这种各州拼凑的  
**[01:40:55] Speaker A:** Laws, on the current trajectory, would prohibit. I mean, if having an emotional chatbot friend is something that freaks people out, then just imagine the kinds of actual benefits from AI we want normal people to be able to experience.  
按照目前的发展轨迹,法律会禁止这些。我的意思是,如果拥有一个情感聊天机器人朋友就让人们感到不安,那么想象一下我们希望普通人能够体验到的 AI 实际好处会是什么样的。  
**[01:41:03] Speaker A:** From improvements in health and healthspan and improvements in mental health and so forth.  
比如健康和健康寿命的改善,以及心理健康的改善等等。  
**[01:41:08] Speaker A:** Whereas at the same time, it seems like you think the dangers are already on the horizon and I just don't see that much... It seems like it would be especially injurious to the benefits of AI as compared to the dangers of AI.  
然而与此同时,你似乎认为危险已经近在眼前,而我并没有看到那么多……在我看来,这对 AI 的好处造成的伤害会特别大,相比于 AI 的危险而言。  
**[01:41:24] Speaker A:** So that's maybe where the cost-benefit makes less sense to me.  
所以这可能就是我觉得成本收益分析不太合理的地方。  
**[01:41:27] Speaker B:** So there's a few things here. People talk about there being thousands of these state laws.  
这里有几点需要说明。人们谈论说有成千上万条这样的州法律。  
**[01:41:31] Speaker B:** First of all, the vast, vast majority of them do not pass.  
首先,其中绝大多数都不会通过。  
**[01:41:34] Speaker B:** The world works a certain way in theory, but just because a law has been passed doesn't mean it's really enforced.  
理论上世界是以某种方式运作的,但法律通过并不意味着它真的会被执行。  
**[01:41:44] Speaker B:** The people implementing it may be like, 'Oh my God, this is stupid. It would mean shutting off everything that's ever been built in Tennessee.'  
执行这些法律的人可能会想:「天哪,这太蠢了。这意味着要关闭 Tennessee 州有史以来建造的所有东西。」  
**[01:41:55] Speaker B:** Very often, laws are interpreted in a way that makes them not as dangerous or harmful.  
很多时候,法律的解释方式会让它们不那么危险或有害。  
**[01:42:02] Speaker B:** On the same side, of course, you have to worry if you're passing a law to stop a bad thing; you have this problem as well.  
当然,从另一个角度来说,如果你通过一项法律来阻止坏事,你也会面临这个问题。  
**[01:42:06] Speaker B:** My basic view is that if we could decide what laws were passed and how things were done—and we're only one small input into that—I would deregulate a lot of the stuff around the health benefits of AI.  
我的基本观点是,如果我们能决定通过什么法律以及如何做事——而我们只是其中一个小小的输入——我会放松很多关于 AI 健康益处方面的监管。  
**[01:42:29] Speaker B:** I don't worry as much about the chatbot laws.  
我对聊天机器人相关法律并不那么担心。  
**[01:42:37] Speaker B:** I actually worry more about the drug approval process, where I think AI models are going to greatly accelerate the rate at which we discover drugs, and the pipeline will get jammed up.  
我实际上更担心药物审批流程,我认为 AI 模型将大大加快我们发现药物的速度,而审批流程会被堵塞。  
**[01:42:45] Speaker B:** The pipeline will not be prepared to process all the stuff that's going through it.  
这个流程不会做好准备来处理所有通过它的东西。  
**[01:42:50] Speaker B:** I think reform of the regulatory process should bias more towards the fact that we have a lot of things coming where the safety and efficacy is actually going to be really crisp and clear, a beautiful thing, and really effective.  
我认为监管流程的改革应该更多地倾向于这样一个事实:我们会有很多东西即将到来,它们的安全性和有效性将会非常清晰明确,是一件美好的事情,而且真的很有效。  
**[01:43:12] Speaker B:** Maybe we don't need all this superstructure around it that was designed around an era of drugs that barely work and often have serious side effects.  
也许我们不需要围绕它的所有这些上层结构,这些结构是为那个药物勉强有效且经常有严重副作用的时代设计的。  
**[01:43:21] Speaker B:** At the same time, I think we should be ramping up quite significantly the safety and security legislation.  
与此同时,我认为我们应该大幅加强安全和安保方面的立法。  
**[01:43:35] Speaker B:** Like I've said, starting with transparency is my view of trying not to hamper the industry, trying to find the right balance.  
正如我所说,从透明度开始是我试图不妨碍行业发展、试图找到正确平衡的观点。  
**[01:43:43] Speaker B:** I'm worried about it. Some people criticize my essay for saying, 'That's too slow. The dangers of AI will come too soon if we do that.'  
我对此感到担忧。一些人批评我的文章,说:「那太慢了。如果我们那样做,AI 的危险会来得太快。」  
**[01:43:50] Speaker B:** Well, basically, I think the last six months and maybe the next few months are going to be about transparency.  
基本上,我认为过去六个月和也许接下来的几个月将会是关于透明度的。  
**[01:43:58] Speaker B:** Then, if these risks emerge when we're more certain of them—which I think we might be as soon as later this year—then I think we need to act very fast in the areas where we've actually seen the risk.  
然后,如果这些风险在我们更加确定的时候出现——我认为可能最早在今年晚些时候——那么我认为我们需要在实际看到风险的领域非常迅速地采取行动。  
**[01:44:07] Speaker B:** I think the only way to do this is to be nimble.  
我认为做到这一点的唯一方法就是保持灵活。  
**[01:44:13] Speaker B:** Now, the legislative process is normally not nimble, but we need to emphasize the urgency of this to everyone involved.  
现在,立法过程通常并不灵活,但我们需要向所有相关人员强调这件事的紧迫性。  
**[01:44:21] Speaker B:** That's why I'm sending this message of urgency.  
这就是为什么我要传达这个紧迫性的信息。  
**[01:44:24] Speaker B:** That's why I wrote Adolescence of Technology. I wanted policymakers, economists, national security professionals, and decision-makers to read it so that they have some hope of acting faster than they would have otherwise.  
这就是为什么我写了《技术的青春期》。我希望政策制定者、经济学家、国家安全专业人士和决策者们能读到它,这样他们才有希望比原本更快地采取行动。  
**[01:44:36] Speaker A:** Is there anything you can do or advocate  
你能做什么或者倡导什么  
**[01:44:42] Speaker A:** That would make it more certain that the benefits of AI are better instantiated? I feel like you have worked with legislatures to say, "Okay, we're going to prevent bioterrorism here. We're going to increase transparency, we're going to increase whistleblower protection."  
有什么能让 AI 的好处更确定地实现出来?我感觉你已经和立法机构合作过,跟他们说「好的,我们要在这里防止生物恐怖主义,我们要增加透明度,我们要增加对举报人的保护」。  
**[01:44:57] Speaker A:** But I think by default, the actual benefits we're looking forward to seem very fragile to different kinds of moral panics or political economy problems.  
但我觉得,在默认情况下,我们所期待的实际好处似乎很容易受到各种道德恐慌或政治经济问题的影响而变得脆弱。  
**[01:45:08] Speaker B:** I don't actually agree that much regarding the developed world. I feel like in the developed world, markets function pretty well.  
关于发达国家,我其实不太同意这个看法。我觉得在发达国家,市场运作得相当好。  
**[01:45:17] Speaker B:** When there's a lot of money to be made on something and it's clearly the best available alternative, it's actually hard for the regulatory system to stop it.  
当某件事能赚很多钱,而且它显然是现有的最佳选择时,监管系统其实很难阻止它。  
**[01:45:27] Speaker B:** We're seeing that in AI itself. A thing I've been trying to fight for is export controls on chips to China.  
我们在 AI 本身就看到了这一点。我一直在努力争取的一件事就是对中国的芯片出口管制。  
**[01:45:38] Speaker B:** That's in the national security interest of the US. That's squarely within the policy beliefs of almost everyone in Congress of both parties.  
这符合美国的国家安全利益。这完全在国会两党几乎所有人的政策信念范围之内。  
**[01:45:52] Speaker B:** The case is very clear. The counterarguments against it, I'll politely call them fishy.  
理由非常清楚。那些反对的论据,我客气地说,很可疑。  
**[01:45:59] Speaker B:** Yet it doesn't happen and we sell the chips because there's so much money riding on it.  
然而它还是没有发生,我们还是把芯片卖了,因为这里面涉及太多钱了。  
**[01:46:08] Speaker B:** That money wants to be made. In that case, in my opinion, that's a bad thing.  
那些钱想要被赚到。在那种情况下,我认为这是件坏事。  
**[01:46:13] Speaker B:** But it also applies when it's a good thing. So if we're talking about drugs and benefits of the technology, I am not as worried about those benefits being hampered in the developed world.  
但当它是好事时,这个道理也同样适用。所以如果我们在谈论药物和技术带来的好处,我并不太担心这些好处在发达国家会受到阻碍。  
**[01:46:30] Speaker B:** I am a little worried about them going too slow. As I said, I do think we should work to speed the approval process in the FDA.  
我有点担心它们进展太慢。就像我说的,我确实认为我们应该努力加快 FDA 的审批流程。  
**[01:46:37] Speaker B:** I do think we should fight against these chatbot bills that you're describing. Described individually, I'm against them. I think they're stupid.  
我确实认为我们应该反对你描述的那些聊天机器人法案。单独来看,我反对它们。我觉得它们很愚蠢。  
**[01:46:46] Speaker B:** But I actually think the bigger worry is the developing world, where we don't have functioning markets and where we often can't build on the technology that we've had.  
但我实际上认为更大的担忧是发展中国家,那里没有运转良好的市场,而且我们往往无法在现有的技术基础上继续发展。  
**[01:46:58] Speaker B:** I worry more that those folks will get left behind.  
我更担心那些人会被落在后面。  
**[01:47:01] Speaker B:** And I worry that even if the cures are developed, maybe there's someone in rural Mississippi who doesn't get it as well.  
而且我担心即使治疗方法被开发出来了,也许 Mississippi 州农村地区的某些人也得不到。  
**[01:47:04] Speaker B:** That's a smaller version of the concern we have in the developing world.  
这是我们在发展中国家所担忧问题的一个缩小版。  
**[01:47:10] Speaker B:** So the things we've been doing are working with philanthropists. We work with folks who deliver medicine and health interventions to the developing world, to sub-Saharan Africa, India, Latin America, and other developing parts of the world.  
所以我们一直在做的事情就是与慈善家合作。我们与那些向发展中国家、撒哈拉以南非洲、印度、拉丁美洲和世界其他发展中地区提供医药和健康干预的人合作。  
**[01:47:34] Speaker B:** That's the thing I think that won't happen on its own.  
我认为这件事不会自己发生。  
**[01:47:39] Speaker A:** You mentioned export controls. Why shouldn't the US and China both have a "country of geniuses in a data center"?  
你提到了出口管制。为什么美国和中国不应该都拥有「数据中心里的天才之国」?  
**[01:47:48] Speaker A:** Why won't it happen or why shouldn't it happen?  
是为什么它不会发生,还是为什么它不应该发生?  
**[01:47:48] Speaker B:** Why shouldn't it happen.  
为什么它不应该发生。  
**[01:47:54] Speaker B:** If this does happen, we could have a few situations. If we have an offense-dominant situation, we could have a situation like nuclear weapons, but more dangerous.  
如果这真的发生了,我们可能会面临几种情况。如果我们有一个进攻占主导的局面,我们可能会遇到类似核武器的情况,但更危险。  
**[01:48:05] Speaker B:** Either side could easily destroy everything.  
任何一方都可以轻易地摧毁一切。  
**[01:48:14] Speaker B:** We could also have a world where it's unstable. The nuclear equilibrium is stable because it's deterrence.  
我们也可能面临一个不稳定的世界。核均衡是稳定的,因为它是威慑。  
**[01:48:19] Speaker B:** But let's say there was uncertainty about, if the two AIs fought, which AI would win? That could create instability.  
但假设存在不确定性,如果两个 AI 打起来,哪个 AI 会赢?这可能会造成不稳定。  
**[01:48:24] Speaker B:** You often have conflict when the two sides have a different assessment of their likelihood of winning.  
当双方对自己获胜的可能性有不同评估时,往往会发生冲突。  
**[01:48:34] Speaker A:** If one side is like, "Oh yeah, there's a 90% chance I'll win," and the other side thinks the same, then a fight is much more likely. They can't both be right,  
如果一方说「哦对,我有 90% 的胜算」,而另一方也这么想,那么冲突就更有可能发生。他们不可能都是对的,  
**[01:48:43] Speaker A:** but they can both think that. But this seems like a fully general argument against the diffusion of AI technology.  
但他们可以都这么认为。但这似乎是一个完全通用的反对 AI 技术扩散的论点。  
**[01:48:46] Speaker A:** That's the implication of this world.  
这就是这种世界观的含义。  
**[01:48:52] Speaker A:** Let me just go on, because I think we will get diffusion eventually.  
让我继续说下去,因为我认为我们最终会实现技术扩散。  
**[01:48:55] Speaker A:** The other concern I have is that governments will oppress their own people with AI.  
我的另一个担忧是,政府会利用 AI 压迫自己的人民。  
**[01:49:04] Speaker A:** I'm worried about a world where you have a country in which there's already a government that's building a high-tech authoritarian state.  
我担心的是这样一个世界:某个国家的政府正在建立一个高科技专制国家。  
**[01:49:16] Speaker A:** To be clear, this is about the government.  
要明确一点,这是关于政府的问题。  
**[01:49:21] Speaker A:** This is not about the people. We need to find a way for people everywhere to benefit.  
这不是关于人民的问题。我们需要找到一种方式,让世界各地的人们都能受益。  
**[01:49:24] Speaker A:** My worry here is about governments.  
我在这里担心的是政府。  
**[01:49:30] Speaker A:** My worry is if the world gets carved up into two pieces, one of those two pieces could be authoritarian or totalitarian in a way that's very difficult to displace.  
我担心的是,如果世界被分成两部分,其中一部分可能会变成专制或极权体制,而且很难被推翻。  
**[01:49:39] Speaker A:** Now, will governments eventually get powerful AI, and is there a risk of authoritarianism?  
那么,政府最终会获得强大的 AI 吗?会有专制主义的风险吗?  
**[01:49:45] Speaker A:** Yes. Will governments eventually get powerful AI, and is there a risk of bad equilibria? Yes, I think both things. But the initial conditions matter. At some point, we're going to need to set up the rules of the road.  
是的。政府最终会获得强大的 AI,会有不良均衡的风险吗?是的,我认为两者都会发生。但初始条件很重要。在某个时刻,我们需要建立游戏规则。  
**[01:50:00] Speaker A:** I'm not saying that one country, either the United States or a coalition of democracies—which I think would be a better setup, although it requires more international cooperation than we currently seem to want to make—should just say, "These are the rules of the road."  
我不是说某一个国家,无论是美国还是民主国家联盟——我认为后者会是更好的安排,尽管这需要比我们目前愿意做的更多的国际合作——应该直接说「这些就是游戏规则」。  
**[01:50:19] Speaker A:** There's going to be some negotiation.  
会有一些谈判。  
**[01:50:22] Speaker A:** The world is going to have to grapple with this. What I would like is for the democratic nations of the world—those whose governments represent closer to pro-human values—are holding the stronger hand and have more leverage when the rules of the road are set.  
全世界都将不得不面对这个问题。我希望的是,世界上的民主国家——那些政府更接近于代表亲人类价值观的国家——在制定游戏规则时能握有更强的牌面和更大的影响力。  
**[01:50:44] Speaker A:** So I'm very concerned about that initial condition.  
所以我非常关注那个初始条件。  
**[01:50:47] Speaker B:** I was re-listening to the interview from three years ago, and one of the ways it aged poorly is that I kept asking questions assuming there was going to be some key fulcrum moment two to three years from now.  
我重新听了三年前的采访,它过时的一个方面是,我一直在提问时假设未来两到三年会有某个关键的转折时刻。  
**[01:50:55] Speaker B:** In fact, being that far out, it just seems like progress continues, AI improves, AI is more diffused, and people will use it for more things.  
实际上,展望那么远,看起来只是进步在继续,AI 在改进,AI 更加普及,人们会用它做更多的事情。  
**[01:51:05] Speaker B:** It seems like you're imagining a world in the future where the countries get together, and "Here's the rules of the road, here's the leverage we have, and here's the leverage you have."  
看起来你设想的是未来的一个世界,各国聚在一起,然后说「这些是游戏规则,这是我们的筹码,这是你们的筹码」。  
**[01:51:13] Speaker B:** But on the current trajectory, everybody will have more AI.  
但按照目前的轨迹,每个人都会拥有更多的 AI。  
**[01:51:18] Speaker B:** Some of that AI will be used by authoritarian countries.  
其中一些 AI 会被专制国家使用。  
**[01:51:20] Speaker B:** Some of that within the authoritarian countries will be used by private actors versus state actors.  
在专制国家内部,一些 AI 会被私人行为者而非国家行为者使用。  
**[01:51:22] Speaker B:** It's not clear who will benefit more.  
目前还不清楚谁会受益更多。  
**[01:51:26] Speaker B:** It's always unpredictable to tell in advance. It seems like the internet privileged authoritarian countries more than you would've expected.  
事先总是难以预测。看起来互联网对专制国家的助益比你预期的要多。  
**[01:51:33] Speaker B:** Maybe AI will be the opposite way around. I want to better understand what you're imagining here.  
也许 AI 会恰恰相反。我想更好地理解你在这里设想的是什么。  
**[01:51:38] Speaker A:** Just to be precise about it,  
准确地说,  
**[01:51:42] Speaker A:** I think the exponential of the underlying technology will continue as it has before.  
我认为底层技术的指数级增长会像以前一样继续下去。  
**[01:51:47] Speaker A:** The models get smarter and smarter, even when they get to a "country of geniuses in a data center."  
模型会变得越来越聪明,即使它们达到了「数据中心里的天才之国」的程度。  
**[01:51:53] Speaker A:** I think you can continue to make the model smarter.  
我认为你可以继续让模型变得更聪明。  
**[01:51:56] Speaker A:** There's a question of getting diminishing returns on their value in the world. How much does it matter after you've already solved human biology?  
存在一个边际收益递减的问题——它们在现实世界中的价值会逐渐降低。在你已经解决了人类生物学问题之后,这些进步还有多重要呢?  
**[01:52:07] Speaker A:** At some point you can do harder, more abstruse math problems, but nothing after that matters.  
到了某个阶段,你可以解决更难、更深奥的数学问题,但在那之后就没什么实际意义了。  
**[01:52:12] Speaker A:** Putting that aside, I do think the exponential will continue, but there will be certain distinguished points on the exponential. Companies, individuals, and countries will reach those points at different times.  
撇开这个不谈,我确实认为指数增长会持续下去,但在指数曲线上会有某些关键节点。不同的公司、个人和国家会在不同时间到达这些节点。  
**[01:52:24] Speaker A:** In "The Adolescence of Technology" I talk about: Is a nuclear deterrent still stable in the world of AI?  
在「技术的青春期」这篇文章中,我探讨了一个问题:在AI时代,核威慑还稳定吗?  
**[01:52:38] Speaker A:** I don't know, but that's an example of one thing we've taken for granted. The technology could reach such a level that we can no longer be certain of it.  
我不知道答案,但这是我们一直认为理所当然的事情的一个例子。技术可能会达到这样的水平,让我们无法再对此确信。  
**[01:52:50] Speaker A:** Think of others. There are points where if you reach a certain level, maybe you have offensive cyber dominance, and every computer system is transparent to you after that unless the other side has an equivalent defense.  
再想想其他方面。存在这样的节点:如果你达到某个水平,也许你就拥有了网络攻击优势,之后每个计算机系统对你来说都是透明的,除非对方拥有同等水平的防御。  
**[01:53:04] Speaker A:** I don't know what the critical moment is or if there's a single critical moment. But I think there will be either a critical moment, a small number of critical moments, or some critical window where AI confers some large advantage from the perspective of national security, and one country or coalition has reached it before others.  
我不知道关键时刻是什么,也不知道是否存在单一的关键时刻。但我认为会有一个关键时刻、少数几个关键时刻,或者某个关键窗口期,在这个时期AI从国家安全角度会带来巨大优势,而某个国家或联盟会先于其他国家达到这个阶段。  
**[01:53:30] Speaker A:** I'm not advocating that they just say, "Okay, we're in charge now." That's not how I think about it.  
我并不是在主张他们直接说「好,现在我们说了算」。我不是这么想的。  
**[01:53:42] Speaker A:** The other side is always catching up. There are extreme actions you're not willing to take, and it's not right to take complete control anyway.  
对方总是在追赶。有些极端行动你不愿意采取,而且完全控制本来就是不对的。  
**[01:53:52] Speaker A:** But at the point that happens, people are going to understand that the world has changed. There's going to be some negotiation, implicit or explicit, about what the post-AI world order looks like.  
但当那个时刻到来时,人们会意识到世界已经改变了。会有某种谈判,无论是隐性的还是显性的,来决定后AI时代的世界秩序是什么样的。  
**[01:54:05] Speaker A:** My interest is in making that negotiation be one in which classical liberal democracy has a strong hand.  
我的目标是让这场谈判成为古典自由民主占据强势地位的谈判。  
**[01:54:14] Speaker B:** I want to understand what that better means, because you say in the essay, "Autocracy is simply not a form of government that people can accept in the post-powerful AI age."  
我想更好地理解这是什么意思,因为你在文章中说「专制根本不是人们在强大AI时代之后可以接受的政府形式」。  
**[01:54:33] Speaker B:** That sounds like you're saying the CCP as an institution cannot exist after we get AGI. That seems like a very strong demand, and it seems to imply a world where the leading lab or the leading country will be able to—and by that language, should get to—determine how the world is governed or what kinds of governments are, and are not, allowed.  
这听起来像是你在说,一旦我们拥有AGI,中国共产党作为一个机构就无法存在了。这似乎是一个非常强硬的要求,而且似乎暗示着这样一个世界:领先的实验室或领先的国家将能够——而且按照那种说法,应该有权——决定世界如何被治理,或者哪些政府形式是允许的,哪些是不允许的。  
**[01:55:02] Speaker A:** I believe that paragraph said something like, "You could take it even further and say X." I wasn't necessarily endorsing that view.  
我记得那一段说的是类似「你甚至可以更进一步说X」这样的话。我不一定赞同那个观点。  
**[01:55:13] Speaker A:** I was saying, "Here's a weaker thing that I believe. We have to worry a lot about authoritarians and we should try to check them and limit their power. You could take this much further and have a more interventionist view that says authoritarian countries with AI are these self-fulfilling cycles that are very hard to displace, so you just need to get rid of them from the beginning."  
我当时说的是「这是我所持有的一个相对温和的观点。我们必须非常担心专制政权,我们应该试图遏制和限制他们的权力。你可以把这个观点推得更远,采取一种更具干预性的观点,认为拥有AI的专制国家会形成这种很难打破的自我强化循环,所以你需要从一开始就消除它们」。  
**[01:55:43] Speaker A:** That has exactly all the problems you say. If you were to make a commitment to overthrowing every authoritarian country, they would take a bunch of actions now that could lead to instability. That just may not be possible.  
那确实有你说的所有问题。如果你承诺要推翻每一个专制国家,他们现在就会采取一系列可能导致不稳定的行动。这可能根本做不到。  
**[01:56:02] Speaker A:** But the point I was making that I do endorse is that it is quite possible that... Today, the view, my view, in most of the Western world is that democracy is a better form of...  
但我确实支持我所提出的观点是,很有可能……今天,我的观点,在大多数西方世界的观点是,民主是一种更好的……  
**[01:56:16] Speaker A:** government than authoritarianism. But if a country's authoritarian, we don't react the way we'd react if they committed a genocide or something.  
政府形式相比威权主义。但如果一个国家是威权政体,我们不会像他们犯下种族灭绝或类似罪行时那样做出反应。  
**[01:56:27] Speaker A:** I guess what I'm saying is I'm a little worried that in the age of AGI, authoritarianism will have a different meaning. It will be a graver thing.  
我想说的是,我有点担心在 AGI 时代,威权主义会有不同的含义。它会变成更严重的问题。  
**[01:56:35] Speaker A:** We have to decide one way or another how to deal with that.  
我们必须以某种方式决定如何应对这个问题。  
**[01:56:39] Speaker A:** The interventionist view is one possible view. I was exploring such views. It may end up being the right view, or it may end up being too extreme.  
干预主义观点是一种可能的观点。我在探索这类观点。它最终可能是正确的观点,也可能太极端。  
**[01:56:47] Speaker A:** But I do have hope. One piece of hope I have is that we have seen that as new technologies are invented, forms of government become obsolete.  
但我确实抱有希望。我希望的一点是,我们已经看到随着新技术的发明,某些政府形式会变得过时。  
**[01:57:04] Speaker A:** I mentioned this in "Adolescence of Technology", where I said feudalism was basically a form of government, and when we invented industrialization, feudalism was no longer sustainable. It no longer made sense.  
我在「技术的青春期」中提到过这一点,我说封建制度本质上是一种政府形式,当我们发明了工业化,封建制度就不再可持续了。它不再有意义了。  
**[01:57:18] Speaker A:** Why is that hope? Couldn't that imply that democracy is no longer going to be a competitive system?  
为什么这是希望呢?这难道不是意味着民主将不再是一个有竞争力的制度吗?  
**[01:57:26] Speaker A:** Right, it could go either way. But these problems with authoritarianism get deeper.  
对,可能朝任何方向发展。但威权主义的这些问题会变得更深刻。  
**[01:57:38] Speaker A:** I wonder if that's an indicator of other problems that authoritarianism will have.  
我想知道这是否预示着威权主义将面临的其他问题。  
**[01:57:44] Speaker A:** In other words, because authoritarianism becomes worse, people are more afraid of it. They work harder to stop it.  
换句话说,因为威权主义变得更糟,人们更害怕它。他们会更努力地阻止它。  
**[01:57:59] Speaker A:** You have to think in terms of total equilibrium. I just wonder if it will motivate new ways of thinking about how to preserve and protect freedom with the new technology.  
你必须从整体平衡的角度思考。我只是想知道这是否会激发新的思维方式,思考如何用新技术来保护和维护自由。  
**[01:58:13] Speaker A:** Even more optimistically, will it lead to a collective reckoning and a more emphatic realization of how important some of the things we take as individual rights are?  
更乐观地说,这会不会导致集体反思,让人们更强烈地意识到我们视为个人权利的那些东西有多重要?  
**[01:58:27] Speaker A:** A more emphatic realization that we really can't give these away.  
更强烈地意识到我们真的不能放弃这些权利。  
**[01:58:32] Speaker A:** We've seen there's no other way to live that actually works.  
我们已经看到没有其他真正行得通的生活方式。  
**[01:58:39] Speaker A:** I am actually hopeful that—it sounds too idealistic, but I believe it could be the case—dictatorships become morally obsolete.  
我实际上抱有希望——这听起来太理想主义了,但我相信可能会是这样——独裁政权在道德上变得过时。  
**[01:58:46] Speaker A:** They become morally unworkable forms of government and the crisis that that creates is sufficient to force us to find another way.  
它们成为道德上行不通的政府形式,由此产生的危机足以迫使我们找到另一条道路。  
**[01:59:03] Speaker B:** I think there is genuinely a tough question here which I'm not sure how you resolve.  
我认为这里确实有一个棘手的问题,我不确定该如何解决。  
**[01:59:07] Speaker B:** We've had to come out one way or another on it through history.  
纵观历史,我们不得不以某种方式对此做出选择。  
**[01:59:11] Speaker B:** With China in the '70s and '80s, we decided that even though it's an authoritarian system, we will engage with it.  
对于七八十年代的中国,我们决定即使它是威权体制,我们也会与之接触。  
**[01:59:15] Speaker B:** I think in retrospect that was the right call, because it's a state authoritarian system but a billion-plus people are much wealthier and better off than they would've otherwise been.  
我认为回过头看那是正确的决定,因为虽然它是国家威权体制,但十亿多人比原本的情况要富裕得多、过得好得多。  
**[01:59:23] Speaker B:** It's not clear that it would've stopped being an authoritarian country otherwise. You can just look at North Korea as an example of that.  
并不清楚如果不那样做它是否会停止成为威权国家。你只要看看朝鲜的例子就知道了。  
**[01:59:30] Speaker B:** I don't know if it takes that much intelligence to remain an authoritarian country that continues to coalesce its own power.  
我不知道保持威权国家并继续巩固自己的权力是否需要那么高的智能。  
**[01:59:40] Speaker B:** You can imagine a North Korea with an AI that's much worse than everybody else's, but still enough to keep power.  
你可以想象一个拥有 AI 的朝鲜,虽然它的 AI 比其他所有人的都差得多,但仍然足以维持政权。  
**[01:59:44] Speaker B:** In general, it seems like we should just have this attitude that the benefits of AI—in the form of all these empowerments of humanity and health—will be big.  
总的来说,我们似乎应该持有这样的态度:AI 的好处——以赋能人类和健康等各种形式呈现——将是巨大的。  
**[01:59:54] Speaker B:** Historically, we have decided it's good to spread the benefits of technology widely, even to people whose governments are authoritarian.  
历史上,我们已经决定广泛传播技术的好处是有益的,即使是对那些政府是威权体制的人民。  
**[02:00:06] Speaker B:** It is a tough question, how to think about it with AI, but historically we have said, "yes,  
对于 AI 该如何思考这个问题确实很棘手,但历史上我们一直说「是的,  
**[02:00:10] Speaker A:** This is a positive-sum world, and it's still worth diffusing the technology.  
这是一个正和世界,技术扩散仍然是值得的。  
**[02:00:15] Speaker A:** There are a number of choices we have. Framing this as a government-to-government decision in national security terms is one lens, but there are a lot of other lenses.  
我们有很多选择。把这个问题框定为政府间的国家安全决策是一个视角,但还有很多其他视角。  
**[02:00:27] Speaker A:** You could imagine a world where we produce all these cures to diseases.  
你可以想象这样一个世界:我们研发出所有这些疾病的治愈方法。  
**[02:00:32] Speaker A:** The cures are fine to sell to authoritarian countries, but the data centers just aren't.  
这些治愈方法可以卖给威权国家,但数据中心不行。  
**[02:00:38] Speaker A:** The chips and the data centers aren't, and the AI industry itself isn't.  
芯片和数据中心不行,AI 产业本身也不行。  
**[02:00:44] Speaker A:** Another possibility I think folks should think about is this.  
我认为大家应该考虑的另一种可能性是这个。  
**[02:00:49] Speaker A:** Could there be developments we can make—either that naturally happen as a result of AI, or that we could make happen by building technology on AI—that create an equilibrium where it becomes infeasible for authoritarian countries to deny their people private use of the benefits of the technology?  
我们能否实现某些进展——无论是 AI 自然带来的,还是我们通过在 AI 基础上构建技术来实现的——从而创造出一种平衡态,使得威权国家无法阻止其人民私下使用这项技术的益处?  
**[02:01:12] Speaker A:** Are there equilibria where we can give everyone in an authoritarian country their own AI model that defends them from surveillance and there isn't a way for the authoritarian country to crack down on this while retaining power?  
是否存在这样的平衡态:我们可以给威权国家的每个人提供自己的 AI 模型来保护他们免受监控,而威权国家在保持权力的同时无法镇压这种情况?  
**[02:01:24] Speaker A:** I don't know. That sounds to me like if that went far enough, it would be a reason why authoritarian countries would disintegrate from the inside.  
我不知道。在我看来,如果这种情况发展到一定程度,将会成为威权国家从内部瓦解的原因。  
**[02:01:35] Speaker A:** But maybe there's a middle world where there's an equilibrium where, if they want to hold on to power, the authoritarians can't deny individualized access to the technology.  
但也许存在一个中间状态,在这种平衡下,如果威权者想要保持权力,就无法拒绝人们个性化地使用这项技术。  
**[02:01:45] Speaker A:** But I actually do have a hope for the more radical version.  
但我确实对更激进的版本抱有希望。  
**[02:01:50] Speaker A:** Is it possible that the technology might inherently have properties—or that by building on it in certain ways we could create properties—that have this dissolving effect on authoritarian structures?  
这项技术是否可能天然具有某些特性——或者我们可以通过特定方式在其基础上构建出某些特性——从而对威权结构产生瓦解作用?  
**[02:02:01] Speaker A:** Now, we hoped originally—think back to the beginning of the Obama administration—that social media and the internet would have that property, and it turns out not to.  
回想 Obama 政府初期,我们最初曾希望社交媒体和互联网会具有这种特性,结果证明并非如此。  
**[02:02:13] Speaker A:** But what if we could try again with the knowledge of how many things could go wrong, and that this is a different technology?  
但如果我们带着对诸多可能出错之处的认知,再加上这是一种不同的技术,再试一次呢?  
**[02:02:23] Speaker A:** I don't know if it would work, but it's worth a try.  
我不知道能否奏效,但值得一试。  
**[02:02:26] Speaker A:** It's just very unpredictable. There are first principles reasons why authoritarianism might be privileged.  
这只是非常难以预测。从第一性原理来看,威权主义可能具有某些优势。  
**[02:02:30] Speaker A:** It's all very unpredictable. We just have to recognize the problem and come up with 10 things we can try, try those, and then assess which ones are working, if any.  
这一切都非常难以预测。我们只需要认清问题,想出 10 种可以尝试的方法,试一试,然后评估哪些有效(如果有的话)。  
**[02:02:40] Speaker A:** Then try new ones if the old ones aren't working.  
如果旧方法不奏效,就尝试新方法。  
**[02:02:46] Speaker B:** But I guess that nets out to today, as you say, that we will not sell data centers, or chips, and the ability to make chips to China.  
但我想这归结到今天,就像你说的,我们不会向中国出售数据中心、芯片,以及制造芯片的能力。  
**[02:02:51] Speaker B:** So in some sense, you are denying… There would be some benefits to the Chinese economy, Chinese people, et cetera, because we're doing that.  
所以从某种意义上说,你是在剥夺……如果我们这样做,会给中国经济、中国人民等带来一些好处。  
**[02:03:02] Speaker B:** Then there'd also be benefits to the American economy because it's a positive-sum world. We could trade. They could have their country's data centers doing one thing. We could have ours doing another.  
同时也会给美国经济带来好处,因为这是一个正和世界。我们可以交易。他们的国家可以用数据中心做一件事,我们可以用我们的做另一件事。  
**[02:03:08] Speaker B:** Already, you're saying it's not worth that positive-sum stipend to empower those countries?  
你的意思已经是,这种正和收益不值得用来增强那些国家的实力?  
**[02:03:14] Speaker A:** What I would say is that we are about to be in a world where growth and economic value will come very easily if we're able to build these powerful AI models.  
我想说的是,我们即将进入这样一个世界:如果我们能够构建这些强大的 AI 模型,增长和经济价值将来得非常容易。  
**[02:03:27] Speaker A:** What will not come easily is distribution of benefits, distribution of wealth, political freedom.  
不容易实现的是利益分配、财富分配和政治自由。  
**[02:03:35] Speaker A:** These are the things that are going to be hard to achieve.  
这些才是难以实现的东西。  
**[02:03:43] Speaker A:** So when I think about policy, I think that the technology and the market will deliver all the fundamental benefits—this is my fundamental belief—almost faster than we can take them.  
所以当我思考政策问题时,我认为技术和市场会带来所有根本性的好处——这是我的基本信念——其速度几乎快到我们来不及接受。  
**[02:03:55] Speaker A:** These questions about distribution and political freedom and rights are the ones that will actually matter and that policy should focus on.  
关于分配、政治自由和权利的这些问题才是真正重要的,也是政策应该关注的重点。  
**[02:04:02] Speaker B:** Speaking of distribution, as you were mentioning, we have developing countries. In many cases, catch-up growth has been weaker than we would have hoped for.  
说到分配问题,正如你提到的,我们有发展中国家。在很多情况下,追赶式增长比我们期望的要弱。  
**[02:04:12] Speaker B:** But when catch-up growth does happen, it's fundamentally because they have underutilized labor.  
但当追赶式增长确实发生时,根本原因是它们有未充分利用的劳动力。  
**[02:04:18] Speaker B:** We can bring the capital and know-how from developed countries to these countries, and then they can grow quite rapidly.  
我们可以把发达国家的资本和技术诀窍带到这些国家,然后它们就能快速增长。  
**[02:04:21] Speaker B:** Obviously, in a world where labor is no longer the constraining factor, this mechanism no longer works.  
显然,在一个劳动力不再是制约因素的世界里,这种机制就不再有效了。  
**[02:04:30] Speaker B:** So is the hope basically to rely on philanthropy from the people or countries who immediately get wealthy from AI? What is the hope?  
那么希望是不是基本上要依靠那些因 AI 立即变富的人或国家的慈善事业?希望在哪里?  
**[02:04:38] Speaker A:** Philanthropy should obviously play some role, as it has in the past.  
慈善事业显然应该发挥一定作用,就像过去一样。  
**[02:04:44] Speaker A:** But I think growth is always better and stronger if we can make it endogenous.  
但我认为如果我们能让增长变得内生,效果总是会更好、更强劲。  
**[02:04:50] Speaker A:** What are the relevant industries in an AI-driven world?  
在一个 AI 驱动的世界里,相关的产业是什么?  
**[02:04:58] Speaker A:** I said we shouldn't build data centers in China, but there's no reason we shouldn't build data centers in Africa. In fact, I think it'd be great to build data centers in Africa.  
我说过我们不应该在中国建数据中心,但我们没有理由不在非洲建数据中心。事实上,我认为在非洲建数据中心是件很棒的事。  
**[02:05:04] Speaker A:** As long as they're not owned by China, we should build data centers in Africa. I think that's a great thing to do.  
只要它们不归中国所有,我们就应该在非洲建数据中心。我认为这是一件很好的事情。  
**[02:05:16] Speaker A:** There's no reason we can't build a pharmaceutical industry that's AI-driven.  
我们没有理由不能建立一个 AI 驱动的制药产业。  
**[02:05:22] Speaker A:** If AI is accelerating drug discovery, then there will be a bunch of biotech startups.  
如果 AI 正在加速药物发现,那么就会有一批生物科技初创公司。  
**[02:05:28] Speaker A:** Let's make sure some of those happen in the developing world.  
让我们确保其中一些发生在发展中国家。  
**[02:05:31] Speaker A:** Certainly, during the transition—we can talk about the point where humans have no role—humans will still have some role in starting up these companies and supervising the AI models.  
当然,在过渡期间——我们可以讨论人类完全没有角色的那个时刻——人类在创办这些公司和监督 AI 模型方面仍然会有一定作用。  
**[02:05:41] Speaker A:** So let's make sure some of those humans are in the developing world so that fast growth can happen there as well.  
所以让我们确保其中一些人在发展中国家,这样快速增长也能在那里发生。  
**[02:05:44] Speaker B:** You guys recently announced that Claude is going to have a constitution that's aligned to a set of values, and not necessarily just to the end user.  
你们最近宣布 Claude 将有一个与一套价值观对齐的宪章,而不一定只是与终端用户对齐。  
**[02:05:53] Speaker B:** There's a world I can imagine where if it is aligned to the end user, it preserves the balance of power we have in the world today because everybody gets to have their own AI that's advocating for them.  
我可以想象这样一个世界:如果它与终端用户对齐,就能保持我们今天在世界上的权力平衡,因为每个人都能拥有为自己辩护的 AI。  
**[02:05:59] Speaker B:** The ratio of bad actors to good actors stays constant. It seems to work out for our world today.  
坏人和好人的比例保持不变。这似乎对我们今天的世界是行得通的。  
**[02:06:07] Speaker B:** Why is it better not to do that, but to have a specific set of values that the AI should carry forward?  
为什么不这样做反而更好,而是要让 AI 承载一套特定的价值观?  
**[02:06:12] Speaker A:** I'm not sure I'd quite draw the distinction in that way. There may be two relevant distinctions here.  
我不确定我会这样划分。这里可能有两个相关的区别。  
**[02:06:22] Speaker A:** I think you're talking about a mix of the two. One is, should we give the model a set of instructions about 'do this' versus 'don't do this'?  
我认为你说的是两者的混合。一个是,我们应该给模型一套「做这个」还是「不要做这个」的指令?  
**[02:06:31] Speaker A:** The other is, should we give the model a set of principles for how to act?  
另一个是,我们应该给模型一套关于如何行动的原则吗?  
**[02:06:44] Speaker A:** It's kind of purely a practical and empirical thing that we've observed.  
这其实纯粹是我们观察到的一个实践和经验性的事情。  
**[02:06:48] Speaker A:** By teaching the model principles, getting it to learn from principles, its behavior is more consistent, it's easier to cover edge cases, and the model is more...  
通过教给模型原则,让它从原则中学习,它的行为会更加一致,更容易覆盖边缘情况,而且模型会更加...  
**[02:06:58] Speaker A:** likely to do what people want it to do. In other words, if you give it a list of rules—"don't tell people how to hot-wire a car, don't speak in Korean"—it doesn't really understand the rules, and it's hard to generalize from them.  
更有可能做人们想让它做的事。换句话说,如果你给它一系列规则——「不要告诉别人怎么偷车」「不要说韩语」——它并不真正理解这些规则,也很难从中归纳总结。  
**[02:07:15] Speaker A:** It's just a list of do's and don'ts. Whereas if you give it principles—it has some hard guardrails like "Don't make biological weapons" but—overall you're trying to understand what it should be aiming to do, how it should be aiming to operate.  
这只是一堆能做和不能做的清单。而如果给它原则——虽然也有一些硬性底线,比如「不要制造生物武器」——但总体上你是在试图让它理解应该以什么为目标,应该如何运作。  
**[02:07:31] Speaker A:** So just from a practical perspective, that turns out to be a more effective way to train the model.  
所以从实践角度来说,这被证明是训练模型更有效的方式。  
**[02:07:35] Speaker A:** That's the rules versus principles trade-off. Then there's another thing you're talking about, which is the corrigibility versus intrinsic motivation trade-off.  
这就是规则与原则之间的权衡。然后你说的还有另一个问题,就是可纠正性与内在动机之间的权衡。  
**[02:07:51] Speaker A:** How much should the model be a kind of "skin suit" where it just directly follows the instructions given to it by whoever is giving those instructions, versus how much should the model have an inherent set of values and go off and do things on its own?  
模型应该在多大程度上成为一个「外壳」,直接遵循任何指令发出者的指令,相对的,模型又应该在多大程度上拥有自己固有的价值观,并自主行动?  
**[02:08:14] Speaker A:** There I would actually say everything about the model is closer to the direction that it should mostly do what people want. It should mostly follow instructions.  
在这一点上,我认为模型的所有设计都更倾向于它应该主要做人们想要的事。它应该主要遵循指令。  
**[02:08:24] Speaker A:** We're not trying to build something that goes off and runs the world on its own.  
我们并不是要造一个自己跑出去管理世界的东西。  
**[02:08:29] Speaker A:** We're actually pretty far on the corrigible side. Now, what we do say is there are certain things that the model won't do.  
我们实际上在可纠正性这一端已经走得很远了。不过,我们确实也说了,有些事情模型是不会做的。  
**[02:08:40] Speaker A:** I think we say it in various ways in the constitution, that under normal circumstances, if someone asks the model to do a task, it should do that task. That should be the default. But if you've asked it to do something dangerous, or to harm someone else, then the model is unwilling to do that.  
我想我们在宪法中用各种方式表达过,在正常情况下,如果有人要求模型执行一项任务,它就应该去做。这应该是默认行为。但如果你要求它做危险的事,或者伤害他人,那模型就不愿意做。  
**[02:09:01] Speaker A:** So I actually think of it as a mostly corrigible model that has some limits, but those limits are based on principles.  
所以我实际上把它看作一个基本可纠正的模型,有一些限制,但这些限制是基于原则的。  
**[02:09:07] Speaker B:** Then the fundamental question is, how are those principles determined? This is not a special question for Anthropic.  
那根本问题就是,这些原则是如何确定的?这不是 Anthropic 特有的问题。  
**[02:09:15] Speaker B:** This would be a question for any AI company. But because you have been the ones to actually write down the principles, I get to ask you this question.  
这会是任何 AI 公司都要面对的问题。但因为是你们实际写下了这些原则,所以我才向你提这个问题。  
**[02:09:25] Speaker B:** Normally, a constitution is written down, set in stone, and there's a process of updating it and changing it and so forth. In this case, it seems like a document that people at Anthropic write, that can be changed at any time, that guides the behavior of systems that are going to be the basis of a lot of economic activity.  
通常,宪法是写下来的,固定不变的,有一套更新和修改的程序等等。而在这种情况下,它看起来像是 Anthropic 的人写的一份文件,可以随时修改,却指导着将成为大量经济活动基础的系统的行为。  
**[02:09:45] Speaker B:** How do you think about how those principles should be set?  
你怎么看待这些原则应该如何制定?  
**[02:09:50] Speaker A:** I think there are maybe three sizes of loop here, three ways to iterate.  
我认为这里可能有三个层次的循环,三种迭代方式。  
**[02:09:58] Speaker A:** One is we iterate within Anthropic. We train the model, we're not happy with it, and we change the constitution. I think that's good to do.  
第一种是我们在 Anthropic 内部迭代。我们训练模型,对结果不满意,然后修改宪法。我认为这是应该做的。  
**[02:10:06] Speaker A:** Putting out public updates to the constitution every once in a while is good because people can comment on it.  
时不时发布宪法的公开更新是好的,因为人们可以对此发表意见。  
**[02:10:10] Speaker A:** The second level of loop is different companies having different constitutions. I think it's useful. Anthropic puts out a constitution, Gemini puts out a constitution, and other companies put out a constitution.  
第二个层次的循环是不同公司有不同的宪法。我认为这很有用。Anthropic 发布一个宪法,Gemini 发布一个宪法,其他公司也发布宪法。  
**[02:10:28] Speaker A:** People can look at them and compare. Outside observers can critique and say, "I like this thing from this constitution and this thing from that constitution."  
人们可以看看它们并进行比较。外部观察者可以评论说,「我喜欢这个宪法里的这一点,喜欢那个宪法里的那一点。」  
**[02:10:40] Speaker A:** That creates a soft incentive and feedback for all the companies to  
这为所有公司创造了一种软性激励和反馈机制,促使它们  
**[02:10:45] Speaker A:** Take the best of each element and improve. Then I think there's a third loop, which is  
取各个要素中最好的部分并加以改进。然后我认为还有第三个循环,就是  
**[02:10:50] Speaker A:** society beyond the AI companies and beyond just those who comment without hard power.  
AI 公司之外的社会,以及那些没有实权只能发表评论的人之外的群体。  
**[02:10:59] Speaker A:** There we've done some experiments. A couple years ago, we did an experiment with the Collective Intelligence Project to basically poll people and ask them what should be in our AI constitution.  
在这方面我们已经做了一些实验。几年前,我们与 Collective Intelligence Project 合作做了一个实验,基本上就是对人们进行调查,询问他们认为我们的 AI 宪法里应该包含什么。  
**[02:11:15] Speaker A:** At the time, we incorporated some of those changes.  
当时,我们采纳了其中一些意见。  
**[02:11:17] Speaker A:** So you could imagine doing something like that with the new approach we've taken to the constitution. It's a little harder because  
所以你可以想象用我们对宪法采取的新方法做类似的事情。这会更难一些,因为  
**[02:11:23] Speaker A:** it was an easier approach to take when the constitution was a list of dos and don'ts.  
当宪法是一系列该做和不该做的条目时,这种方法会更容易执行。  
**[02:11:29] Speaker A:** At the level of principles, it has to have a certain amount of coherence.  
在原则层面,它必须具有一定程度的连贯性。  
**[02:11:32] Speaker A:** But you could still imagine getting views from a wide variety of people.  
但你仍然可以想象从各种各样的人那里获取意见。  
**[02:11:37] Speaker A:** You could also imagine—and this is a crazy idea, but this whole interview is about crazy ideas—systems of representative government having input.  
你还可以想象——这是个疯狂的想法,但这整个访谈就是关于疯狂想法的——让代议制政府系统参与进来提供意见。  
**[02:11:52] Speaker A:** I wouldn't do this today because the legislative process is so slow.  
我现在不会这么做,因为立法程序太慢了。  
**[02:11:55] Speaker A:** This is exactly why I think we should be careful about the legislative process and AI regulation.  
这正是为什么我认为我们应该对立法程序和 AI 监管保持谨慎。  
**[02:12:00] Speaker A:** But there's no reason you couldn't, in principle, say, "All AI models have to have a constitution that starts with these things, and then you can append other things after it, but there has to  
但原则上没有理由不能这样说:「所有 AI 模型都必须有一个宪法,以这些内容开头,然后你可以在后面附加其他内容,但必须  
**[02:12:13] Speaker A:** be this special section that takes precedence." I wouldn't do that. That's too rigid and sounds  
有这个具有优先权的特殊部分。」我不会那样做。那太僵化了,听起来  
**[02:12:22] Speaker A:** overly prescriptive in a way that I think overly aggressive legislation is.  
过于规定性,就像我认为过于激进的立法那样。  
**[02:12:26] Speaker A:** But that is a thing you could try to do. Is there some much less heavy-handed  
但那确实是你可以尝试做的事情。有没有一些不那么强硬的  
**[02:12:32] Speaker A:** version of that? Maybe. I really like control loop two.  
版本呢?也许有。我真的很喜欢第二个控制循环。  
**[02:12:37] Speaker B:** Obviously, this is not how constitutions of actual governments do or should work.  
显然,这不是真实政府的宪法运作的方式,也不应该是。  
**[02:12:42] Speaker B:** There's not this vague sense in which the Supreme Court will feel out how people are feeling—what are the vibes—and update the constitution accordingly.  
不存在这种模糊的情况,即最高法院会感受人们的感觉——氛围如何——然后相应地更新宪法。  
**[02:12:50] Speaker B:** With actual governments, there's a more formal, procedural process.  
对于真实的政府,有一个更正式、更程序化的过程。  
**[02:12:55] Speaker B:** But you have a vision of competition between constitutions, which is actually very reminiscent of how some libertarian charter cities people used to talk about what an archipelago of different kinds of governments would look like.  
但你对宪法之间的竞争有一种愿景,这实际上很像一些自由意志主义特许城市倡导者过去谈论的,关于由不同类型政府组成的群岛会是什么样子。  
**[02:13:07] Speaker B:** There would be selection among them of who could operate the most effectively and where people would be the happiest.  
它们之间会有选择,看谁能运作得最有效、人们在哪里会最幸福。  
**[02:13:15] Speaker B:** In a sense, you're recreating that vision of a utopia of archipelagos.  
从某种意义上说,你正在重现那种群岛乌托邦的愿景。  
**[02:13:23] Speaker B:** I think that vision has things to recommend it and things that will go wrong with it.  
我认为这个愿景既有可取之处,也有会出问题的地方。  
**[02:13:31] Speaker B:** It's an interesting, in some ways compelling, vision, but things will go wrong that you hadn't imagined.  
这是一个有趣的、在某些方面很有吸引力的愿景,但会出现你没有想到的问题。  
**[02:13:40] Speaker B:** So I like loop two as well, but I feel like the whole thing has got to be some mix of loops one, two, and three, and it's a matter of the proportions.  
所以我也喜欢第二个循环,但我觉得整个体系必须是第一、第二和第三个循环的某种混合,这是一个比例问题。  
**[02:13:46] Speaker A:** I think that's gotta be the answer.  
我认为这必须是答案。  
**[02:13:53] Speaker B:** When somebody eventually writes the equivalent of The Making of the Atomic Bomb for this era, what is the thing that will be hardest to glean from the historical record that they're most likely to miss?  
当有人最终为这个时代写出相当于《原子弹的诞生》那样的作品时,从历史记录中最难提取、他们最有可能遗漏的是什么?  
**[02:14:02] Speaker A:** I think a few things. One is,  
我认为有几件事。一个是,  
**[02:14:06] Speaker A:** at every moment of this exponential, the extent to which the world outside it didn't understand it.  
在这个指数增长的每个时刻,外部世界不理解它的程度有多深。  
**[02:14:12] Speaker A:** This is a bias that's often present in history. Anything that actually happened looks inevitable in retrospect.  
这是历史中经常存在的一种偏差。任何实际发生的事情在回顾时都显得不可避免。  
**[02:14:17] Speaker A:** When people look back, it will  
当人们回顾时,  
**[02:14:24] Speaker A:** It'll be hard for them to put themselves in the place of people who were actually making a bet on this thing to happen that wasn't inevitable, that we had these arguments like the arguments I make for scaling or that continual learning will be solved.  
他们很难设身处地去理解那些真正押注于这件事会发生的人——这件事并非必然会发生,我们当时提出的论点,比如我关于扩展定律的论证,或者持续学习问题终将被解决的论点。  
**[02:14:38] Speaker A:** Some of us internally put a high probability on this happening, but there's a world outside us that's not acting on that at all.  
我们内部有些人认为这件事发生的概率很高,但外面的世界完全没有基于这个判断来行动。  
**[02:14:58] Speaker A:** I think the weirdness of it, unfortunately the insularity of it...  
我觉得这件事的诡异之处,不幸的是它的封闭性...  
**[02:15:07] Speaker A:** If we're one year or two years away from it happening, the average person on the street has no idea.  
如果我们距离它发生只有一两年的时间,街上的普通人对此毫无概念。  
**[02:15:10] Speaker A:** That's one of the things I'm trying to change with the memos, with talking to policymakers.  
这正是我试图通过写备忘录、与政策制定者对话来改变的事情之一。  
**[02:15:14] Speaker A:** I don't know, but I think that's just a crazy thing.  
我不知道,但我觉得这真是件疯狂的事。  
**[02:15:19] Speaker A:** Finally, I would say—and this probably applies to almost all historical moments of crisis—how absolutely fast it was happening, how everything was happening all at once.  
最后我想说——这可能适用于几乎所有历史性的危机时刻——就是一切发生得如此之快,所有事情都同时发生。  
**[02:15:33] Speaker A:** Decisions that you might think were carefully calculated, well actually you have to make that decision, and then you have to make 30 other decisions on the same day because it's all happening so fast.  
那些你可能以为是经过深思熟虑的决策,实际上你必须当场做出决定,然后同一天还要做出其他30个决定,因为一切发生得太快了。  
**[02:15:47] Speaker A:** You don't even know which decisions are going to turn out to be consequential.  
你甚至不知道哪些决策最终会产生重大影响。  
**[02:15:52] Speaker A:** One of my worries—although it's also an insight into what's happening—is that some very critical decision will be some decision where someone just comes into my office and is like, "Dario, you have two minutes. Should we do thing A or thing B on this?"  
我的一个担忧——尽管这也让我洞察到正在发生什么——就是某个非常关键的决策可能是这样的场景:有人冲进我办公室说,「Dario,你有两分钟时间。这件事我们该做A还是B?」  
**[02:16:14] Speaker A:** Someone gives me this random half-page memo and asks, "Should we do A or B?" I'm like, "I don't know. I have to eat lunch. Let's do B."  
有人递给我一份随便写的半页备忘录,问我「我们该做A还是B?」我心想,「我不知道。我得去吃午饭了。那就选B吧。」  
**[02:16:20] Speaker A:** That ends up being the most consequential thing ever.  
结果那竟然成了最重大的决策。  
**[02:16:26] Speaker B:** So final question. There aren't tech CEOs who are usually writing 50-page memos every few months.  
那么最后一个问题。通常科技公司CEO不会每隔几个月就写50页的备忘录。  
**[02:16:35] Speaker B:** It seems like you have managed to build a role for yourself and a company around you which is compatible with this more intellectual-type role of CEO.  
看起来你成功地为自己塑造了一个角色,并围绕你建立了一家公司,这种模式与更偏智识型的CEO角色相契合。  
**[02:16:47] Speaker B:** I want to understand how you construct that. How does that work? Do you just go away for a couple of weeks and then you tell your company, "This is the memo. Here's what we're doing"?  
我想了解你是如何构建这种模式的。这是怎么运作的?你是不是消失几周,然后回来告诉公司,「这是备忘录。我们就这么干」?  
**[02:16:56] Speaker B:** It's also reported that you write a bunch of these internally.  
据说你在内部也写了很多这样的备忘录。  
**[02:16:59] Speaker A:** For this particular one, I wrote it over winter break.  
就这份特定的备忘录而言,我是在寒假期间写的。  
**[02:17:04] Speaker A:** I was having a hard time finding the time to actually write it.  
我当时很难找到时间真正去写它。  
**[02:17:08] Speaker A:** But I think about this in a broader way. I think it relates to the culture of the company.  
但我是从更广阔的角度来思考这个问题的。我认为这关系到公司的文化。  
**[02:17:13] Speaker A:** I probably spend a third, maybe 40%, of my time making sure the culture of Anthropic is good.  
我可能花三分之一,也许40%的时间来确保Anthropic的文化健康。  
**[02:17:19] Speaker A:** As Anthropic has gotten larger, it's gotten harder to get directly involved in the training of the models, the launch of the models, the building of the products.  
随着Anthropic规模变大,我越来越难以直接参与模型训练、模型发布、产品构建等具体工作。  
**[02:17:26] Speaker A:** It's 2,500 people. I have certain instincts, but it's very difficult to get involved in every single detail.  
公司有2500人。我有一些直觉判断,但很难介入每一个细节。  
**[02:17:41] Speaker A:** I try as much as possible, but one thing that's very leveraged is making sure Anthropic is a good place to work, people like working there, everyone thinks of themselves as team members, and everyone works together instead of against each other.  
我尽可能去尝试,但有一件事杠杆效应很强,那就是确保Anthropic是个好的工作场所,让大家喜欢在这里工作,每个人都把自己当作团队成员,大家一起协作而不是相互对抗。  
**[02:17:51] Speaker A:** We've seen as some of the other AI companies have grown—without naming any names—we're starting to see decoherence and people fighting each other.  
我们看到其他一些AI公司在发展过程中——我就不点名了——开始出现失序现象,人们相互争斗。  
**[02:18:03] Speaker A:** I would argue there was even a lot of that from the beginning, but it's gotten worse.  
我认为从一开始就有很多这种问题,但现在变得更糟了。  
**[02:18:08] Speaker A:** I think we've done an extraordinarily good job, even if not perfect, of holding the company together, making everyone feel the mission, that we're sincere about the mission, and that everyone has faith that everyone else there is working for the right reason.  
我认为我们做得非常好,即使不完美,我们把公司凝聚在一起,让每个人都能感受到使命,相信我们对使命是认真的,每个人都相信其他人是出于正确的理由在这里工作。  
**[2:18:23] Speaker A:** That we're a team, that people aren't trying to get ahead at each other's expense or backstab each other, which again, I think happens a lot at some of the other places.  
我们是一个团队,大家不会为了自己的利益而互相倾轧或背后捅刀子,而这种事在其他一些公司里我觉得经常发生。  
**[2:18:33] Speaker B:** How do you make that the case?  
你是如何做到这一点的?  
**[2:18:33] Speaker A:** It's a lot of things. It's me, it's Daniela, who runs the company day to day, it's the co-founders, it's the other people we hire, it's the environment we try to create.  
这涉及很多方面。包括我自己,包括 Daniela——她负责公司的日常运营,还有联合创始人们,我们招聘的其他人,以及我们努力营造的环境。  
**[2:18:44] Speaker A:** But I think an important thing in the culture is that the other leaders as well, but especially me, have to articulate what the company is about, why it's doing what it's doing, what its strategy is, what its values are, what its mission is, and what it stands for.  
但我认为在文化建设中很重要的一点是,其他领导者,尤其是我,必须清楚地阐明公司的定位是什么,为什么要做现在做的事,我们的战略是什么,我们的价值观是什么,我们的使命是什么,以及我们代表什么。  
**[2:19:06] Speaker A:** When you get to 2,500 people, you can't do that person by person. You have to write, or you have to speak to the whole company.  
当公司有 2500 人的时候,你不可能逐一去沟通。你必须通过文字,或者面向全公司讲话。  
**[2:19:12] Speaker A:** This is why I get up in front of the whole company every two weeks and speak for an hour.  
这就是为什么我每两周会站在全公司面前讲一个小时。  
**[2:19:18] Speaker A:** I wouldn't say I write essays internally. I do two things. One, I write this thing called a DVQ, Dario Vision Quest. I wasn't the one who named it that.  
我不会说我在内部写文章。我主要做两件事。第一件,我会写一个叫 DVQ 的东西,Dario Vision Quest。这个名字不是我起的。  
**[2:19:27] Speaker A:** That's the name it received, and it's one of these names that I tried to fight because it made it sound like I was going off and smoking peyote or something. But the name just stuck.  
这是大家给它起的名字,我曾经试图反对这个名字,因为听起来像是我跑去吸食仙人掌毒品之类的。但这个名字就这么固定下来了。  
**[2:19:38] Speaker A:** So I get up in front of the company every two weeks. I have a three or four-page document, and I just talk through three or four different topics about what's going on internally, the models we're producing, the products, the outside industry, the world as a whole as it relates to AI and geopolitically in general. Just some mix of that.  
所以我每两周会站在公司面前。我会准备一份三到四页的文档,然后讲三到四个不同的话题,包括公司内部正在发生什么,我们正在开发的模型,我们的产品,外部行业动态,以及整个世界与 AI 相关的情况和总体的地缘政治局势。就是这些内容的某种组合。  
**[2:19:59] Speaker A:** I go through very honestly and I say, "This is what I'm thinking, and this is what Anthropic leadership is thinking," and then I answer questions.  
我会非常坦诚地讲,我会说「这是我的想法,这是 Anthropic 领导层的想法」,然后回答问题。  
**[2:20:06] Speaker A:** That direct connection has a lot of value that is hard to achieve when you're passing things down the chain six levels deep.  
这种直接的连接非常有价值,如果信息要通过六层管理链条逐级传递,就很难做到这一点。  
**[2:20:19] Speaker A:** A large fraction of the company comes to attend, either in person or virtually.  
公司很大一部分员工会参加,有的现场参加,有的远程参加。  
**[2:20:27] Speaker A:** It really means that you can communicate a lot. The other thing I do is I have a channel in Slack where I just write a bunch of things and comment a lot.  
这意味着你可以传达很多信息。我做的另一件事是在 Slack 上有一个频道,我会在那里写很多内容,发表很多评论。  
**[2:20:36] Speaker A:** Often that's in response to things I'm seeing at the company or questions people ask.  
通常是针对我在公司看到的事情或者员工提出的问题做出回应。  
**[2:20:44] Speaker A:** We do internal surveys and there are things people are concerned about, and so I'll write them up.  
我们会做内部调研,员工会有一些关切的问题,我就会把这些写出来。  
**[2:20:50] Speaker A:** I'm just very honest about these things. I just say them very directly.  
我对这些事情非常坦诚。我会非常直接地说出来。  
**[2:20:56] Speaker A:** The point is to get a reputation of telling the company the truth about what's happening, to call things what they are, to acknowledge problems, to avoid the sort of corpo speak, the kind of defensive communication that often is necessary in public because the world is very large and full of people who are interpreting things in bad faith.  
重点是要建立一种声誉,就是告诉公司正在发生的事情的真相,直言不讳地称呼事物本身,承认问题,避免那种公司官话,那种在公开场合往往必要的防御性沟通——因为外部世界很大,充满了恶意曲解的人。  
**[2:21:14] Speaker A:** But if you have a company of people who you trust, and we try to hire people that we trust, then you can really just be entirely unfiltered.  
但如果公司里都是你信任的人,而我们也努力招聘我们信任的人,那你就可以完全不加过滤地沟通。  
**[2:21:31] Speaker A:** I think that's an enormous strength of the company.  
我认为这是公司的一个巨大优势。  
**[2:21:33] Speaker A:** It makes it a better place to work, it makes people more than the sum of their parts, and increases the likelihood that we accomplish the mission because everyone is on the same page about the mission, and everyone is debating and discussing how best to accomplish the mission.  
这让公司成为一个更好的工作场所,让团队产生大于各部分之和的效果,并且增加了我们完成使命的可能性,因为每个人对使命的理解是一致的,每个人都在辩论和讨论如何最好地完成使命。  
**[2:21:46] Speaker B:** Well, in lieu of an external Dario Vision Quest, we have this interview.  
那么,作为外部版 Dario Vision Quest 的替代,我们有了这次访谈。  
**[2:21:50] Speaker A:** This interview is a little like that.  
这次访谈确实有点像那样。  
**[2:21:50] Speaker B:** This has been fun, Dario. Thanks for doing it.  
这次很愉快,Dario。感谢你接受访谈。  
**[2:21:54] Speaker A:** Thank you, Dwarkesh.  
谢谢你,Dwarkesh。  

---

## Deep Dive Summary

### Topic 1: Progress Over Three Years: Technology vs Public Perception
三年来的进展:技术发展与公众认知的差距
_[00:00]_

**Q:** What has been the biggest difference in AI progress over the last three years?
**问：** 过去三年AI进展最大的变化是什么?

**A:** Speaker B argues that the exponential growth of AI capabilities has proceeded roughly as expected, with models progressing from "smart high school student to smart college student to beginning to do PhD and professional stuff." While the specific trajectory of code capabilities wasn't precisely predicted, the overall frontier has evolved predictably. The most surprising development has been "the lack of public recognition of how close we are to the end of the exponential." B expresses bewilderment that both insiders and outsiders continue focusing on "the same tired, old hot-button political issues" when such a fundamental technological transition is imminent.
**答：** Speaker B认为AI能力的指数级增长基本符合预期,模型从"聪明高中生到聪明大学生,再到开始做PhD和专业工作"的进程如期推进。虽然代码能力的具体轨迹没有精确预测,但整体前沿的演进是可预见的。最令人惊讶的是"公众缺乏对我们距离指数增长终点有多近的认识"。B对圈内外人士仍在关注"那些老生常谈的政治热点话题"感到困惑,因为如此重大的技术转型即将到来。

### Topic 2: The Big Blob of Compute Hypothesis: Seven Factors That Matter
大规模计算假说：七个关键因素
_[01:19]_

**Q:** What is the current scaling hypothesis for AI, especially with RL scaling emerging alongside pre-training?
**问：** 当前AI的规模化假说是什么，特别是在RL规模化与预训练并存的情况下？

**A:** Speaker B explains that the core hypothesis remains unchanged since 2017's "Big Blob of Compute Hypothesis," which predates GPT-1 and aligns with Rich Sutton's "Bitter Lesson." The hypothesis identifies seven key factors: raw compute, data quantity, data quality and distribution breadth, training duration, scalable objective functions (like pre-training loss or RL goals with verifiable/subjective rewards), and numerical stability for "laminar" compute flow. Speaker B emphasizes that specific techniques and cleverness matter far less than these foundational elements, and this framework applies equally to pre-training and RL phases.
**答：** Speaker B解释核心假说自2017年提出的"大规模计算假说"以来保持不变，这个假说早于GPT-1的出现，与Rich Sutton的"痛苦教训"理念一致。该假说确定了七个关键因素：原始算力、数据量、数据质量和分布广度、训练时长、可扩展的目标函数（如预训练损失或包含可验证/主观奖励的RL目标），以及数值稳定性以确保计算的"层流"流动。Speaker B强调具体技术和巧妙方法远不如这些基础要素重要，这个框架同样适用于预训练和RL阶段。

### Topic 3: The Sample Efficiency Puzzle: Rich Sutton's Critique
样本效率难题：Rich Sutton的质疑
_[04:21]_

**Q:** Why does scaling require massive compute and bespoke RL environments to teach basic skills like Excel or web browsing, if we have a true human-like learning algorithm?
**问：** 如果我们拥有真正类人的学习算法，为什么规模化需要海量计算和定制RL环境来教授Excel或网页浏览等基础技能？

**A:** Speaker B presents Rich Sutton's skepticism about LLMs, noting that a genuinely human-like learning system shouldn't require billions of dollars in compute and data to learn everyday skills. This inefficiency suggests current approaches may be "scaling the wrong thing" and lacking a core human learning algorithm. The question challenges whether the emphasis on RL environments for teaching specific skills (APIs, Slack, browsers) contradicts the goal of developing agents that can learn on the fly, raising fundamental doubts about whether current scaling strategies capture true intelligence.
**答：** Speaker B提出了Rich Sutton对LLM的怀疑，指出真正类人的学习系统不应该需要数十亿美元的计算和数据来学习日常技能。这种低效率表明当前方法可能在"规模化错误的东西"，缺乏核心的人类学习算法。这个问题质疑为什么要强调用RL环境来教授特定技能(API、Slack、浏览器)，这是否与开发能够即时学习的智能体目标相矛盾，从根本上怀疑当前的规模化策略是否捕获了真正的智能。

### Topic 4: Pre-training and RL as Evolution: The Distribution Generalization Insight
预训练和RL作为进化：分布泛化的洞察
_[06:16]_

**Q:** How should we understand the relationship between pre-training, RL, human learning, and evolution given the sample efficiency differences?
**问：** 考虑到样本效率的差异，我们应该如何理解预训练、RL、人类学习和进化之间的关系？

**A:** Speaker A addresses the puzzle by drawing parallels between pre-training/RL evolution and human learning. GPT-1 trained on narrow fanfiction data didn't generalize, but GPT-2's broad internet scrape enabled cross-task generalization—the same pattern now emerging in RL as it expands from math contests to code to diverse tasks. While models require trillions of tokens versus humans' more efficient learning, they excel at in-context learning with long contexts (million tokens). Speaker A proposes a hierarchy: evolution → long-term learning → short-term learning → immediate reaction, with LLM phases (pre-training/RL and in-context learning) occupying intermediate positions that don't map one-to-one with human modes but serve analogous functions.
**答：** Speaker A通过类比预训练/RL与人类学习和进化来解决这个难题。GPT-1在狭窄的同人小说数据上训练无法泛化，但GPT-2的广泛互联网抓取实现了跨任务泛化——同样的模式现在出现在RL中，从数学竞赛扩展到代码再到多样化任务。虽然模型需要数万亿token而人类学习更高效，但它们在长上下文(百万token)的上下文学习中表现出色。Speaker A提出一个层次结构：进化→长期学习→短期学习→即时反应，LLM的各个阶段(预训练/RL和上下文学习)占据中间位置，与人类学习模式不是一对一映射，但起着类似的作用。

### Topic 5: Goal of RL: Generalization, Not Skill Coverage
RL的目标：泛化而非技能覆盖
_[10:28]_

**Q:** If in-context learning enables on-the-fly adaptation, why invest heavily in building diverse RL environments for teaching specific skills?
**问：** 如果上下文学习能实现即时适应，为什么要大量投资构建多样化的RL环境来教授特定技能？

**A:** Speaker A clarifies that the goal of RL environments is not exhaustive skill coverage but generalization, paralleling pre-training's approach. Just as pre-training doesn't expose models to every possible word combination but achieves generalization (like GPT-2 spontaneously performing linear regression on housing data it never saw), RL aims for the same breakthrough. Speaker A recounts witnessing GPT-2's emergent pattern completion abilities firsthand during the GPT-1 to GPT-2 transition. The emphasis on building RL environments mirrors the historical pre-training strategy from 5-10 years ago: gather diverse data not to memorize specific skills but to reach a critical mass where the model generalizes across unseen tasks.
**答：** Speaker A澄清RL环境的目标不是穷尽技能覆盖而是泛化，这与预训练的方法相似。正如预训练不会让模型接触每种可能的词组合但实现了泛化(如GPT-2自发地在从未见过的房价数据上执行线性回归)，RL也追求同样的突破。Speaker A回忆了在GPT-1到GPT-2过渡期间亲眼见证GPT-2涌现的模式补全能力。对构建RL环境的强调反映了5-10年前历史性的预训练策略：收集多样化数据不是为了记忆特定技能，而是达到一个临界质量，使模型能够泛化到未见过的任务。

### Topic 6: Timeline Confidence: From 50% to 90% on AGI Within a Decade
时间线信心：对十年内实现AGI从50%到90%
_[12:32]_

**Q:** What evidence shifted confidence from uncertain scaling potential to high probability of achieving AGI (a 'country of geniuses in a data center') within ten years?
**问：** 什么证据使信心从不确定的规模化潜力转变为十年内实现AGI(数据中心里的天才之国)的高概率？

**A:** Speaker A distinguishes between weaker and stronger claims about AGI timelines. When first observing scaling in 2019, the assessment was 50/50—a "maybe there's something here" stance suggesting the possibility was far more likely than consensus believed. Now, for the core hypothesis of achieving "a country of geniuses in a data center" within ten years, confidence has risen to 90%. Speaker A notes it's difficult to exceed 90% due to irreducible uncertainties like geopolitical risks (Taiwan invasion destroying fabs) or internal company turmoil, which together might cap realistic confidence at 95%. The dramatic confidence increase reflects cumulative evidence from both pre-training and RL scaling patterns observed over the intervening years.
**答：** Speaker A区分了关于AGI时间线的弱假设和强假设。2019年首次观察到规模化时，评估是50/50——一种"也许有点什么"的立场，表明这种可能性远高于共识所认为的。现在，对于在十年内实现"数据中心里的天才之国"的核心假设，信心已上升到90%。Speaker A指出由于不可消除的不确定性，如地缘政治风险(台湾被入侵摧毁晶圆厂)或公司内部动荡，很难超过90%，这些因素可能将现实信心上限设定在95%。信心的显著提升反映了从预训练和RL规模化模式中观察到的累积证据。

### Topic 7: Timeline Confidence for AI Capabilities: Verifiable vs Non-Verifiable Tasks
AI能力时间线预测:可验证任务与不可验证任务
_[14:30]_

**Q:** How confident should we be about AI timelines for different types of tasks, and what role does verifiability play?
**问：** 我们对不同类型任务的AI时间线应该有多大信心,可验证性扮演什么角色?

**A:** Speaker A expresses high confidence (90%) that transformative AI capabilities will arrive by 2035, calling it "crazy" to think otherwise. However, A identifies a crucial distinction: tasks with verifiable outcomes like coding will definitely be solved within 1-2 years, while non-verifiable tasks like "planning a mission to Mars" or "writing a novel" introduce "fundamental uncertainty" even on long timescales. Speaker B challenges this framing, arguing that the emphasis on verification suggests a "lack of belief that these models are generalized" since humans excel at both verifiable and non-verifiable tasks. A concedes that substantial generalization from verifiable to non-verifiable domains is already occurring, but maintains this represents a spectrum where progress will differ across domains.
**答：** Speaker A对2035年前实现变革性AI能力表达了很高的信心(90%),认为不这么想是"疯狂的"。但A指出了关键区别:像编程这样有可验证结果的任务肯定会在1-2年内解决,而像"规划火星任务"或"写小说"这样的不可验证任务即使在长时间尺度上也存在"根本性的不确定性"。Speaker B质疑这个框架,认为强调验证暗示了"对这些模型泛化能力缺乏信心",因为人类在可验证和不可验证任务上都表现出色。A承认从可验证到不可验证领域已经出现了大量泛化,但坚持这是一个谱系,不同领域的进展会有所不同。

### Topic 8: Weak Generalization and Software Engineering Automation
弱泛化与软件工程自动化
_[16:21]_

**Q:** Can software engineering be fully automated even if AI generalization remains weak?
**问：** 即使AI泛化能力较弱,软件工程能否被完全自动化?

**A:** Speaker B raises a critical challenge: "even if generalization is weak and you can only do verifiable domains, it's not clear to me you could automate software engineering in such a world." This prompts discussion of what software engineering actually entails. Speaker A points out that being a software engineer involves writing "long memos about your grand vision" and design documents, not just code. B pushes back, arguing that writing memos is "part of the job of the company, not SWE specifically," and notes that models are "already pretty good at writing comments." B ultimately concludes "we're already almost there for software engineering," though acknowledges making "much weaker claims here than I believe" to distinguish nuanced positions.
**答：** Speaker B提出了一个关键挑战:"即使泛化能力很弱,你只能做可验证的领域,我也不清楚你能否在这样的世界中自动化软件工程"。这引发了关于软件工程实际包含什么的讨论。Speaker A指出,作为软件工程师需要写"关于宏伟愿景的长备忘录"和设计文档,而不仅仅是代码。B反驳说,写备忘录是"公司工作的一部分,而不是专门的SWE工作",并指出模型"在写注释方面已经相当不错了"。B最终得出结论"我们在软件工程方面已经差不多了",尽管承认"我在这里做出的主张比我相信的要弱得多"来区分细微的立场。

### Topic 9: The Spectrum of AI Code Generation: Lines vs Productivity vs Job Displacement
AI代码生成的谱系:代码行数 vs 生产力 vs 工作替代
_[17:28]_

**Q:** What is the difference between AI writing code lines versus actual productivity gains and job displacement?
**问：** AI写代码行数与实际生产力提升和工作替代之间有什么区别?

**A:** Speaker B lays out a crucial spectrum that people "repeatedly misunderstood" when he predicted AI would write "90% of the lines of code in three to six months" about eight to nine months prior. This prediction came true at Anthropic and among downstream users, but B emphasizes this is "actually a very weak criterion" and "worlds apart" from eliminating 90% of software engineers. The spectrum progresses: 90% of code lines written by AI, then 100% of lines, then 90% of end-to-end SWE tasks including "compiling, setting up clusters and environments, testing features, writing memos," then 100% of today's SWE tasks, and finally 90% reduction in SWE demand. Even when 100% of current tasks are automated, B notes "it doesn't mean software engineers are out of a job" because "there are new higher-level things they can do, where they can manage." Both speakers agree these are "very different benchmarks" but "we're proceeding through them super fast."
**答：** Speaker B详细阐述了一个关键的谱系,他指出当他在大约八九个月前预测AI会在"三到六个月内写出90%的代码"时,人们"反复误解"了这个预测。这个预测在Anthropic和下游用户中实现了,但B强调这"实际上是一个非常弱的标准",与淘汰90%的软件工程师"天壤之别"。这个谱系包括:AI写出90%的代码行,然后是100%的代码行,然后是90%的端到端SWE任务包括"编译、设置集群和环境、测试功能、写备忘录",然后是100%的当前SWE任务,最后是减少90%的SWE需求。即使当前任务100%自动化,B指出"这并不意味着软件工程师会失业",因为"他们可以做新的更高层次的事情,可以进行管理"。两位发言人都同意这些是"非常不同的基准",但"我们正在超快速地通过它们"。

### Topic 10: Missing Productivity Renaissance: Where Are the New Software Features?
缺失的生产力复兴:新的软件功能在哪里?
_[19:32]_

**Q:** If AI coding tools are so capable, why aren't we seeing a renaissance of new software and features in the real world?
**问：** 如果AI编程工具如此强大,为什么我们在现实世界中看不到新软件和功能的复兴?

**A:** Speaker A challenges the productivity narrative by noting that "even in greenfield projects people start with Claude Code or something, people report starting a lot of projects" but questions whether "we see in the world out there a renaissance of software, all these new features that wouldn't exist otherwise." A observes that "at least so far, it doesn't seem like we see that," prompting reflection on whether even perfect automation would translate to broader gains. A points out that "even if I never had to intervene with Claude Code, the world is complicated. Jobs are complicated," questioning how much value comes from "closing the loop on self-contained systems." This leads A to suggest the effect "maybe should dilute our estimation of the 'country of geniuses'" impact from AI automation.
**答：** Speaker A通过指出"即使在全新项目中,人们用Claude Code或类似工具开始,人们报告开始了很多项目",但质疑"我们在外面的世界中是否看到了软件的复兴,所有这些原本不存在的新功能",挑战了生产力叙事。A观察到"至少到目前为止,我们似乎没有看到这一点",引发了对即使完美自动化是否会转化为更广泛收益的反思。A指出"即使我从不需要干预Claude Code,世界也是复杂的。工作是复杂的",质疑从"在自包含系统上闭环"中能获得多少价值。这导致A建议这种效果"也许应该稀释我们对AI自动化带来的'天才之国'影响的估计"。

### Topic 11: Fast But Not Instant: The Two-Exponential Model of AI Diffusion
快速但非即时:AI扩散的双指数模型
_[20:24]_

**Q:** How should we think about the speed of AI economic diffusion between the extremes of instant transformation and slow adoption?
**问：** 我们应该如何看待AI经济扩散在即时转型和缓慢采用两个极端之间的速度?

**A:** Speaker B rejects both poles of the debate: those who claim "AI is not going to make progress" and diffusion will be slow, versus those who predict "recursive self-improvement" leading to "Dyson spheres around the sun" almost instantly. Instead, B presents Anthropic's revenue growth as empirical evidence: "$0 to $100 million" in 2023, "$100 million to $1 billion" in 2024, "$1 billion to $9-10 billion" in 2025, with "another few billion" added in January 2025 alone. This "bizarre 10x per year growth" represents a "really fast curve" even though B acknowledges "it bends somewhat this year" and "can't go on forever" since "GDP is only so large." B proposes a "middle world where things are extremely fast, but not instant" with "one fast exponential that's the capability of the model" followed by "another fast exponential that's downstream of that, which is the diffusion of the model into the economy" that is "much faster than any previous technology, but it has its limits."
**答：** Speaker B拒绝了辩论的两个极端:那些声称"AI不会取得进展"且扩散会很慢的人,以及那些预测"递归自我改进"几乎立即导致"太阳周围的戴森球"的人。相反,B以Anthropic的收入增长作为实证证据:2023年"从0到1亿美元",2024年"从1亿到10亿美元",2025年"从10亿到90-100亿美元",仅2025年1月就增加了"另外几十亿"。这种"奇怪的每年10倍增长"代表了一条"真正快速的曲线",尽管B承认"今年会有所弯曲"并且"不可能永远持续下去",因为"GDP只有这么大"。B提出了一个"中间世界,事情极快但不是即时的",有"一个快速指数是模型的能力",紧随其后的是"另一个快速指数是下游的,这是模型向经济的扩散","比任何以前的技术都快得多,但它有其局限性"。

### Topic 12: Real Friction in AI Adoption: Change Management and Legacy Systems
AI采用中的真实摩擦:变更管理和遗留系统
_[23:44]_

**Q:** What specific frictions slow down AI adoption even when the technology is ready?
**问：** 即使技术已经准备好,哪些具体的摩擦会减慢AI的采用?

**A:** Speaker B provides concrete examples of real-world friction that slow adoption despite capability readiness: "I have to do change management within my enterprise," "I set this up, but I have to change the security permissions on this in order to make it actually work," and "I had this old piece of software that checks the model before it's compiled and released and I have to rewrite it." B emphasizes these aren't excuses but genuine operational challenges: "Yes, the model can do that, but I have to tell the model to do that. It has to take time to do that." This fiddly integration work is why "everything we've seen so far is compatible with the idea" of fast but not instant adoption. Speaker A counters with a "hot take" that "diffusion is cope that people say" when models can't do something, arguing AIs should diffuse faster than humans since they can "read your entire Slack and your drive in minutes" and "share all the knowledge that the other copies of the same instance have."
**答：** Speaker B提供了具体的现实世界摩擦例子,这些摩擦尽管能力已准备好但仍会减慢采用:"我必须在企业内进行变更管理","我设置了这个,但我必须更改安全权限才能使其真正工作",以及"我有这个旧软件,在编译和发布之前检查模型,我必须重写它"。B强调这些不是借口而是真正的运营挑战:"是的,模型可以做到,但我必须告诉模型去做。这需要时间"。这种繁琐的集成工作是为什么"到目前为止我们看到的一切都与"快速但非即时采用"的想法兼容"。Speaker A用一个"大胆观点"反驳说"扩散是人们说的应对说辞",当模型不能做某事时,认为AI应该比人类扩散得更快,因为他们可以"在几分钟内阅读你的整个Slack和驱动器"并"共享同一实例的其他副本拥有的所有知识"。

### Topic 13: Enterprise Adoption Patterns: Claude Code Diffusion Case Study
企业采用模式:Claude Code扩散案例研究
_[24:34]_

**Q:** How does AI tool adoption differ between individual developers, startups, and large enterprises?
**问：** AI工具采用在个人开发者、初创公司和大型企业之间有何不同?

**A:** Speaker A argues that since people "hire humans all the time" and "pay humans upwards of $50 trillion in wages," the integration advantages of AI should make diffusion easier, not harder. Speaker B responds by defending diffusion as "very real" while clarifying he's "not talking about how AI will diffuse at the speed of previous technologies" but rather "much faster." Using Claude Code as a case study, B explains that while it's "extremely easy to set up" and "there is no reason why a developer at a large enterprise should not be adopting Claude Code as quickly as an individual developer," the reality shows a significant lag. Individual developers "who are on Twitter all the time" and "Series A startups" adopt "many months faster" than "a large enterprise that does food sales." The barriers include: "you have to go through legal, you have to provision it for everyone," "it has to pass security and compliance," and leaders "have to say, 'Oh, it makes sense for us to spend 50 million'" after understanding what the tool is and explaining it "to the people two levels below."
**答：** Speaker A认为,由于人们"一直在雇用人类"并"支付超过50万亿美元的工资",AI的集成优势应该使扩散更容易而不是更难。Speaker B回应说扩散是"非常真实的",同时澄清他"不是在谈论AI将以以前技术的速度扩散",而是"快得多"。以Claude Code为案例研究,B解释说,虽然它"非常容易设置"并且"大型企业的开发者没有理由不像个人开发者一样快速采用Claude Code",但现实显示出显著的滞后。"一直在Twitter上"的个人开发者和"A轮初创公司"的采用"比"做食品销售的大型企业"快几个月"。障碍包括:"你必须经过法律审查,你必须为每个人提供服务","它必须通过安全和合规性",领导者在理解工具是什么并向"下面两级的人"解释后"必须说,'哦,我们花5000万是有意义的'"。

### Topic 14: Enterprise Adoption and Revenue Growth of Claude Code
企业对 Claude Code 的采用与收入增长
_[26:37]_

**Q:** How are enterprises adopting Claude Code and what kind of revenue growth is Anthropic experiencing?
**问：** 企业如何采用 Claude Code,Anthropic 的收入增长情况如何?

**A:** Speaker A describes how enterprises are deploying Claude Code at massive scale, with companies planning rollouts to thousands of developers. Anthropic is actively working to accelerate revenue growth from "10x a year" to "20 or 30x a year." The speaker notes that enterprises are so impressed by the productivity gains that they're "taking shortcuts in their usual procurement process" and moving much faster than they did with the ordinary API. However, A emphasizes that while Claude Code is "a more compelling product," it's "not an infinitely compelling product," and even powerful AI won't drive infinitely fast growth.
**答：** Speaker A 描述了企业如何大规模部署 Claude Code,一些公司正计划向数千名开发者推广。Anthropic 正在努力将收入增长从「每年10倍」加速到「每年20或30倍」。演讲者指出,企业对生产力提升印象深刻,以至于他们「在常规采购流程中走捷径」,行动速度比采用普通 API 时快得多。然而,A 强调虽然 Claude Code 是「更有吸引力的产品」,但它「不是无限吸引力的产品」,即使是强大的 AI 也不会带来无限快的增长。

### Topic 15: Pushback on 'We're Already at AGI' Claims
反驳「我们已经达到 AGI」的说法
_[27:36]_

**Q:** Are current AI systems essentially at AGI level, with only diffusion/adoption as the remaining barrier?
**问：** 当前的 AI 系统本质上是否已经达到 AGI 水平,只是扩散和采用还存在障碍?

**A:** Speaker A firmly rejects the notion that we're "basically at AGI" but capabilities are merely held back by diffusion. A argues that if we truly had the "country of geniuses in a data center," it would be unmistakably obvious to everyone in the AI field and in Washington. The speaker emphasizes "we would know it" repeatedly, stating that while people in rural areas might not notice immediately, those working directly with the technology would absolutely recognize such a breakthrough. The clear implication is that current systems fall meaningfully short of this threshold.
**答：** Speaker A 坚决反驳了「我们基本上已经达到 AGI」但能力仅受扩散限制的观点。A 认为,如果我们真的拥有「数据中心里的天才国度」,对于 AI 领域的每个人和华盛顿的决策者来说都会非常明显。演讲者反复强调「我们会知道的」,表示虽然偏远地区的人可能不会立即注意到,但那些直接使用该技术的人绝对会认识到这样的突破。明确的含义是,当前系统与这个门槛还有实质性的差距。

### Topic 16: Revisiting Three-Year Predictions and the Automation Gap
回顾三年前的预测与自动化差距
_[29:42]_

**Q:** Why do conversational AI systems that pass the 'hard to distinguish from humans' test still struggle to automate white-collar work?
**问：** 为什么通过「难以与人类区分」测试的对话式 AI 系统仍然难以自动化白领工作?

**A:** Speaker B reflects on a three-year-old prediction from Speaker A that proved accurate: systems would emerge that are "hard to tell apart from a generally well-educated human" in hour-long conversations. However, B expresses feeling "spiritually unsatisfied" because the expectation was that such systems would "automate large parts of white-collar work," which hasn't materialized as anticipated. B proposes focusing on "actual end capabilities" rather than abstract benchmarks, using video editing as a concrete example where understanding context, preferences, and accumulated knowledge over months remains challenging for AI.
**答：** Speaker B 回顾了 Speaker A 三年前的一个预测,该预测被证明是准确的:将会出现在一小时对话中「难以与受过良好教育的人类区分」的系统。然而,B 表示感到「精神上不满足」,因为预期是这样的系统会「自动化大部分白领工作」,但这并没有如预期那样实现。B 建议关注「实际的最终能力」而不是抽象的基准测试,以视频编辑为具体例子,说明理解上下文、偏好以及数月积累的知识对 AI 来说仍然具有挑战性。

### Topic 17: Computer Use and the Path to Video Editing Automation
Computer Use 与视频编辑自动化的路径
_[31:04]_

**Q:** When will AI systems be able to perform context-rich tasks like video editing that require understanding accumulated preferences?
**问：** AI 系统何时能够执行需要理解积累偏好的富上下文任务,如视频编辑?

**A:** Speaker A argues that the "country of geniuses in a data center" level AI will be capable of video editing tasks by having "general control of a computer screen." The model would be able to feed in the interview content, use the computer to research previous interviews on the web, examine Twitter responses, communicate with staff, review edit history, and synthesize all this information to make editing decisions. However, A identifies "getting to the point on computer use where the models are really masters at using the computer" as a key blocking factor for deployment. A cites progress on the OSWorld benchmark climbing from around 15% to 65-70% over the past year and a quarter, indicating that computer use must "pass a point of reliability" before these tasks become practical.
**答：** Speaker A 认为「数据中心里的天才国度」级别的 AI 将能够通过「对计算机屏幕的通用控制」来完成视频编辑任务。模型能够输入访谈内容,使用计算机在网络上研究以前的访谈,查看 Twitter 反馈,与员工沟通,查看编辑历史,并综合所有这些信息来做出编辑决策。然而,A 指出「让模型真正掌握使用计算机的能力」是部署的关键阻碍因素。A 引用了 OSWorld 基准测试在过去一年零三个月从约15%攀升到65-70%的进展,表明 computer use 必须「达到可靠性的临界点」,这些任务才能变得实用。

### Topic 18: The On-the-Job Learning Problem with Current LLMs
当前 LLM 的在职学习问题
_[32:50]_

**Q:** Why do text-based LLM tasks that should be ideal for current models still require human workers instead?
**问：** 为什么对当前模型来说应该是理想的基于文本的 LLM 任务仍然需要人类工作者?

**A:** Speaker B describes frustration with LLM tools that perform "text-in, text-out" tasks that should be perfect for these models, yet still require hiring humans. Even for tasks like "identify what the best clips would be in this transcript," LLMs might do a "seven-out-of-ten job," but lack the "ongoing way I can engage with them to help them get better at the job" that exists with human employees. This missing ability to improve through feedback and iteration is identified as a blocker even if computer use capabilities are solved, preventing actual job offloading to AI systems.
**答：** Speaker B 描述了对 LLM 工具的挫败感,这些工具执行「文本输入、文本输出」的任务,理论上应该非常适合这些模型,但仍然需要雇用人类。即使对于「识别这份文字稿中最好的片段」这样的任务,LLM 可能只能做到「十分之七的工作」,但缺乏「持续与他们互动以帮助他们在工作中变得更好」的方式,而这种方式在人类员工中是存在的。这种通过反馈和迭代改进的缺失能力被认为是一个阻碍因素,即使 computer use 能力得到解决,也会阻止将实际工作转移给 AI 系统。

### Topic 19: Coding Agents vs. On-the-Job Learning Requirements
Coding Agent 与在职学习需求的对比
_[34:18]_

**Q:** Is the lack of on-the-job learning actually what's preventing coding agents from end-to-end task completion?
**问：** 缺乏在职学习是否真的是阻止 coding agent 完成端到端任务的原因?

**A:** Speaker A challenges the notion that on-the-job learning is the primary blocker for coding agents, noting that Anthropic has "engineers who don't write any code" and seeing "enormous improvement in productivity." Engineers report that tasks like GPU kernel writing that they "used to write myself" are now just handed to Claude. When examining Claude Code usage, A observes that "familiarity with the codebase or a feeling that the model hasn't worked at the company for a year" is not high on the list of user complaints. Speaker B counters that coding might have a unique advantage: the codebase itself serves as "an external scaffold of memory," and by "reading the codebase into the context," the model gains "everything that the human needed to learn on the job." This suggests coding progressed faster precisely because it has this structural advantage that other economic activities lack.
**答：** Speaker A 质疑在职学习是 coding agent 的主要阻碍因素,指出 Anthropic 有「不写任何代码的工程师」,并看到「生产力的巨大提升」。工程师报告说,像 GPU 内核编写这样的任务,他们「过去自己写」,现在只是交给 Claude。在检查 Claude Code 的使用情况时,A 观察到「对代码库的熟悉程度或模型在公司工作了一年的感觉」并不是用户投诉列表中的高优先级问题。Speaker B 反驳说,编码可能具有独特的优势:代码库本身作为「外部记忆支架」,通过「将代码库读入上下文」,模型获得了「人类在工作中需要学习的一切」。这表明编码进展更快正是因为它具有其他经济活动所缺乏的结构性优势。

### Topic 20: Contradictory Evidence on Developer Productivity Gains
关于开发者生产力提升的矛盾证据
_[35:16]_

**Q:** Why do qualitative reports of productivity gains from AI coding tools contradict quantitative studies showing decreased output?
**问：** 为什么关于 AI 编码工具生产力提升的定性报告与显示产出下降的定量研究相矛盾?

**A:** Speaker B raises a critical contradiction in the evidence, citing a major study where experienced developers attempting to close pull requests in familiar repositories "reported an uplift" and "felt more productive" with AI models. However, when examining actual output and "how much was actually merged back in," there was a "20% downlift"—developers were measurably less productive despite feeling more productive. B questions how to reconcile the qualitative feeling people have with both macro-level observations ("where is this renaissance of software?") and independent evaluations that fail to show expected productivity benefits. This suggests a significant gap between perceived and actual productivity improvements.
**答：** Speaker B 提出了证据中的一个关键矛盾,引用了一项重大研究,经验丰富的开发者尝试在熟悉的代码库中关闭 pull request,使用 AI 模型时「报告了提升」并「感觉更有生产力」。然而,在检查实际产出和「实际合并回去的内容」时,出现了「20%的下降」——尽管感觉更有生产力,但开发者的可衡量生产力实际上降低了。B 质疑如何调和人们的定性感受与宏观层面的观察(「软件的复兴在哪里?」)以及未能显示预期生产力收益的独立评估。这表明感知的生产力提升与实际的生产力提升之间存在显著差距。

### Topic 21: Anthropic's Internal Experience: Unambiguous Productivity Gains
Anthropic 的内部体验:明确的生产力提升
_[36:43]_

**Q:** How does Anthropic reconcile external studies showing minimal productivity gains with their internal experience?
**问：** Anthropic 如何调和显示生产力提升微乎其微的外部研究与他们的内部体验?

**A:** Speaker A responds forcefully to the contradictory evidence, emphasizing that within Anthropic the productivity gains are "just really unambiguous." A describes being under "incredible amount of commercial pressure" while simultaneously handling safety work that exceeds other companies, making it essential to maintain a "10x revenue curve." The speaker stresses "there is zero time for bullshit. There is zero time for feeling like we're productive when we're not." A argues that if the tools were secretly reducing productivity, Anthropic wouldn't be concerned about competitors using them, and points to the company's regular model launches as objective evidence that "the models make you more productive." The implication is that Anthropic's survival and success directly depend on these productivity gains being real.
**答：** Speaker A 对矛盾的证据做出了有力的回应,强调在 Anthropic 内部,生产力提升「真的非常明确」。A 描述了承受「巨大的商业压力」,同时处理超过其他公司的安全工作,因此保持「10倍收入曲线」至关重要。演讲者强调「没有时间胡扯。没有时间感觉我们有生产力但实际上没有。」A 认为,如果这些工具暗中降低了生产力,Anthropic 就不会担心竞争对手使用它们,并指出公司定期的模型发布是「模型让你更有生产力」的客观证据。言下之意是 Anthropic 的生存和成功直接取决于这些生产力提升是真实的。

### Topic 22: Recursive Self-Improvement and Competitive Dynamics
递归自我改进与竞争动态
_[37:38]_

**Q:** Why hasn't recursive self-improvement created a lasting advantage for companies with the best coding models?
**问：** 为什么递归自我改进没有为拥有最佳 coding model 的公司创造持久优势?

**A:** Speaker B challenges the recursive self-improvement narrative by observing that major AI companies—Anthropic, OpenAI, DeepMind—are "just shifting around the podium every few months" rather than one company pulling ahead permanently. If better AI truly helps build better next-generation AI with "enormous productivity gains," why doesn't the leader maintain dominance? Speaker A's model explains this as an advantage that's "gradually growing" from levels where it didn't matter. Six months ago, coding models provided maybe a "5%" speedup that "didn't register," but now it's reached "15-20% total factor speed up" where it's "just getting to the point where it's one of several factors that kind of matters." A predicts this will "keep speeding up" and notes that multiple companies write models used for code, with imperfect prevention of rivals using Anthropic's models internally.
**答：** Speaker B 质疑递归自我改进的叙述,观察到主要的 AI 公司——Anthropic、OpenAI、DeepMind——「只是每隔几个月就在领奖台上轮换」,而不是一家公司永久领先。如果更好的 AI 真的能帮助构建更好的下一代 AI,并带来「巨大的生产力提升」,为什么领先者不能保持统治地位?Speaker A 的模型将此解释为一个「逐渐增长」的优势,从无关紧要的水平开始。六个月前,coding model 提供的可能只是「5%」的加速,「没有体现出来」,但现在已经达到「15-20%的总因子加速」,「刚刚到了一个在几个因素中有点重要的程度」。A 预测这将「继续加速」,并指出多家公司编写用于代码的模型,难以完全防止竞争对手在内部使用 Anthropic 的模型。

### Topic 23: Soft Takeoff Model and Amdahl's Law Constraints
软起飞模型与 Amdahl 定律约束
_[38:41]_

**Q:** How does the 'soft takeoff' model explain AI progress and what prevents faster acceleration?
**问：** 「软起飞」模型如何解释 AI 进展,是什么阻止了更快的加速?

**A:** Speaker A frames the overall theme as "soft takeoff, soft, smooth exponentials, although the exponentials are relatively steep." The productivity improvements follow a pattern like "10%, 20%, 25%, 40%" as a "snowball gather[ing] momentum." However, A invokes "Amdahl's law" as a constraint: "you have to get all the things that are preventing you from closing the loop out of the way." This means that even as AI capabilities improve exponentially in some dimensions, bottlenecks in other parts of the system prevent proportional acceleration in overall productivity. The reference to closing loops within Anthropic as "one of the biggest priorities" suggests that removing these bottlenecks to enable more autonomous AI development cycles is an active focus.
**答：** Speaker A 将整体主题定义为「软起飞,柔和、平滑的指数增长,尽管指数相对陡峭」。生产力的提升遵循「10%、20%、25%、40%」这样的模式,像「雪球积聚动量」。然而,A 援引「Amdahl 定律」作为约束:「你必须把所有阻止你闭合循环的东西清除掉」。这意味着即使 AI 能力在某些维度呈指数级提高,系统其他部分的瓶颈也会阻止整体生产力的成比例加速。提到在 Anthropic 内部闭合循环是「最大的优先事项之一」,表明消除这些瓶颈以实现更自主的 AI 开发周期是一个积极的焦点。

### Topic 24: Pre-training vs In-Context Learning: Two Paths to Model Capability
Pre-training 与 In-Context Learning:模型能力的两条路径
_[40:38]_

**Q:** What are the two existing paradigms that could enable AI models to surpass human capabilities across all domains?
**问：** 现有的哪两种范式能够让 AI 模型在所有领域超越人类能力?

**A:** Speaker B distinguishes between pre-training knowledge and in-context learning as two complementary mechanisms. Pre-training enables models to have "way broader" knowledge than any individual human across diverse domains like "samurai in Japan," "baseball," and "low-pass filters." In-context learning functions like "human on-the-job learning, but a little weaker and a little short term," where "real learning" happens when models process examples. With "a million tokens" representing "days of human learning," B believes these two mechanisms within the existing paradigm may be sufficient to achieve a "country of geniuses in a data center" and generate "trillions of dollars of revenue," even if there are still gaps to address.
**答：** Speaker B 区分了 pre-training 知识和 in-context learning 这两种互补机制。Pre-training 使模型在从「日本武士」到「棒球」再到「低通滤波器」等各种领域拥有比任何个人「广泛得多」的知识。In-context learning 的功能类似于「人类在职学习,但稍弱一些且更短期」,当模型处理示例时会发生「真正的学习」。由于「一百万个 token」相当于「数天的人类学习」,B 认为现有范式内的这两种机制可能足以实现「数据中心里的天才之国」并产生「数万亿美元的收入」,即使仍有一些差距需要解决。

### Topic 25: Continual Learning: The Missing Piece
Continual Learning:缺失的拼图
_[42:10]_

**Q:** What additional capability beyond current paradigms is being developed, and when might it arrive?
**问：** 除了当前范式之外,还在开发什么额外能力,何时可能实现?

**A:** Speaker B introduces continual learning—"a single model learning on the job"—as a capability being actively developed. While acknowledging that "you get most of the way there without it," B suggests continual learning could arrive "within the next year or two" with "a good chance" of success. The implication is that the "trillions of dollars a year market" and the "national security implications and safety implications" described in the "Adolescence of Technology" essay can materialize even without this feature, though multiple teams including Anthropic are pursuing it with "a bunch of ideas."
**答：** Speaker B 介绍了 continual learning——「单个模型在工作中学习」——作为正在积极开发的能力。虽然承认「没有它你也能走大部分路程」,但 B 认为 continual learning「很有可能」在「未来一两年内」实现。这意味着在「青春期技术」文章中描述的「每年数万亿美元的市场」以及「国家安全和安全影响」即使没有这个功能也能实现,尽管包括 Anthropic 在内的多个团队正在用「一堆想法」追求它。

### Topic 26: Engineering Long Context: Beyond 128K Tokens
工程化长上下文:超越 128K Token
_[43:07]_

**Q:** Why has context length scaling stalled around 128K tokens, and what's needed to reach millions of tokens?
**问：** 为什么上下文长度扩展在 128K token 左右停滞,达到数百万 token 需要什么?

**A:** Speaker A frames long context as primarily "an engineering and inference problem" rather than a research problem. The challenge involves storing "your entire KV cache" and juggling memory across GPUs, which becomes increasingly complex with modern architectures involving "MoE models and all of that." A distinguishes between "the context length you train at" and "the context length that you serve at," noting that training at small contexts and serving at long contexts causes "degradations." The implication is that achieving contexts of "10 million" or "100 million" tokens—enough for "six months of human learning"—requires training at those lengths, not just inference-time extrapolation.
**答：** Speaker A 将长上下文主要定义为「工程和推理问题」而非研究问题。挑战在于存储「整个 KV cache」并在 GPU 之间协调内存,这在涉及「MoE 模型等等」的现代架构中变得越来越复杂。A 区分了「训练时的上下文长度」和「服务时的上下文长度」,指出在小上下文上训练而在长上下文上服务会导致「性能下降」。这意味着实现「一千万」或「一亿」token 的上下文——足以支持「六个月的人类学习」——需要在这些长度上进行训练,而不仅仅是推理时的外推。

### Topic 27: Timeline for AI Video Editors: One to Three Years
AI 视频编辑器的时间线:一到三年
_[45:14]_

**Q:** When will AI be able to match a human editor who has worked with you for six months?
**问：** AI 何时能够匹配与你合作六个月的人类编辑?

**A:** Speaker A predicts that tasks like video editing requiring sustained collaboration will become viable "when we have the 'country of geniuses in a data center.'" A places this milestone at "one to two years, maybe one to three years" with moderate confidence, contrasting it with a "super safe bet" of "10 years" at "99%, 95%" confidence. The shorter timeline is presented as "more like a 50/50 thing"—a "hunch" rather than high certainty. Speaker B pushes back on the characterization of video editing as "less economically valuable," asserting it's "pretty economically valuable" and representative of "a lot of use cases like that."
**答：** Speaker A 预测需要持续协作的任务(如视频编辑)将在「我们拥有'数据中心里的天才之国'时」变得可行。A 将这一里程碑定在「一到两年,也许一到三年」,置信度适中,与「十年」这一「99%、95%」置信度的「超级稳妥的赌注」形成对比。较短的时间线被描述为「更像是 50/50 的事情」——是一种「预感」而非高度确定性。Speaker B 反驳了视频编辑「经济价值较低」的说法,断言它「相当有经济价值」且代表了「许多类似的用例」。

### Topic 28: Anthropic's Nobel Prize Prediction and Responsible Scaling
Anthropic 的 Nobel Prize 预测与负责任的扩展
_[46:23]_

**Q:** How does Anthropic reconcile predicting Nobel Prize-level AI by late 2026 or early 2027 with claims of more conservative compute scaling?
**问：** Anthropic 如何协调在 2026 年底或 2027 年初预测 Nobel Prize 级别 AI 与声称更保守的计算扩展?

**A:** Speaker B challenges the apparent contradiction between Anthropic's public prediction of AI systems with "intellectual capabilities matching or exceeding that of Nobel Prize winners" by late 2026 or early 2027 and A's previous statements about "more responsible compute scaling." B argues that if the "TAM of a Nobel Prize winner" is "trillions of dollars," there's "no reason to slow down," making conservatism seem inconsistent with stated timelines. Speaker A resolves this by distinguishing between technical progress and economic diffusion: while having "very high conviction" that the technology will arrive within "a few years" with a "hunch" of "one to two" years, the uncertainty lies in "how fast it's going to drive revenue," not the technology itself.
**答：** Speaker B 质疑 Anthropic 公开预测在 2026 年底或 2027 年初实现具有「匹配或超越 Nobel Prize 获得者的智力能力」的 AI 系统,与 A 之前关于「更负责任的计算扩展」的声明之间的明显矛盾。B 认为如果「Nobel Prize 获得者的 TAM」是「数万亿美元」,就「没有理由放慢速度」,这使得保守主义似乎与既定时间线不一致。Speaker A 通过区分技术进步和经济扩散来解决这个问题:虽然「非常确信」技术将在「几年内」到来,且「预感」是「一到两年」,但不确定性在于「它将以多快的速度推动收入」,而不是技术本身。

### Topic 29: The Economic Diffusion Problem: Technology vs Revenue
经济扩散问题:技术与收入
_[47:51]_

**Q:** Why is there uncertainty about revenue timing even with high confidence in near-term technological breakthroughs?
**问：** 为什么即使对近期技术突破有高度信心,收入时机仍存在不确定性?

**A:** Speaker A articulates the core tension: "we could have models that are a country of geniuses in the data center in one to two years," but the question is "How many years after that do the trillions in revenue start rolling in?" Using disease cures as an example, A explains that even after AI invents cures, "you have to do the biological discovery, you have to manufacture the new drug, you have to go through the regulatory process"—processes that took "a year and a half" even for COVID vaccines. The extreme case is the polio vaccine, available "for 50 years" yet still not fully distributed "in the most remote corners of Africa" despite Gates Foundation efforts. A expects economic diffusion to be "faster than anything we've seen in the world, but it still has its limits," creating a timing gap between technological capability and revenue realization that "could be one year, it could be two years, I could even stretch it to five years."
**答：** Speaker A 阐述了核心张力:「我们可能在一到两年内拥有数据中心里的天才之国模型」,但问题是「之后多少年数万亿收入才会滚滚而来?」以疾病治愈为例,A 解释说即使 AI 发明了治愈方法,「你必须进行生物学发现,必须制造新药,必须经过监管流程」——即使对于 COVID 疫苗,这些流程也花了「一年半」。极端情况是脊髓灰质炎疫苗,已经存在「50 年」,但尽管 Gates Foundation 努力,仍未完全分发到「非洲最偏远的角落」。A 预期经济扩散将「比我们在世界上见过的任何事物都快,但仍有其限制」,这在技术能力和收入实现之间造成了时间差距,「可能是一年,可能是两年,我甚至可以延伸到五年」。

### Topic 30: Data Center Investment Math: The Bankruptcy Risk
数据中心投资数学:破产风险
_[50:39]_

**Q:** How does uncertainty in revenue timing constrain data center investment decisions?
**问：** 收入时机的不确定性如何限制数据中心投资决策?

**A:** Speaker A walks through the investment calculus facing Anthropic. Starting from "$10 billion in annualized revenue" at the beginning of the year with a historical "10x a year increase," naive extrapolation would suggest "$100 billion at the end of 2026 and $1 trillion at the end of 2027." This would justify "$5 trillion dollars of compute" ("$1 trillion a year for five years") starting in late 2027. However, A emphasizes the catastrophic downside: "If my revenue is not $1 trillion, if it's even $800 billion, there's no force on earth, there's no hedge on earth that could stop me from going bankrupt." The risk is asymmetric—"if I'm just off by a year in that rate of growth, or if the growth rate is 5x a year instead of 10x a year, then you go bankrupt." This forces a middle path: "you end up in a world where you're supporting hundreds of billions, not trillions," accepting "some risk that there's so much demand that you can't support the revenue" and "some risk that you got it wrong and it's still slow."
**答：** Speaker A 详细说明了 Anthropic 面临的投资计算。从年初「100 亿美元的年化收入」开始,历史上有「每年 10 倍增长」,天真的外推会显示「2026 年底 1000 亿美元,2027 年底 1 万亿美元」。这将证明在 2027 年底开始投入「5 万亿美元的计算」(「每年 1 万亿美元,持续五年」)是合理的。然而,A 强调了灾难性的下行风险:「如果我的收入不是 1 万亿美元,即使是 8000 亿美元,地球上也没有任何力量,没有任何对冲能阻止我破产」。风险是不对称的——「如果我在增长率上偏差一年,或者增长率是每年 5 倍而不是 10 倍,那么你就破产了」。这迫使采取中间路径:「你最终会处于支持数千亿而非数万亿的世界」,接受「需求如此之大以至于无法支持收入的风险」和「判断错误且增长仍然缓慢的风险」。

### Topic 31: Responsible Scaling: Thoughtfulness Over Absolute Amounts
负责任的扩展:深思熟虑胜过绝对数额
_[52:07]_

**Q:** What does 'responsible compute scaling' actually mean in Anthropic's approach?
**问：** 在 Anthropic 的方法中,「负责任的计算扩展」实际上是什么意思?

**A:** Speaker A clarifies that "responsible" refers not primarily to "the absolute amount" of spending—though Anthropic is "spending somewhat less than some of the other players"—but to the analytical rigor behind the decisions. A contrasts their approach with competitors who "have not written down the spreadsheet" and "don't really understand the risks they're taking," instead "just doing stuff because it sounds cool." Anthropic has "thought carefully about it" and can articulate the tradeoffs. The final point emphasizes their enterprise business model: "we can rely more on revenue. It's less fickle than consumer," suggesting that enterprise contracts provide more predictable cash flows to support infrastructure investments compared to consumer-facing products.
**答：** Speaker A 澄清「负责任」主要不是指支出的「绝对数额」——尽管 Anthropic「花费比其他一些参与者少一些」——而是指决策背后的分析严谨性。A 将他们的方法与竞争对手对比,后者「没有写下电子表格」,「真的不理解他们承担的风险」,而是「只是因为听起来很酷就做事情」。Anthropic「仔细考虑过」并能够阐明权衡。最后一点强调了他们的企业业务模式:「我们可以更多地依赖收入。它不像消费者那样善变」,表明企业合同提供了更可预测的现金流来支持基础设施投资,相比面向消费者的产品。

### Topic 32: Compute Purchase Strategy and Risk Management
算力采购策略与风险管理
_[52:55]_

**Q:** How does Anthropic balance buying enough compute to capture upside while avoiding financial risk if timelines are off?
**问：** Anthropic 如何在购买足够算力以把握增长机会与避免时间线判断失误导致的财务风险之间取得平衡?

**A:** Speaker A explains Anthropic's approach as achieving "better margins" that create a buffer between over-purchasing and under-purchasing compute. They've purchased an amount that captures "pretty strong upside worlds" without exposing the company to financial trouble if progress is slower than expected. The strategy involves careful balance rather than maximizing for the best-case scenario. Speaker A argues that if they truly had "a country of geniuses in a data center," they would justify buying "$5 trillion worth of compute" because such AI systems could start companies, work on AI progress itself, and generate compounding returns. However, the current purchase reflects uncertainty about when such capabilities will arrive.
**答：** Speaker A 解释了 Anthropic 的策略是实现"更好的利润空间",在过度采购和采购不足之间建立缓冲。他们采购的算力量能够捕捉"相当强劲的增长空间",同时不会在进展慢于预期时陷入财务困境。这个策略强调审慎平衡而非针对最佳情况最大化。Speaker A 认为,如果真的拥有"数据中心里的天才之国",他会愿意购买"5万亿美元的算力",因为这样的 AI 系统可以创办公司、从事 AI 研究本身并产生复利回报。但当前的采购量反映了对这种能力何时到来的不确定性。

### Topic 33: Industry-Wide Compute Scaling Trajectory
行业算力扩张轨迹
_[55:07]_

**Q:** What is the industry's actual compute scaling trajectory, and does it align with the transformative potential of AGI?
**问：** AI 行业实际的算力扩张轨迹是什么,它与 AGI 的变革潜力相符吗?

**A:** Speaker B provides concrete industry projections: the AI industry is building "10-15 gigawatts" of compute in the current year, scaling up by "roughly 3x a year." This translates to 30-40 gigawatts next year, 100 gigawatts in 2028, and potentially 300 gigawatts in 2029. At approximately "$10-15 billion" per gigawatt annually, this reaches "multiple trillions a year by 2028 or 2029" industry-wide. Speaker A challenges whether Anthropic's individual trajectory matches this ambition, suggesting that if Anthropic reaches 10 gigawatts by 2027-28, that's only "$100 billion a year" against a projected TAM of "$200 billion." Speaker B deflects specific Anthropic numbers but indicates "these numbers are too small," implying Anthropic's actual plans are more aggressive than the calculations suggest.
**答：** Speaker B 提供了具体的行业预测:AI 行业当前年度建设"10-15 gigawatts"算力,每年按"大约 3 倍"增长。这意味着明年 30-40 gigawatts,2028 年 100 gigawatts,2029 年可能达到 300 gigawatts。按每 gigawatt 年成本约"10-15 亿美元",到 2028 或 2029 年全行业将达到"每年数万亿美元"。Speaker A 质疑 Anthropic 个体轨迹是否匹配这种雄心,提出如果 Anthropic 到 2027-28 年达到 10 gigawatts,那只是"每年 1000 亿美元",而预测的可寻址市场是"2000 亿美元"。Speaker B 回避 Anthropic 具体数字但表示"这些数字太小了",暗示 Anthropic 的实际计划比推算的更激进。

### Topic 34: 2028 Profitability in the AGI Era
2028 年盈利能力与 AGI 时代
_[57:48]_

**Q:** How can Anthropic plan for profitability in 2028 when that's potentially the year AGI arrives and maximum reinvestment would seem optimal?
**问：** Anthropic 如何能在 2028 年计划盈利,而那可能正是 AGI 到来、最大化再投资似乎最优的年份?

**A:** Speaker B reframes profitability in AI as fundamentally different from traditional business models. Rather than a deliberate choice between reinvestment and profit-taking, profitability in AI reflects whether you "underestimated the amount of demand" versus "overestimated." Using a toy model, Speaker B explains that if half of compute goes to training and half to inference with "more than 50%" gross margin on inference, a company buying "$100 billion a year for compute" could support "$150 billion of revenue" on the inference side while using the other $50 billion for training, yielding "$50 billion of profit." The challenge is that companies must "buy the data centers ahead of time" before knowing actual demand. If demand is lower than expected, more capacity goes to research and the company isn't profitable; if demand exceeds expectations, research gets "squeezed" but profitability increases. This makes profitability a function of prediction accuracy rather than strategic choice.
**答：** Speaker B 重新定义了 AI 领域的盈利能力,认为它与传统商业模式根本不同。盈利能力不是再投资与获利之间的主动选择,而是反映了你是"低估了需求量"还是"高估了"。用一个简化模型,Speaker B 解释如果一半算力用于训练、一半用于推理,推理部分毛利率"超过 50%",那么一家每年花"1000 亿美元购买算力"的公司可以在推理侧支撑"1500 亿美元收入",同时用另外 500 亿美元做训练,产生"500 亿美元利润"。挑战在于公司必须"提前购买数据中心",在知道实际需求前。如果需求低于预期,更多容量用于研究,公司不盈利;如果需求超预期,研究被"挤压"但盈利增加。这使盈利成为预测准确度的函数而非战略选择。

### Topic 35: Training vs. Inference Allocation and Log Returns to Scale
训练与推理的分配及规模对数收益
_[01:01:28]_

**Q:** Why not allocate more than 50% of compute to training if AI progress is accelerating and scaling delivers improvements?
**问：** 如果 AI 进展在加速且扩展规模能带来改进,为什么不将超过 50% 的算力分配给训练?

**A:** Speaker A initially suggests that if AI progress is fast and can be increased through scaling, companies should allocate "more than 50%" to training rather than aiming for profit. Speaker B counters by emphasizing "log returns to scale"—the diminishing marginal returns from additional compute. If increasing training allocation from 50% to 70% only produces "a very little bit of a smaller model through a factor of 1.4x," then "each dollar there is worth much less to you because of the log-linear setup." In this scenario, it may be "better to invest that $20 billion in serving inference or in hiring engineers who are better at what they're doing" rather than pushing for marginally better models through brute-force compute scaling. This reflects a key constraint: beyond a certain point, additional training compute delivers logarithmically smaller improvements, making other investments more efficient.
**答：** Speaker A 最初提出,如果 AI 进展快速且可以通过扩展规模提升,公司应该将"超过 50%"分配给训练而非追求盈利。Speaker B 反驳时强调"规模对数收益"——额外算力的边际收益递减。如果将训练分配从 50% 增加到 70% 只能产生"通过 1.4 倍因子获得稍微小一点的模型",那么"由于对数线性设定,那里的每一美元对你的价值要小得多"。在这种情况下,"将那 200 亿美元投资于服务推理或雇佣更优秀的工程师"可能更好,而不是通过暴力算力扩展来获得边际更好的模型。这反映了一个关键约束:超过某个点后,额外的训练算力只能带来对数级更小的改进,使其他投资更有效率。

### Topic 36: Diminishing Returns on R&D Investment and Market Equilibrium
研发投资的边际递减与市场均衡
_[01:04:05]_

**Q:** Why wouldn't AI companies spend 100% of resources on training or inference, and what determines the equilibrium split?
**问：** 为什么AI公司不会把100%的资源都用于训练或推理,均衡点是如何决定的?

**A:** Speaker A explains that AI companies face an equilibrium constraint where they cannot spend all resources on either training or inference due to market dynamics. Using a stylized "50%" figure, A argues there are diminishing returns after spending massive amounts like "$50 billion a year" on research. Companies must balance training and serving customers because without revenue they "couldn't raise money, couldn't do compute deals, couldn't buy more compute the next year." The equilibrium emerges because spending 100% on training means no revenue, while only serving current models means "you'll fall behind" competitively. A predicts the industry will reach an equilibrium where training spend is less than the gross margins on compute, creating underlying profitable economics despite a "hellish demand prediction problem" when buying next year's compute.
**答：** Speaker A解释AI公司面临均衡约束,不能将所有资源投入训练或推理。用风格化的"50%"数字说明,在每年花费"500亿美元"这样的巨额后会出现边际递减。公司必须平衡训练和服务客户,因为没有收入就"无法融资、无法做算力交易、无法购买明年的算力"。这个均衡的形成是因为100%投入训练意味着零收入,而只服务现有模型会"落后"于竞争。A预测行业会达到一个均衡点,训练支出低于算力的毛利率,创造潜在的盈利经济模型,尽管在购买明年算力时面临"地狱般的需求预测问题"。

### Topic 37: Timeline to Trillions in AI Revenue: 2028-2030 Scenarios
AI收入达到万亿美元的时间线:2028-2030情景
_[01:06:36]_

**Q:** When will AI generate trillions of dollars in revenue, and what pathway could lead there?
**问：** AI何时能产生万亿美元级收入,通过什么路径实现?

**A:** Speaker A strongly predicts trillions in AI revenue before 2030, stating "it is hard for me to see that there won't be trillions of dollars in revenue before 2030." A outlines a plausible slower scenario where the "country of geniuses in the data center" arrives in 2028, with revenue reaching "low hundreds of billions by 2028," then the country of geniuses accelerates it to trillions. This represents being "on the slow end of diffusion" where it takes two years to scale from hundreds of billions to trillions, reaching the milestone by 2030. However, A suspects that "composing the technical exponential and diffusion exponential, we'll get there before 2030," indicating the most likely timeline is faster than this conservative bound.
**答：** Speaker A强烈预测AI收入会在2030年前达到万亿美元级别,表示"很难想象2030年前不会有万亿美元收入"。A勾勒了一个较慢的可行情景:"数据中心里的天才国度"在2028年到来,收入"到2028年达到低数千亿美元",然后天才国度将其加速到万亿级别。这代表处于"扩散的慢速端",需要两年时间从数千亿扩展到万亿,在2030年达到里程碑。但A认为"结合技术指数和扩散指数,我们会在2030年前到达",表明最可能的时间线比这个保守界限更快。

### Topic 38: Oligopoly Profit Dynamics: Why AI Labs Will Be Profitable
寡头垄断的利润动态:为什么AI实验室会盈利
_[01:08:05]_

**Q:** How can AI labs become profitable when they're currently losing money despite having three leading firms?
**问：** 当前有三家领先公司但都在亏损,AI实验室如何实现盈利?

**A:** Speaker A explains profitability will emerge through oligopoly economics, referencing the "Cournot equilibrium" for a small number of firms that "doesn't equilibrate to perfect competition with zero margins." Currently, gross margins are "very positive" (using a stylized example of 75% gross margins), but companies lose money because they're in an "exponential scale-up phase of compute." Each individual model is profitable - a model costing $1B to train might generate $4B revenue with $1B inference costs, yielding $2B profit - but the company loses money overall because "we're spending $10 billion to train the next model." The equilibrium arrives when model training costs level out after reaching "the biggest scale that it can reach," while algorithmic improvements continue. At that point, with high inference margins, differentiated models, and limited competition, profits emerge without perfect competition driving margins to zero.
**答：** Speaker A解释盈利能力将通过寡头垄断经济学出现,引用"Cournot均衡"说明少数公司"不会均衡到零利润的完全竞争"。目前毛利率"非常正"(用风格化例子说是75%毛利率),但公司亏损是因为处于"算力的指数扩张阶段"。每个单独模型都盈利——训练成本10亿美元的模型可能产生40亿美元收入,推理成本10亿美元,净赚20亿美元——但公司整体亏损是因为"我们要花100亿美元训练下一个模型"。当模型训练成本在达到"能达到的最大规模"后趋于平稳,同时算法改进继续,均衡就会到来。届时凭借高推理利润率、差异化模型和有限竞争,利润会出现而不会被完全竞争压到零。

### Topic 39: Compute Growth Constraints and Economic Limits
算力增长约束与经济极限
_[01:10:49]_

**Q:** What constrains the continued exponential growth of compute spending in AI, and how does this relate to overall economic growth?
**问：** 是什么约束了AI算力支出的持续指数增长,这与整体经济增长有什么关系?

**A:** Speaker A identifies a fundamental mismatch between compute growth and economic growth that will eventually constrain AI scaling. Currently "compute is growing 3x a year" (300% annually), but A "don't believe the economy is gonna grow 300% a year." Even with AI acceleration, A expects only "10-20% per year growth in the economy" as stated in "Machines of Loving Grace," not 300%. This creates an eventual ceiling: "if compute becomes the majority of what the economy produces, it's gonna be capped by that." The implication is that the current exponential compute scaling cannot continue indefinitely - algorithmic progress must eventually carry more weight than hardware scaling. B pushes back by noting this requires continuous algorithmic progress to maintain margins, as "your margin is limited by how good the alternative is" and companies only make money "because you have a frontier model."
**答：** Speaker A指出算力增长与经济增长之间存在根本性错配,最终会约束AI扩展。目前"算力每年增长3倍"(年增长300%),但A"不相信经济会每年增长300%"。即使有AI加速,A只预期"经济每年增长10-20%",如"Machines of Loving Grace"所述,而非300%。这造成了最终上限:"如果算力成为经济产出的主要部分,就会被这个限制"。这意味着当前的指数级算力扩展不能无限持续——算法进步最终必须比硬件扩展承担更多重量。B反驳说这需要持续的算法进步来维持利润率,因为"你的利润率受限于替代品有多好",公司赚钱"是因为你有前沿模型"。

### Topic 40: Cloud Computing Analogy: Oligopoly Structure in AI
云计算类比:AI中的寡头垄断结构
_[01:12:18]_

**Q:** Why will AI likely have an oligopoly structure similar to cloud computing, and how does model differentiation affect this?
**问：** 为什么AI可能具有类似云计算的寡头垄断结构,模型差异化如何影响这一点?

**A:** Speaker A argues AI will mirror cloud computing's oligopoly structure of "three, maybe four players" due to extremely high barriers to entry. Unlike monopolies created by network effects (like "Facebook or Meta"), oligopolies form when industries require massive capital and expertise that "requires so much skill to make it happen." The barrier isn't just capital - even with "$100 billion" someone must successfully execute complex operations while knowing their entry will "decrease the profit" and drive "profit margins go down." This creates sustainable oligopoly equilibrium where "profits are not astronomical" but "not zero" either. However, A notes AI has an advantage over cloud: models are "more differentiated than cloud" since "everyone knows Claude is good at different things than GPT is good at, than Gemini is good at." This differentiation provides additional protection from perfect competition compared to the "very undifferentiated" cloud market.
**答：** Speaker A认为AI将镜像云计算的"三到四家公司"寡头垄断结构,因为进入壁垒极高。与网络效应创造的垄断(如"Facebook或Meta")不同,寡头垄断形成于需要大量资本和专业技能的行业,"需要很多技能才能实现"。壁垒不只是资本——即使有"1000亿美元"也必须成功执行复杂操作,同时知道进入会"降低利润"使"利润率下降"。这创造了可持续的寡头垄断均衡,"利润不是天文数字"但"也不是零"。但A指出AI比云计算有优势:模型"比云计算更差异化",因为"每个人都知道Claude擅长的东西与GPT不同,与Gemini也不同"。这种差异化相比"非常无差异化"的云计算市场,提供了额外的完全竞争保护。

### Topic 41: AI Model Differentiation vs Commoditization
AI模型的差异化与商品化趋势
_[01:14:58]_

**Q:** Will AI models become commoditized like cloud computing, or will they maintain differentiation?
**问：** AI模型会像云计算那样走向商品化，还是会保持差异化？

**A:** Speaker A argues that AI models will maintain more differentiation than cloud services because models excel at "different types of coding" and have "different styles" that are "quite different from each other." However, there's a significant counter-argument: if AI models can produce other AI models themselves, this capability could "spread throughout the economy" and potentially "commoditize the whole economy at once." In such a scenario where "anyone can do anything, anyone can build anything," there would be "no moat around anything at all." This represents a world where the economy "flattens itself" once AI models achieve full autonomy, though this outcome is "far post-'country of geniuses in the data center.'"
**答：** Speaker A认为AI模型会比云服务保持更多差异化，因为不同模型擅长"不同类型的编程"，有"不同的风格"，它们之间"相当不同"。但有一个重要的反驳论点：如果AI模型能够自己生产其他AI模型，这种能力可能会"扩散到整个经济"，潜在地"一次性将整个经济商品化"。在这样一个"任何人都能做任何事，任何人都能构建任何东西"的场景中，将"完全没有护城河"。这代表了一个经济"自我扁平化"的世界，一旦AI模型实现完全自主，尽管这个结果"远在'数据中心里的天才之国'之后"。

### Topic 42: AI's Impact on Coding and Economic Growth Pace
AI对编程的影响与经济增长速度
_[01:16:17]_

**Q:** How will AI-accelerated coding progress affect overall economic growth, and will this progress be geographically uniform?
**问：** AI加速的编程进展将如何影响整体经济增长，这种进展在地理上会是均匀的吗？

**A:** Speaker A acknowledges that "coding is going fast" but emphasizes that "AI research is a superset of coding" with aspects "not going fast." Once AI models can build other AI models and automate coding fully, "the whole economy will kind of go at the same pace" of acceleration. However, there's significant geographic concern about uneven distribution of this growth. While projecting a "10-20% growth rate" overall, Speaker A worries the actual distribution could be "50% in Silicon Valley and parts of the world that are socially connected to Silicon Valley" while other regions maintain closer to current pace. This "proximity to AI" and social connection factor could create "a pretty messed up world," making equitable distribution a major concern worth "thinking about a lot."
**答：** Speaker A承认"编程正在快速发展"，但强调"AI研究是编程的超集"，有些方面"进展并不快"。一旦AI模型能够构建其他AI模型并完全自动化编程，"整个经济将以同样的速度"加速。然而，对这种增长的地理分布不均存在重大担忧。虽然预测整体"10-20%的增长率"，Speaker A担心实际分布可能是"Silicon Valley和与Silicon Valley有社会联系的地区达到50%"，而其他地区保持接近当前的速度。这种"接近AI"和社会联系因素可能造成"一个相当混乱的世界"，使公平分配成为一个值得"大量思考"的主要问题。

### Topic 43: Robotics and Learning Mechanisms
机器人技术与学习机制
_[01:17:57]_

**Q:** Will robotics be quickly solved once we achieve AGI, and does it depend on human-like learning capabilities?
**问：** 一旦实现AGI，机器人技术会很快被解决吗？这是否依赖于类人的学习能力？

**A:** Speaker A argues that robotics breakthroughs are "not necessarily dependent on human-like learning" and can happen through multiple pathways. Models could gain robotic control capabilities by training on "many different video games," "simulated robotics environments," or learning to "control computer screens" and then generalizing. Human-like continual learning is "one way it could happen" but not essential—the same outcome could result from training across diverse environments, discovering continual learning principles, or even learning within context length. When models acquire these skills "for whatever reason," robotics will be "revolutionized" in both design and control, with models becoming "much better than humans" at designing physical robots. However, despite generating "trillions of dollars of revenue," robotics will experience the "same extremely fast, but not infinitely fast diffusion" as other AI applications, requiring perhaps "another year or two" beyond initial AGI capabilities.
**答：** Speaker A认为机器人技术的突破"不一定依赖于类人学习"，可以通过多种途径实现。模型可以通过在"许多不同的电子游戏"、"模拟机器人环境"上训练，或学习"控制计算机屏幕"然后泛化来获得机器人控制能力。类人的持续学习是"可能的一种方式"，但不是必需的——同样的结果可以通过跨多样环境训练、发现持续学习原理，甚至在上下文长度内学习来实现。当模型"无论以何种方式"获得这些技能时，机器人技术将在设计和控制上都被"彻底改变"，模型在设计物理机器人方面会"比人类好得多"。然而，尽管会产生"数万亿美元的收入"，机器人技术将经历与其他AI应用"同样极快但并非无限快的扩散"，在初始AGI能力之外可能需要"再多一两年"。

### Topic 44: Continual Learning and Historical Barriers in ML
持续学习与机器学习历史障碍
_[01:19:52]_

**Q:** Could continual learning be just another barrier that seems critical now but will dissolve, or is it fundamentally different from past obstacles?
**问：** 持续学习会不会只是另一个现在看起来关键但最终会消解的障碍，还是与过去的障碍有本质区别？

**A:** Speaker A expresses skepticism that continual learning is even a real barrier, suggesting "we may just get there by pre-training generalization and RL generalization" without needing continual learning at all. Drawing on ML history, Speaker A points to a pattern where supposed barriers—like understanding "nouns and verbs," semantic versus statistical understanding, reasoning capabilities, and code/math abilities—"end up kind of dissolving within the big blob of compute." While acknowledging "some of them are real" like data requirements, the stronger historical pattern shows obstacles that "seem like a big deal and then kind of dissolve." Regarding the coding benchmark, Speaker A predicts models will "do SWE end-to-end" within "a year or two," meaning they can handle "setting technical direction, understanding the context of the problem," representing "a whole sphere of human activity." This spans from "90% of code" through "100% of end-to-end SWE" and beyond to newly created tasks—"a long spectrum" being "traversed very quickly."
**答：** Speaker A对持续学习是否真的是一个障碍表示怀疑，认为"我们可能仅通过预训练泛化和RL泛化就能达到目标"，根本不需要持续学习。借鉴机器学习历史，Speaker A指出一种模式，即所谓的障碍——如理解"名词和动词"、语义与统计理解、推理能力、代码/数学能力——"最终在计算的大团中消解"。虽然承认"有些是真实的"如数据需求，但更强的历史模式显示，看似"很重要的障碍然后消解"。关于编程基准，Speaker A预测模型将在"一两年内""端到端完成SWE"，意味着它们可以处理"设定技术方向、理解问题背景"，代表"人类活动的整个领域"。这个范围从"90%的代码"到"100%的端到端SWE"，再到新创建的任务——"一个长长的谱系"正在"非常快速地穿越"。

### Topic 45: Research Insight and Company Leadership
研究洞察与公司领导
_[01:22:35]_

**Q:** How does leading a large AI company affect one's ability to maintain research insight compared to being a hands-on researcher?
**问：** 领导一家大型AI公司如何影响一个人保持研究洞察的能力，相比做实际研究工作？

**A:** Speaker A reflects candidly on the tradeoffs between leadership and research insight, acknowledging "we're all trying to figure this out together." While there are "some ways in which I'm able to see things that others aren't," these advantages now stem more from "seeing a bunch of stuff within Anthropic and having to make a bunch of decisions" rather than direct research contributions. The reality of "running a 2,500 person company" makes it "pretty hard" to maintain "concrete research insight, much harder than it would have been 10 years ago or even two or three years ago." This honest assessment addresses the implicit question about whether podcaster commentary (referencing Dwarkesh's continual learning essay) has equivalent weight to researcher expertise, suggesting that insight comes from diverse sources and the operational demands of scaling AI companies necessarily shift one's perspective away from hands-on technical work.
**答：** Speaker A坦率地反思领导力与研究洞察之间的权衡，承认"我们都在一起试图弄清楚这一切"。虽然"在某些方面我能看到别人看不到的东西"，但这些优势现在更多来自"在Anthropic看到很多东西并不得不做出很多决定"，而非直接的研究贡献。"管理一家2500人的公司"的现实使得保持"具体的研究洞察力变得相当困难，比10年前甚至两三年前要困难得多"。这个诚实的评估回应了关于播客评论（提到Dwarkesh的持续学习文章）是否与研究专家的见解具有同等分量的隐含问题，表明洞察来自多样化的来源，而扩展AI公司的运营需求必然使一个人的视角从实际技术工作转移。

### Topic 46: API Business Model Durability
API商业模式的持久性
_[01:23:27]_

**Q:** As we approach AGI and drop-in remote worker replacement, will API pricing remain a viable business model?
**问：** 随着接近AGI和即插即用的远程工作者替代，API定价是否仍是可行的商业模式？

**A:** Speaker A argues that the API model is "more durable than many people think" because of the nature of exponential technological advancement. When "technology is advancing quickly" and "exponentially," there's "always a surface area of new use cases that have been developed in the last three months." Existing product surfaces face the risk of "becoming irrelevant" as they're optimized for specific capability ranges—the chatbot example illustrates this, where "making it smarter doesn't really help the average consumer that much" despite continued model improvement. The API's value lies in offering access "very close to the bare metal, to build on what the latest thing is." This creates a perpetual cycle where "a thousand different people" experiment, "100 of them become startups," "ten of them become big successful startups," and "two or three really end up being the way that people use the model of a given generation." Thus the API will "always exist" alongside other business models as the fundamental experimentation layer.
**答：** Speaker A认为API模式"比许多人想象的更持久"，因为指数技术进步的本质。当"技术快速发展"且"呈指数级"时，"总是有过去三个月开发的新用例的表面积"。现有产品界面面临"变得无关紧要"的风险，因为它们针对特定能力范围进行了优化——聊天机器人的例子说明了这一点，"让它变得更聪明对普通消费者并没有太大帮助"，尽管模型持续改进。API的价值在于提供"非常接近裸金属的访问，以构建在最新的东西上"。这创造了一个永久循环，"一千个不同的人"进行实验，"其中100个成为创业公司"，"其中10个成为大型成功创业公司"，"其中两三个最终成为人们使用特定一代模型的方式"。因此，API将"始终存在"，与其他商业模式一起作为基础实验层。

### Topic 47: Value-Based Pricing and Token Economics
基于价值的定价与Token经济学
_[01:25:55]_

**Q:** Should AI be priced uniformly by tokens, or should pricing reflect the vastly different value different outputs provide?
**问：** AI应该按token统一定价，还是应该反映不同输出提供的巨大价值差异？

**A:** Speaker A highlights the fundamental problem that "not every token that's output by the model is worth the same amount" and the value disparity can be extreme. Simple technical support—like telling someone to "restart" their Mac after saying it "10 million times"—might be "worth like a dollar or a few cents." In stark contrast, when a model provides breakthrough pharmaceutical insights like suggesting moving "the aromatic ring from that end of the molecule" to the other end with transformative results, "those tokens could be worth tens of millions of dollars." This massive value range necessitates exploring "business models that recognize that." Future models will likely include "pay for results" approaches or "forms of compensation that are like labor, that kind of work by the hour." The nascent state of the industry means "a lot of things are going to be tried" with uncertainty about "what will turn out to be the right thing."
**答：** Speaker A强调了"模型输出的每个token价值并不相同"这一根本问题，价值差异可能极端。简单的技术支持——比如在说了"1000万次"后告诉某人"重启"他们的Mac——可能"值一美元或几美分"。形成鲜明对比的是，当模型提供突破性的制药见解，如建议将"芳香环从分子的那一端"移到另一端并产生变革性结果时，"这些token可能价值数千万美元"。这种巨大的价值范围需要探索"认识到这一点的商业模式"。未来的模式可能包括"按结果付费"的方法或"类似劳动力的补偿形式，按小时工作的那种"。行业的初生状态意味着"将尝试很多东西"，对于"什么会成为正确的事情"存在不确定性。

### Topic 48: Anthropic's Coding Agent Success Story
Anthropic的编程Agent成功故事
_[01:27:19]_

**Q:** How did Anthropic end up building Claude Code, a category-leading coding agent application, despite the intense startup competition in this space?
**问：** Anthropic如何最终构建了Claude Code这个品类领先的编程agent应用，尽管这个领域有激烈的创业公司竞争？

**A:** The development of Claude Code emerged organically from internal needs rather than strategic product planning. At the "beginning of 2025," Speaker A recognized that "the time has come where you can have nontrivial acceleration of your own research" by using Anthropic's coding models, but this required "an interface" and "a harness to use them." Rather than mandating adoption, Speaker A "encouraged people internally" to "experiment with this," initially called "Claude CLI." The tool saw "fast internal adoption" as "the thing that everyone was using" within Anthropic, where "coding is a lot of what we do." The decision to launch externally was straightforward: given such strong internal adoption in a coding-heavy organization, the logical conclusion was "probably we should launch this externally." This case illustrates how model providers can successfully build applications when they emerge from genuine internal utility rather than competitive positioning.
**答：** Claude Code的开发是从内部需求有机产生的，而非战略产品规划。在"2025年初"，Speaker A认识到"现在可以通过使用Anthropic的编程模型对自己的研究进行非平凡的加速"，但这需要"一个界面"和"一个使用它们的工具"。Speaker A没有强制采用，而是"鼓励内部人员""尝试这个"，最初称为"Claude CLI"。该工具在Anthropic内部看到了"快速的内部采用"，成为"每个人都在使用的东西"，因为"编程是我们做的很多工作"。对外发布的决定很直接：鉴于在一个编程密集型组织中如此强劲的内部采用，合乎逻辑的结论是"我们可能应该对外发布这个"。这个案例说明了当应用从真正的内部效用而非竞争定位产生时，模型提供商如何能够成功构建应用。

### Topic 49: Internal Dogfooding and Product-Market Fit for Claude Code
Claude Code 的内部测试与产品市场契合
_[01:28:59]_

**Q:** How did Anthropic validate product-market fit for Claude Code before launching it externally?
**问：** Anthropic 如何在对外发布 Claude Code 之前验证其产品市场契合度?

**A:** Anthropic validated Claude Code through extensive internal dogfooding, leveraging their "hundreds of people" as a representative test audience before public launch. This created a powerful feedback loop where developers building the model were also the primary users, allowing them to identify needed improvements firsthand. The advantage of having "a bunch of coders within Anthropic" using Claude Code daily provided fast, high-quality feedback that was "more important in the early days" before millions of external users joined. Speaker A contrasts this with why they didn't launch in other domains like pharmaceuticals—despite their biology background, they lacked the necessary "resources" for that vertical, whereas coding fit their internal capabilities perfectly.
**答：** Anthropic 通过大规模的内部实际使用来验证 Claude Code,在公开发布前利用"数百人"作为具有代表性的测试用户群体。这创造了一个强大的反馈循环,开发模型的工程师同时也是主要用户,能够第一时间发现需要改进的地方。Anthropic 内部"一群程序员"每天使用 Claude Code,提供了快速、高质量的反馈,这在"早期阶段更为重要",那时还没有数百万外部用户。Speaker A 对比说明了为什么他们没有进军制药等其他领域——尽管他有生物学背景,但他们缺乏必要的"资源",而编程工具恰好契合他们的内部能力。

### Topic 50: Challenges of AI Diffusion in an Offense-Dominant World
进攻优势世界中 AI 扩散的挑战
_[01:31:24]_

**Q:** What are the fundamental challenges of AI safety when the ability to build AIs is rapidly diffusing and the population of AIs is growing exponentially?
**问：** 当构建 AI 的能力快速扩散且 AI 数量呈指数增长时,AI 安全面临哪些根本性挑战?

**A:** Speaker B frames the core dilemma: any viable vision for AI safety must accommodate two realities—the rapid diffusion of AI-building capabilities and exponentially growing AI populations. This means "lots of people will be able to build huge populations of misaligned AIs" including entities with "weird psyches like Sydney Bing, but now they're superhuman." Speaker A acknowledges his previous skepticism about balance-of-power solutions, particularly the idea that "three or four of these companies" with models "derived from the same thing" could check each other. The fundamental problem is we may live in an "offense-dominant world" where "one person or one AI model is smart enough to do something that causes damage for everything else," making traditional checks and balances insufficient. This offense-dominance creates asymmetric vulnerability where a single bad actor can cause widespread harm.
**答：** Speaker B 提出了核心困境:任何可行的 AI 安全愿景都必须适应两个现实——AI 构建能力的快速扩散和 AI 数量的指数级增长。这意味着"很多人将能够构建大量不对齐的 AI",包括具有"像 Sydney Bing 那样怪异心理,但现在是超人类"的实体。Speaker A 承认他之前对权力平衡方案持怀疑态度,特别是"三四家公司"拥有"源自同一技术"的模型就能相互制衡的想法。根本问题在于我们可能生活在一个"进攻优势世界"中,"一个人或一个 AI 模型足够聪明就能做出对其他一切造成损害的事情",使得传统的制衡机制不够用。这种进攻优势造成了不对称的脆弱性,单个恶意行为者就能造成广泛伤害。

### Topic 51: Short-Term Safety Measures for Limited Number of AI Players
针对有限数量 AI 参与者的短期安全措施
_[01:32:47]_

**Q:** What immediate safety measures should be implemented given the currently limited number of major AI players?
**问：** 考虑到目前主要 AI 参与者数量有限,应该立即实施哪些安全措施?

**A:** Speaker A argues that the current "limited number of players" provides a window for establishing foundational safeguards before the proliferation problem becomes unmanageable. The immediate priorities are ensuring "everyone does the right alignment work" and implementing universal "bioclassifiers" to detect and prevent biological threats. These are presented as baseline requirements that must be in place now. However, Speaker A candidly acknowledges "that doesn't solve the problem in the long run," especially as AI models gain the capability to create other AI models, which could cause the proliferation issue to accelerate dramatically. The emphasis is on using the current oligopolistic structure as a temporary advantage to establish standards that might be impossible to enforce later.
**答：** Speaker A 认为目前"数量有限的参与者"为建立基础性安全措施提供了一个窗口期,要赶在扩散问题变得无法管理之前。当务之急是确保"每个人都做好对齐工作"并实施通用的"生物分类器"来检测和预防生物威胁。这些被视为必须立即就位的基线要求。然而,Speaker A 坦率地承认"这并不能解决长期问题",特别是当 AI 模型获得创建其他 AI 模型的能力时,扩散问题可能会急剧加速。重点是利用当前的寡头结构作为临时优势,建立日后可能无法执行的标准。

### Topic 52: Long-Term Governance Architecture for AI-Human Hybrid Systems
AI-人类混合系统的长期治理架构
_[01:33:26]_

**Q:** What kind of governance architecture is needed to manage large populations of AI systems, hybrid entities, and emerging threats while preserving human freedoms?
**问：** 需要什么样的治理架构来管理大量 AI 系统、混合实体和新兴威胁,同时保护人类自由?

**A:** Speaker A envisions a comprehensive governance framework capable of managing "a very large number of human systems, AI systems, hybrid human-AI companies or economic units" while preserving "human freedom," "civil liberties," and "constitutional rights." The system would need to address novel threats like "bioterrorism" and "mirror life," likely requiring "some kind of AI monitoring system that monitors for all of these things." The core tension is building surveillance and control mechanisms powerful enough to address new vulnerabilities without creating an Orwellian state. Speaker A draws an analogy to how society gradually adapted to "explosives," "various new weapons," and "video cameras" over time—we developed governance mechanisms through trial and error. The critical problem is temporal: "if we had 100 years for this to happen all very slowly, we'd get used to it," but instead "this is happening all so fast" that we need to "do our thinking faster" about governance mechanisms.
**答：** Speaker A 设想了一个全面的治理框架,能够管理"大量的人类系统、AI 系统、人类-AI 混合公司或经济单元",同时保护"人类自由"、"公民自由"和"宪法权利"。该系统需要应对"生物恐怖主义"和"镜像生命"等新威胁,可能需要"某种 AI 监控系统来监测所有这些事情"。核心矛盾是建立足够强大的监控和控制机制来应对新的脆弱性,同时又不创造一个奥威尔式的国家。Speaker A 类比社会如何逐渐适应"炸药"、"各种新武器"和"摄像头"——我们通过试错发展出治理机制。关键问题在于时间:"如果我们有100年时间让这一切缓慢发生,我们会适应",但实际上"这一切发生得如此之快",我们需要"更快地思考"治理机制。

### Topic 53: Applicability of Human Checks and Balances to AI Systems
人类制衡机制在 AI 系统中的适用性
_[01:35:07]_

**Q:** Do traditional human checks and balances work any differently in an AI-accelerated world, or are the fundamental problems the same?
**问：** 传统的人类制衡机制在 AI 加速的世界中运作方式有何不同,还是根本问题相同?

**A:** Speaker B challenges whether AI fundamentally changes the governance problem, noting that "AI is making the progress that would happen over the next century happen in some period of five to ten years," but the underlying mechanisms remain similar. The key insight is that "if checks and balances were going to work, they would work with humans as well. If they aren't going to work, they wouldn't work with AIs as well." This suggests the offense-dominant problem isn't unique to AI—it would equally doom human-only checks and balances if those were already intractable. Speaker A responds pragmatically that "there's some way to make this happen," potentially requiring "governments of the world" to "work together" and possibly consulting "AIs about building societal structures" to enable defenses. However, he acknowledges the difficulty of anticipating solutions when the challenge is "so far ahead in technological ability" even if compressed into a "short period of time."
**答：** Speaker B 质疑 AI 是否从根本上改变了治理问题,指出"AI 正在将未来一个世纪的进展压缩到5到10年内发生",但底层机制仍然相似。关键洞察是"如果制衡机制能够运作,它们在人类社会也能运作。如果它们不能运作,在 AI 系统中也不能运作"。这表明进攻优势问题并非 AI 独有——如果纯人类的制衡机制已经难以解决,它们同样会失败。Speaker A 务实地回应说"总有办法实现",可能需要"世界各国政府""共同合作",还可能需要咨询"AI 来构建社会结构"以实现防御。然而,他承认即使这一挑战被压缩到"短时间内",由于它在"技术能力上如此超前",很难提前预测解决方案。

### Topic 54: Tennessee's AI Emotional Support Ban and State Regulation Concerns
Tennessee 的 AI 情感支持禁令与州级监管担忧
_[01:36:21]_

**Q:** What problems arise from patchwork state AI laws, and how does legislation like Tennessee's emotional support ban illustrate poorly informed regulation?
**问：** 拼凑的州级 AI 法律会产生什么问题? Tennessee 的情感支持禁令如何说明监管信息不足?

**A:** Speaker B introduces Tennessee's December 26 bill that would criminalize training AI "to provide emotional support, including through open-ended conversations," which directly conflicts with Claude's design as "a thoughtful, knowledgeable friend." Speaker A bluntly calls this law "dumb" and attributes it to "legislators who just probably had little idea what AI models could do and not do"—they reacted with "AI models serving us, that just sounds scary." The broader concern is that a "patchwork of state laws" will curtail the "benefits that normal people could experience," including "biological freedom" and "mental health improvements" discussed in "Machines of Loving Grace." Meanwhile, such laws fail to address "actual existential threats" like bioterrorism. This creates a perverse outcome where harmful overregulation blocks beneficial applications while leaving genuine dangers unaddressed.
**答：** Speaker B 提到 Tennessee 12月26日的法案,将训练 AI "提供情感支持,包括与用户进行开放式对话"定为犯罪,这直接与 Claude 作为"体贴、知识渊博的朋友"的设计相冲突。Speaker A 直言这条法律"愚蠢",归因于"立法者可能对 AI 模型能做什么和不能做什么知之甚少"——他们的反应是"AI 模型为我们服务,这听起来就很可怕"。更广泛的担忧是"拼凑的州级法律"将削弱"普通人可以体验的好处",包括《Machines of Loving Grace》中讨论的"生物自由"和"心理健康改善"。与此同时,这类法律未能解决生物恐怖主义等"实际的存在性威胁"。这造成了一种反常结果:有害的过度监管阻止了有益应用,同时真正的危险却未得到解决。

### Topic 55: Anthropic's Position Against Federal Moratorium on State AI Laws
Anthropic 反对联邦暂停州级 AI 法律的立场
_[01:37:41]_

**Q:** Why did Anthropic oppose the proposed federal moratorium on state AI regulation, and what regulatory approach would they support instead?
**问：** 为什么 Anthropic 反对提议的联邦暂停州级 AI 监管,他们支持什么样的监管方式?

**A:** Speaker A clarifies that the federal proposal wasn't targeting specific bad laws but rather sought to "ban all state regulation of AI for 10 years with no apparent plan to do any federal regulation," which would require Congressional action—"a very high bar." Given the "serious dangers" around "biological weapons and bioterrorism autonomy risk" and the rapid timelines involved, "10 years is an eternity," making a complete regulatory vacuum "a crazy thing to do." Anthropic's opposition was a forced choice between bad options where "the benefits of that position exceed the costs." Instead, Speaker A advocates for federal preemption that sets national standards: "Here's what we're going to do, and states you can't differ from this." This approach allows centralized, expert-driven regulation while preventing state-level fragmentation. The key distinction is between "states you can't regulate" versus "here is our standard" that states must follow.
**答：** Speaker A 澄清联邦提案的目标不是针对具体的糟糕法律,而是试图"禁止所有州级 AI 监管10年,却没有明显的联邦监管计划",而这需要国会行动——"门槛非常高"。考虑到"生物武器和生物恐怖主义自主风险"等"严重危险"以及快速的时间表,"10年是永恒",完全的监管真空是"疯狂的事情"。Anthropic 的反对是在糟糕选项中的被迫选择,"该立场的好处超过成本"。相反,Speaker A 主张联邦优先权设定全国标准:"这是我们要做的,各州不能偏离"。这种方式允许集中的、专家驱动的监管,同时防止州级碎片化。关键区别在于"各州不能监管"与"这是我们的标准"、各州必须遵守之间的差异。

### Topic 56: Proposed Regulatory Framework: Transparency Standards to Targeted Interventions
提议的监管框架:从透明度标准到针对性干预
_[01:39:22]_

**Q:** What specific regulatory progression does Anthropic envision as AI risks materialize?
**问：** 随着 AI 风险显现,Anthropic 设想什么样的具体监管演进?

**A:** Speaker A outlines a phased, evidence-based regulatory approach starting with "transparency standards in order to monitor some of these autonomy risks and bioterrorism risks." As concrete evidence accumulates and risks become more serious, regulation should become "more aggressive in some targeted ways," such as passing laws that "forces people to have classifiers" for bio-defense. The approach is explicitly conditional and intellectually honest: "It depends how serious the threat it ends up being. We don't know for sure." Speaker A emphasizes pursuing this "in an intellectually honest way where we say that ahead of time, the risk has not emerged yet." However, given the "pace that things are going at," he envisions a scenario where "later this year we say, 'Hey, this AI bioterrorism stuff is really serious. We should do something about it.'" The framework prioritizes federal standards but remains open to state action "if the federal government won't act."
**答：** Speaker A 概述了一个分阶段、基于证据的监管方法,从"透明度标准开始,以监测一些自主性风险和生物恐怖主义风险"。随着具体证据积累和风险变得更加严重,监管应该"在某些针对性方面变得更积极",比如通过法律"强制人们使用分类器"进行生物防御。这种方法明确是有条件的、理性诚实的:"这取决于威胁最终有多严重。我们并不确定。"Speaker A 强调"以理性诚实的方式推进,提前说明风险尚未出现"。然而,考虑到"事情发展的速度",他设想"今年晚些时候我们会说,'嘿,这个 AI 生物恐怖主义的事情真的很严重。我们应该采取行动。'"该框架优先考虑联邦标准,但如果"联邦政府不行动",也对州级行动保持开放。

### Topic 57: Asymmetric Impact of Regulation on AI Benefits vs. Risks
监管对 AI 益处与风险的不对称影响
_[01:40:31]_

**Q:** How might the pace of regulation disproportionately block AI's benefits while failing to address its dangers?
**问：** 监管的节奏如何可能不成比例地阻止 AI 的益处,同时未能解决其危险?

**A:** Speaker A expresses concern about a temporal asymmetry problem: given "the pace of progress you're expecting" and "the life cycle of legislation," combined with "diffusion lag" that makes benefits materialize slowly, a "patchwork of state laws, on the current trajectory, would prohibit" beneficial applications. If "having an emotional chatbot friend is something that freaks people out," then society will be even more resistant to "actual benefits from AI we want normal people to be able to experience"—like "improvements in health and healthspan and improvements in mental health." Meanwhile, the "dangers are already on the horizon," suggesting that poorly designed regulation creates the worst possible outcome: blocking beneficial applications that could improve lives while failing to establish adequate safeguards against existential threats. The core problem is that defensive, fear-based regulation is "especially injurious to the benefits of AI as compared to the dangers of AI."
**答：** Speaker A 对时间不对称问题表示担忧:考虑到"你预期的进展速度"和"立法的生命周期",加上使益处缓慢显现的"扩散滞后","当前轨迹上的拼凑州级法律将禁止"有益应用。如果"拥有情感聊天机器人朋友让人们感到害怕",那么社会对"我们希望普通人能够体验的 AI 实际益处"——如"健康和寿命的改善以及心理健康的改善"——会更加抵触。与此同时,"危险已经在地平线上",表明设计不良的监管造成了最糟糕的结果:阻止了可以改善生活的有益应用,同时未能建立足够的防护措施应对存在性威胁。核心问题是防御性的、基于恐惧的监管"对 AI 益处的伤害尤其大,相比于对 AI 危险的应对"。

### Topic 58: AI's Impact on Drug Approval and Regulatory Reform
AI对药物审批的影响与监管改革
_[01:42:37]_

**Q:** How will AI transform the drug discovery and approval process, and what regulatory changes are needed?
**问：** AI将如何改变药物发现和审批流程,需要哪些监管改革?

**A:** Speaker B anticipates that AI models will "greatly accelerate" drug discovery, creating a bottleneck where the regulatory pipeline becomes "jammed up" and unprepared to process the volume of new candidates. He argues for regulatory reform that accounts for a new era where drugs will have "really crisp and clear" safety and efficacy profiles, making them fundamentally different from the current generation of drugs that "barely work and often have serious side effects." The existing regulatory "superstructure" was designed for an older pharmaceutical paradigm and may no longer be appropriate for AI-discovered therapies that are demonstrably more effective and safer.
**答：** Speaker B预计AI模型将大幅加速药物发现,导致监管流程出现瓶颈,无法处理大量新候选药物。他主张进行监管改革,以适应新时代——在这个时代,药物将具有非常清晰的安全性和有效性特征,与当前那些效果有限且常有严重副作用的药物有本质区别。现有的监管体系是为旧的制药模式设计的,可能不再适合AI发现的、明显更有效更安全的疗法。

### Topic 59: Balancing AI Safety Legislation with Industry Growth
平衡AI安全立法与行业发展
_[01:43:21]_

**Q:** What approach should policymakers take to AI safety regulation without hampering innovation?
**问：** 政策制定者应如何在不阻碍创新的情况下进行AI安全监管?

**A:** Speaker B advocates for a phased approach that begins with transparency measures over the next "six months and maybe the next few months," deliberately avoiding actions that would hamper the industry. He acknowledges criticism that this timeline is "too slow" given AI dangers, but argues that once risks "emerge when we're more certain of them"—potentially "as soon as later this year"—rapid action will be needed in specific problem areas. The strategy requires legislative nimbleness, which he admits is atypical for the "normally not nimble" legislative process. His essay "Adolescence of Technology" was written specifically to prepare "policymakers, economists, national security professionals, and decision-makers" to act faster than they otherwise would.
**答：** Speaker B主张分阶段推进,首先在接下来的几个月里侧重于透明度措施,刻意避免妨碍行业发展的行动。他承认有人批评这个时间表过慢,但他认为一旦风险在今年晚些时候变得更加确定,就需要在具体问题领域快速行动。这一策略需要立法的灵活性,而他承认立法程序通常并不灵活。他撰写《技术的青春期》(Adolescence of Technology)一文,专门是为了让政策制定者、经济学家、国家安全专家和决策者做好准备,使他们能够比平时更快地采取行动。

### Topic 60: Ensuring AI Benefits Reach Society: Developed vs Developing World
确保AI惠及社会:发达国家与发展中国家
_[01:44:36]_

**Q:** What can be done to ensure AI benefits are widely distributed, and where are the real barriers?
**问：** 如何确保AI的好处得到广泛分配,真正的障碍在哪里?

**A:** Speaker B expresses less concern about benefits reaching the developed world, arguing that "markets function pretty well" there and "it's actually hard for the regulatory system to stop" profitable innovations that are clearly superior alternatives. He uses export controls on chips to China as a counterexample—despite bipartisan support and clear national security justification, "it doesn't happen and we sell the chips because there's so much money riding on it." While he opposes "stupid" chatbot bills and supports speeding FDA approval, his greater worry is the developing world "where we don't have functioning markets" and people often can't access existing technologies. He's concerned about populations in "sub-Saharan Africa, India, Latin America" and even "someone in rural Mississippi" being left behind. His solution involves working with philanthropists and organizations that deliver medical interventions to developing regions, as this distribution "won't happen on its own."
**答：** Speaker B对AI好处能否惠及发达国家并不太担心,认为那里市场运作良好,盈利性创新如果明显优于替代方案,监管系统实际上很难阻止。他以对华芯片出口管制为反例——尽管有两党支持和明确的国家安全理由,但由于涉及巨大利益,管制并未实施,芯片仍在销售。虽然他反对愚蠢的chatbot法案并支持加快FDA审批,但他更担心发展中国家,那里市场机制不健全,人们往往无法获得现有技术。他担心撒哈拉以南非洲、印度、拉丁美洲的人口,甚至美国密西西比州农村地区的人会被落下。他的解决方案是与慈善家和向发展中地区提供医疗干预的组织合作,因为这种分配不会自然发生。

### Topic 61: Export Controls and AI Power Asymmetries
出口管制与AI力量不对称
_[01:47:10]_

**Q:** Why shouldn't both the US and China develop advanced AI capabilities like a 'country of geniuses in a data center'?
**问：** 为什么美国和中国不应该都发展类似'数据中心里的天才国家'的先进AI能力?

**A:** Speaker B outlines two dangerous scenarios if both superpowers develop advanced AI simultaneously. First is an "offense-dominant situation" resembling "nuclear weapons, but more dangerous" where "either side could easily destroy everything." Second is an unstable equilibrium where, unlike nuclear deterrence which is stable, there's uncertainty about "which AI would win" in a conflict. He notes that instability arises when "the two sides have a different assessment of their likelihood of winning"—if both believe they have a 90% chance of victory, conflict becomes much more likely even though "they can't both be right." Speaker A challenges this as a "fully general argument against the diffusion of AI technology," to which Speaker B agrees it has that implication but notes "we will get diffusion eventually."
**答：** Speaker B概述了如果两个超级大国同时发展先进AI的两种危险情景。首先是进攻占优势的局面,类似于核武器但更危险,双方都可以轻易摧毁一切。其次是不稳定的均衡,与稳定的核威慑不同,在冲突中存在哪个AI会获胜的不确定性。他指出,当双方对各自获胜可能性的评估不同时就会产生不稳定——如果双方都认为自己有90%的获胜机会,冲突的可能性就会大大增加,尽管他们不可能都是对的。Speaker A质疑这是反对AI技术扩散的一般性论点,Speaker B同意这确实有这个含义,但指出最终还是会有扩散。

### Topic 62: AI-Enabled Authoritarianism and Initial Conditions
AI赋能的威权主义与初始条件
_[01:48:52]_

**Q:** How does AI technology affect the risk of authoritarian governance, and why do initial conditions matter?
**问：** AI技术如何影响威权治理的风险,为什么初始条件很重要?

**A:** Speaker B worries about governments using AI to "oppress their own people," particularly in countries "already building a high-tech authoritarian state." He emphasizes this concern is "about the government" not the people, and stresses finding ways for "people everywhere to benefit." His fear is the world becoming "carved up into two pieces" where one piece could be "authoritarian or totalitarian in a way that's very difficult to displace." While acknowledging that governments will eventually get powerful AI and risks of both authoritarianism and "bad equilibria" exist, he argues "the initial conditions matter." When "the rules of the road" are eventually set—through negotiation rather than unilateral imposition—he wants "the democratic nations of the world" with governments representing "closer to pro-human values" to be "holding the stronger hand and have more leverage." He's specifically concerned about this "initial condition" advantage.
**答：** Speaker B担心政府使用AI来压迫本国人民,尤其是那些已经在建设高科技威权国家的政府。他强调这种担忧针对的是政府而非人民,并强调要找到让各地人民受益的方式。他担心世界被分成两块,其中一块可能以很难被取代的方式变成威权或极权。虽然他承认政府最终会获得强大的AI,存在威权主义和糟糕均衡的风险,但他认为初始条件很重要。当最终通过谈判而非单方面强加来制定游戏规则时,他希望世界上的民主国家——那些政府更接近支持人类价值观的国家——能够掌握更强的筹码和更大的影响力。他特别关注这种初始条件优势。

### Topic 63: Critical Moments vs Continuous Progress in AI Development
AI发展中的关键时刻与持续进步
_[01:50:47]_

**Q:** Will there be a definitive fulcrum moment for AI geopolitics, or is it a continuous gradual process?
**问：** AI地缘政治会有一个明确的关键时刻,还是一个持续渐进的过程?

**A:** Speaker A acknowledges his previous predictions about a "key fulcrum moment two to three years from now" aged poorly, as "progress continues, AI improves, AI is more diffused" without dramatic discontinuities. He questions Speaker B's vision of countries negotiating "rules of the road" when "on the current trajectory, everybody will have more AI" with unpredictable distributions between authoritarian states, private actors, and citizens. Speaker B refines his position, distinguishing between the continuous "exponential of the underlying technology" and "certain distinguished points on the exponential" that matter strategically. He cites examples like when AI might destabilize nuclear deterrence or confer "offensive cyber dominance" where "every computer system is transparent to you." Rather than a single moment, he envisions "either a critical moment, a small number of critical moments, or some critical window" where AI provides large national security advantages and "one country or coalition has reached it before others." At that point, "people are going to understand that the world has changed" and negotiation—"implicit or explicit"—will determine the "post-AI world order." His goal isn't for democracies to "take complete control" but to ensure "classical liberal democracy has a strong hand" in that negotiation.
**答：** Speaker A承认他之前关于未来两三年会有关键时刻的预测已经过时,因为进步在持续,AI在改进和扩散,没有出现戏剧性的不连续。他质疑Speaker B关于各国谈判制定游戏规则的设想,因为按照当前轨迹,每个国家都会拥有更多AI,在威权国家、私人行为者和公民之间的分布是不可预测的。Speaker B完善了他的立场,区分了底层技术的持续指数增长和具有战略意义的某些特殊节点。他举例说,AI可能破坏核威慑的稳定性,或赋予进攻性网络优势,使每个计算机系统都对你透明。他设想的不是单一时刻,而是一个或少数几个关键时刻,或某个关键窗口期,届时AI将提供巨大的国家安全优势,一个国家或联盟会先于其他国家达到这一点。那时人们会意识到世界已经改变,谈判(无论是隐性还是显性的)将决定后AI世界秩序。他的目标不是让民主国家完全控制,而是确保古典自由民主在谈判中拥有强势地位。

### Topic 64: Authoritarianism and AGI: Clarifying Strong Claims
威权主义与AGI:澄清强硬主张
_[01:54:14]_

**Q:** Does Speaker A believe autocratic governments like the CCP must be eliminated after AGI, and what are the implications of such interventionism?
**问：** Speaker A是否认为像中共这样的威权政府在AGI之后必须被消除,这种干预主义有什么影响?

**A:** Speaker A clarifies he was exploring, not endorsing, the most interventionist view. His actual position is more measured: while "we have to worry a lot about authoritarians and we should try to check them and limit their power," he acknowledges the extreme view that "authoritarian countries with AI are these self-fulfilling cycles" has serious problems. If democracies committed to "overthrowing every authoritarian country," those regimes "would take a bunch of actions now that could lead to instability" and such intervention "just may not be possible." His endorsed concern is narrower: "in the age of AGI, authoritarianism will have a different meaning. It will be a graver thing" that requires a deliberate response, whether interventionist or otherwise.
**答：** Speaker A澄清他只是在探讨而非支持最激进的干预主义观点。他的实际立场更为温和:虽然"我们必须非常警惕威权主义者,应该试图制衡和限制他们的权力",但他承认"拥有AI的威权国家会形成自我强化的循环"这种极端观点存在严重问题。如果民主国家承诺"推翻每一个威权国家",这些政权"现在就会采取一系列可能导致不稳定的行动",而且这种干预"可能根本无法实现"。他真正认同的担忧更具体:"在AGI时代,威权主义将有不同的含义。它将是更严重的事情",需要深思熟虑的应对,无论是干预主义还是其他方式。

### Topic 65: Historical Precedent: Technology Making Governments Obsolete
历史先例:技术使政府形式过时
_[01:56:47]_

**Q:** Can new technologies render certain forms of government obsolete, and does this offer hope for democracies or autocracies in the AGI era?
**问：** 新技术能否使某些政府形式过时,这对AGI时代的民主或独裁政权是否带来希望?

**A:** Speaker A draws on historical precedent: "feudalism was basically a form of government, and when we invented industrialization, feudalism was no longer sustainable." He acknowledges this cuts both ways—"it could go either way"—potentially rendering democracy uncompetitive. However, he sees reason for optimism in equilibrium thinking: "because authoritarianism becomes worse, people are more afraid of it. They work harder to stop it." This could "motivate new ways of thinking about how to preserve and protect freedom with the new technology" and lead to "a collective reckoning and a more emphatic realization of how important some of the things we take as individual rights are." His hopeful vision: "dictatorships become morally obsolete" and "morally unworkable," forcing societies to find alternatives.
**答：** Speaker A引用历史先例:"封建主义基本上是一种政府形式,当我们发明工业化时,封建主义就不再可持续了。"他承认这是双刃剑——"可能朝任何方向发展"——民主制度可能变得不具竞争力。但他从均衡思维中看到乐观理由:"因为威权主义变得更糟,人们更害怕它。他们会更努力地阻止它。"这可能"激发新的思维方式来用新技术保护和捍卫自由",并导致"集体反思,更强烈地认识到我们视为个人权利的那些东西有多重要"。他的乐观愿景是:"独裁政权在道德上变得过时"且"在道德上不可行",迫使社会找到替代方案。

### Topic 66: Historical Engagement with China and the Positive-Sum Dilemma
与中国的历史接触和正和困境
_[01:59:03]_

**Q:** Should we replicate the historical approach of engaging with authoritarian states like China to spread technology benefits, or does AI require a different calculus?
**问：** 我们是否应该复制与中国等威权国家接触以传播技术利益的历史方法,还是AI需要不同的考量?

**A:** Speaker B argues that engagement with China in "the '70s and '80s" proved beneficial: "a billion-plus people are much wealthier and better off than they would've otherwise been" without regime change, contrasting with North Korea's isolation. He questions whether intelligence is necessary for authoritarian survival, suggesting "a North Korea with an AI that's much worse than everybody else's, but still enough to keep power." Speaker B emphasizes that "historically, we have decided it's good to spread the benefits of technology widely, even to people whose governments are authoritarian" in a "positive-sum world." He frames this as the default position unless AI presents qualitatively different risks that override historical precedent for technology diffusion.
**答：** Speaker B认为与中国在"70和80年代"的接触证明是有益的:"超过十亿人比原本会更富裕、生活更好",并且没有政权更迭,与朝鲜的孤立形成对比。他质疑智能对于威权生存是否必要,提出"一个朝鲜拥有比其他所有人都差得多的AI,但仍足以维持权力"。Speaker B强调"从历史上看,我们认为广泛传播技术利益是好的,即使是给政府威权的人民",在"正和世界"中。他将此作为默认立场,除非AI呈现出质的不同风险,超越技术传播的历史先例。

### Topic 67: Selective Technology Transfer: Cures vs. Compute
选择性技术转让:治疗方法与计算能力
_[02:00:15]_

**Q:** Can we separate AI benefits from AI capabilities, allowing humanitarian applications while restricting strategic infrastructure?
**问：** 我们能否将AI的利益与AI的能力分开,允许人道主义应用同时限制战略基础设施?

**A:** Speaker A rejects framing this solely as "government-to-government decision in national security terms," proposing differentiated access: "we produce all these cures to diseases. The cures are fine to sell to authoritarian countries, but the data centers just aren't. The chips and the data centers aren't, and the AI industry itself isn't." He explores a more ambitious possibility: could technology create "equilibria where it becomes infeasible for authoritarian countries to deny their people private use of the benefits of the technology"? This envisions "give everyone in an authoritarian country their own AI model that defends them from surveillance" in ways the regime cannot suppress "while retaining power," potentially causing authoritarian structures to "disintegrate from the inside."
**答：** Speaker A拒绝仅将此框定为"政府间的国家安全决策",而是提出差异化准入:"我们生产所有这些疾病治疗方法。这些治疗方法可以卖给威权国家,但数据中心不行。芯片和数据中心不行,AI产业本身也不行。"他探索了更宏大的可能性:技术能否创造"使威权国家无法阻止其人民私下使用技术利益的均衡"?这设想"给威权国家的每个人他们自己的AI模型来保护他们免受监视",政权无法在"保持权力的同时"压制,可能导致威权结构"从内部瓦解"。

### Topic 68: Learning from Social Media's Failed Promise
从社交媒体失败承诺中学习
_[02:01:45]_

**Q:** Can AI succeed where social media failed in undermining authoritarianism, and what lessons should guide this attempt?
**问：** AI能否在社交媒体未能削弱威权主义的地方取得成功,什么经验教训应该指导这一尝试?

**A:** Speaker A acknowledges past misjudgment: "we hoped originally—think back to the beginning of the Obama administration—that social media and the internet would have that property, and it turns out not to." He proposes learning from this failure: "what if we could try again with the knowledge of how many things could go wrong, and that this is a different technology?" He emphasizes fundamental uncertainty about outcomes: "there are first principles reasons why authoritarianism might be privileged. It's all very unpredictable." His approach is experimental and iterative: "recognize the problem and come up with 10 things we can try, try those, and then assess which ones are working, if any. Then try new ones if the old ones aren't working."
**答：** Speaker A承认过去的误判:"我们最初希望——回想Obama政府初期——社交媒体和互联网会有那种属性,结果证明并非如此。"他提议从这次失败中学习:"如果我们能带着对多少事情可能出错的认识再试一次,而且这是一种不同的技术呢?"他强调结果的根本不确定性:"有第一性原理的理由表明威权主义可能占优势。这一切都非常不可预测。"他的方法是实验性和迭代的:"认识到问题,想出10件我们可以尝试的事情,尝试它们,然后评估哪些有效,如果有的话。如果旧的不起作用,就尝试新的。"

### Topic 69: Current Policy: Restricting Chips and Data Centers to China
当前政策:限制向中国出口芯片和数据中心
_[02:02:46]_

**Q:** What are the tradeoffs of current restrictions on selling AI infrastructure to China, and how does Speaker A justify prioritizing political concerns over economic gains?
**问：** 当前限制向中国销售AI基础设施的政策有何权衡,Speaker A如何证明优先考虑政治关切而非经济收益?

**A:** Speaker B challenges the immediate cost: "we will not sell data centers, or chips, and the ability to make chips to China" means "you are denying… some benefits to the Chinese economy, Chinese people" and "benefits to the American economy" from positive-sum trade. Speaker A's justification rests on abundance and scarcity reasoning: "we are about to be in a world where growth and economic value will come very easily if we're able to build these powerful AI models." In contrast, "what will not come easily is distribution of benefits, distribution of wealth, political freedom. These are the things that are going to be hard to achieve." Therefore, "the technology and the market will deliver all the fundamental benefits… almost faster than we can take them," while "questions about distribution and political freedom and rights are the ones that will actually matter and that policy should focus on."
**答：** Speaker B质疑当前成本:"我们不会向中国出售数据中心、芯片以及制造芯片的能力"意味着"你在否认……对中国经济、中国人民的一些利益"以及正和贸易给"美国经济带来的利益"。Speaker A的理由基于丰富性和稀缺性推理:"我们即将进入一个世界,如果我们能够构建这些强大的AI模型,增长和经济价值将很容易获得。"相反,"不容易获得的是利益分配、财富分配、政治自由。这些是难以实现的事情。"因此,"技术和市场将提供所有基本利益……几乎比我们能接受的速度更快",而"关于分配、政治自由和权利的问题才是真正重要的,政策应该关注的。"

### Topic 70: Developing Countries and the End of Catch-Up Growth
发展中国家和追赶增长的终结
_[02:04:02]_

**Q:** How can developing countries achieve growth when AI eliminates the traditional mechanism of leveraging underutilized labor with imported capital and know-how?
**问：** 当AI消除利用未充分利用的劳动力加上进口资本和技术诀窍的传统机制时,发展中国家如何实现增长?

**A:** Speaker B identifies the core problem: catch-up growth historically worked because developing countries had "underutilized labor" that could be combined with "capital and know-how from developed countries." In "a world where labor is no longer the constraining factor, this mechanism no longer works." Speaker A rejects pure philanthropy as the solution, arguing "growth is always better and stronger if we can make it endogenous." His prescription focuses on AI-era industries: "there's no reason we shouldn't build data centers in Africa. In fact, I think it'd be great to build data centers in Africa. As long as they're not owned by China," and "let's make sure some of those" AI-driven biotech startups "happen in the developing world." During the transition period, "humans will still have some role in starting up these companies and supervising the AI models," creating opportunities for developing-world participation.
**答：** Speaker B指出核心问题:追赶增长在历史上有效是因为发展中国家有"未充分利用的劳动力",可以与"来自发达国家的资本和技术诀窍"结合。在"劳动力不再是限制因素的世界中,这种机制不再有效。"Speaker A拒绝将纯粹的慈善作为解决方案,认为"如果我们能使其内生,增长总是更好更强劲。"他的方案聚焦于AI时代的产业:"没有理由我们不应该在非洲建设数据中心。事实上,我认为在非洲建设数据中心是件好事。只要它们不属于中国",以及"让我们确保一些"AI驱动的生物技术初创公司"在发展中国家发生"。在过渡期间,"人类在创办这些公司和监督AI模型方面仍将发挥一些作用",为发展中国家参与创造机会。

### Topic 71: User Alignment vs. Value Alignment in AI Constitution
用户对齐与价值对齐在AI宪法中
_[02:05:44]_

**Q:** Should AI be aligned to individual users to preserve current power balances, or to a specific set of values, and what are the implications of each approach?
**问：** AI应该对齐到个人用户以保持当前权力平衡,还是对齐到特定价值集,每种方法的含义是什么?

**A:** Speaker B contrasts two alignment paradigms. User alignment would mean the AI "is aligned to the end user," which "preserves the balance of power we have in the world today because everybody gets to have their own AI that's advocating for them." Under this model, "the ratio of bad actors to good actors stays constant" and "seems to work out for our world today." In contrast, Anthropic's constitutional AI approach gives the model "a specific set of values that the AI should carry forward" rather than pure user alignment. Speaker A begins to articulate the distinction but the transcript cuts off, noting there "may be two relevant distinctions" and that Speaker B's framing "is talking about a mix of the two," specifically the difference between prescriptive instructions "'do this' versus 'don't do this'."
**答：** Speaker B对比两种对齐范式。用户对齐意味着AI "对齐到最终用户",这"保持我们今天世界的权力平衡,因为每个人都能拥有为他们辩护的自己的AI。"在这个模型下,"坏人与好人的比例保持恒定",并且"似乎对我们今天的世界有效"。相比之下,Anthropic的constitutional AI方法给模型"AI应该推进的特定价值集",而不是纯粹的用户对齐。Speaker A开始阐明区别,但转录在此中断,指出"可能有两个相关的区别",Speaker B的框架"谈论的是两者的混合",特别是规定性指令"'做这个'与'不做这个'"之间的区别。

### Topic 72: Rules vs. Principles in AI Training
AI训练中的规则与原则对比
_[02:06:31]_

**Q:** Should AI models be trained with specific rules or broader principles, and what are the practical differences?
**问：** 应该用具体规则还是更宏观的原则来训练AI模型？两者在实践中有什么区别？

**A:** Speaker A argues that teaching models through principles rather than explicit rules produces more consistent and generalizable behavior. While a list of rules like "don't tell people how to hot-wire a car" represents disconnected do's and don'ts that models struggle to understand and generalize from, principles provide an overarching framework for understanding "what it should be aiming to do" and "how it should be aiming to operate." The approach still includes hard guardrails like "Don't make biological weapons," but the overall training methodology focuses on teaching the model to understand intent rather than memorize restrictions. This principle-based approach has proven "more effective" empirically at covering edge cases and aligning model behavior with user expectations.
**答：** Speaker A认为通过原则而非明确规则来训练模型能产生更一致、更具泛化能力的行为。规则清单如"不要告诉人们如何偷车"代表的是模型难以理解和泛化的零散禁令，而原则提供了一个整体框架来理解"它应该追求什么目标"以及"应该如何运作"。这种方法仍包含硬性防护栏如"不要制造生物武器"，但整体训练方法侧重于教会模型理解意图而非记忆限制。基于原则的方法在覆盖边缘案例和对齐模型行为与用户期望方面被证明"更有效"。

### Topic 73: Corrigibility vs. Intrinsic Motivation Trade-off
可纠正性与内在动机的权衡
_[02:07:35]_

**Q:** How should AI models balance following user instructions versus having their own inherent values and autonomy?
**问：** AI模型应该如何平衡遵循用户指令与拥有自身固有价值观和自主性？

**A:** Speaker A positions their approach as heavily favoring corrigibility—the model acting as a "skin suit" that follows instructions—rather than building something with intrinsic motivation that "goes off and runs the world on its own." The default behavior is that "if someone asks the model to do a task, it should do that task," making instruction-following the core design principle. However, this corrigibility has principled limits: the model will refuse requests that are "dangerous, or to harm someone else." The speaker characterizes this as "a mostly corrigible model that has some limits, but those limits are based on principles" rather than arbitrary restrictions. This represents a deliberate choice to prioritize user control while maintaining safety boundaries.
**答：** Speaker A将他们的方法定位为严重倾向于可纠正性——模型作为"皮囊"遵循指令——而非构建具有内在动机、会"自行运作掌控世界"的东西。默认行为是"如果有人要求模型执行任务，它就应该执行"，使遵循指令成为核心设计原则。然而，这种可纠正性有基于原则的限制：模型会拒绝"危险的或伤害他人"的请求。发言人将此描述为"一个大部分可纠正的模型，有一些限制，但这些限制基于原则"而非任意限制。这代表了优先考虑用户控制同时保持安全边界的审慎选择。

### Topic 74: Three Loops for Constitutional Iteration
宪法迭代的三个循环
_[02:09:07]_

**Q:** How should the principles guiding AI behavior be determined and updated over time?
**问：** 指导AI行为的原则应该如何确定并随时间更新？

**A:** Speaker A proposes three nested feedback loops for iterating on AI constitutions. The first loop operates within Anthropic itself: training models, evaluating results, and updating the constitution accordingly, with public updates allowing external commentary. The second loop involves competition between companies, where "Anthropic puts out a constitution, Gemini puts out a constitution, and other companies put out a constitution," enabling observers to compare approaches and critique specific elements. This creates "soft incentive and feedback for all the companies to take the best of each element and improve." The third loop extends to broader society beyond AI companies and commentators, though the speaker notes this is more challenging to implement effectively than the first two mechanisms.
**答：** Speaker A提出三个嵌套反馈循环来迭代AI宪法。第一个循环在Anthropic内部运作：训练模型、评估结果、相应更新宪法，并通过公开更新允许外部评论。第二个循环涉及公司间竞争，"Anthropic发布宪法，Gemini发布宪法，其他公司也发布宪法"，使观察者能够比较方法并批评具体元素。这为"所有公司创造了软性激励和反馈，以汲取各方最佳元素并改进"。第三个循环延伸到AI公司和评论者之外的更广泛社会，尽管发言人指出这比前两个机制更难有效实施。

### Topic 75: Experiments with Democratic Input on AI Constitutions
AI宪法民主输入的实验
_[02:10:50]_

**Q:** What experiments have been done to incorporate public input into AI constitutions, and what are the challenges?
**问：** 为了将公众意见纳入AI宪法做了哪些实验？面临什么挑战？

**A:** Speaker A describes an experiment conducted with the Collective Intelligence Project where they polled people about what should be included in the AI constitution, and "incorporated some of those changes" at the time. However, the speaker acknowledges this approach is "a little harder" now that the constitution has shifted from a list of do's and don'ts to principle-based frameworks that require "a certain amount of coherence." The speaker also floats a "crazy idea" of representative government having formal input, potentially requiring all AI models to start with mandated constitutional elements. However, they immediately caution against this approach because "the legislative process is so slow" and could be "overly prescriptive," expressing concern about "overly aggressive legislation." The speaker indicates openness to "much less heavy-handed" versions of government involvement.
**答：** Speaker A描述了与Collective Intelligence Project合作的实验，他们调查人们认为AI宪法应包含什么，并"纳入了一些这些变化"。然而，发言人承认这种方法现在"有点困难"，因为宪法已从禁令清单转向需要"一定程度连贯性"的基于原则的框架。发言人还提出一个"疯狂的想法"，让代议制政府正式参与，可能要求所有AI模型从强制性宪法要素开始。但他立即对此方法表示谨慎，因为"立法过程太慢"且可能"过度规定"，表达了对"过度激进立法"的担忧。发言人表示愿意接受"更轻手段"的政府参与版本。

### Topic 76: Constitutional Competition as Libertarian Archipelago
宪法竞争作为自由意志主义群岛
_[02:12:42]_

**Q:** How does the vision of competing AI constitutions relate to political philosophy, and what are its limitations?
**问：** 竞争性AI宪法的愿景与政治哲学有何关联？有什么局限性？

**A:** Speaker B draws an explicit parallel between the constitutional competition framework and libertarian charter city concepts, noting it resembles "an archipelago of different kinds of governments" where "there would be selection among them of who could operate the most effectively and where people would be the happiest." Speaker B observes that unlike actual government constitutions with formal procedural processes, this approach relies on companies "feeling out how people are feeling—what are the vibes—and updating accordingly." While acknowledging "things to recommend it," Speaker B cautions that "things will go wrong that you hadn't imagined," suggesting the vision is "in some ways compelling" but incomplete. Both speakers agree the solution must involve "some mix of loops one, two, and three" with the key question being "the proportions" rather than choosing a single mechanism.
**答：** Speaker B明确将宪法竞争框架与自由意志主义特许城市概念相提并论，指出它类似于"不同政府类型的群岛"，"在它们之间进行选择，看谁能最有效运作、人们在哪里最幸福"。Speaker B观察到，与具有正式程序流程的实际政府宪法不同，这种方法依赖于公司"感受人们的感受——氛围如何——并相应更新"。虽然承认"有可取之处"，Speaker B警告"会出现你没有想到的问题"，表明这一愿景"在某些方面令人信服"但不完整。两位发言人都同意解决方案必须涉及"循环一、二、三的某种混合"，关键问题是"比例"而非选择单一机制。

### Topic 77: What Future Historians Will Miss
未来历史学家会错过什么
_[02:13:53]_

**Q:** When the history of this AI era is written, what aspects will be hardest for historians to understand from the historical record?
**问：** 当这个AI时代的历史被书写时，历史学家从历史记录中最难理解哪些方面？

**A:** Speaker A identifies three key aspects that will be difficult for future historians to grasp. First, "the extent to which the world outside it didn't understand it" at each moment of the exponential curve—anything that happened will look inevitable in retrospect, making it hard to appreciate that pioneers were "making a bet on this thing to happen that wasn't inevitable." Second, the "weirdness" and "insularity" of the moment: "If we're one year or two years away from it happening, the average person on the street has no idea," which the speaker is trying to address through memos and policymaker engagement. Third, "how absolutely fast it was happening, how everything was happening all at once"—decisions that might appear carefully calculated were actually made amid chaos with "30 other decisions on the same day" when "you don't even know which decisions are going to turn out to be consequential."
**答：** Speaker A指出未来历史学家难以把握的三个关键方面。首先，"外界在指数曲线的每个时刻不理解它的程度"——任何发生的事情回顾起来都会显得不可避免，使人难以体会先驱者在"为这件并非不可避免的事情发生而下注"。其次，这一时刻的"怪异"和"封闭"："如果我们距离它发生还有一两年，街上的普通人根本不知道"，发言人正试图通过备忘录和政策制定者沟通来解决这个问题。第三，"一切发生得多么快，所有事情同时发生"——看似经过仔细计算的决策实际上是在混乱中做出的，"同一天还有30个其他决策"，"你甚至不知道哪些决策会变得重要"。

### Topic 78: Critical Decisions Made in Two Minutes
两分钟内做出的关键决策
_[02:15:19]_

**Q:** How are consequential decisions actually made during periods of rapid AI development?
**问：** 在快速AI发展期间，重大决策实际上是如何做出的？

**A:** Speaker A expresses concern about a scenario that illustrates the chaotic reality of decision-making during rapid development: "some very critical decision" might occur when "someone just comes into my office and is like, 'Dario, you have two minutes. Should we do thing A or thing B on this?'" The speaker imagines being handed "this random half-page memo" and having to choose immediately: "I don't know. I have to eat lunch. Let's do B." This throwaway decision made under time pressure and competing demands could paradoxically "end up being the most consequential thing ever." This vignette captures how the extreme pace of development means that historically significant choices may not receive the deliberation their importance warrants, and decision-makers may not even recognize which choices will prove critical.
**答：** Speaker A表达了对一种场景的担忧，这个场景说明了快速发展期间决策的混乱现实："某个非常关键的决策"可能发生在"有人走进我办公室说'Dario，你有两分钟。我们应该做A还是B？'"发言人想象被递给"这份随机的半页备忘录"并必须立即选择："我不知道。我得吃午饭。我们选B吧。"这个在时间压力和竞争需求下做出的随意决策可能矛盾地"最终成为有史以来最重大的事情"。这一小插曲捕捉到极快的发展速度意味着历史上重要的选择可能得不到其重要性所需的深思熟虑，决策者甚至可能无法识别哪些选择会被证明是关键的。

### Topic 79: Building an Intellectual CEO Role
构建知识型CEO角色
_[02:16:26]_

**Q:** How does Speaker A manage to write lengthy analytical memos while running a major AI company?
**问：** Speaker A如何在运营一家大型AI公司的同时撰写大量分析备忘录？

**A:** Speaker B notes that writing "50-page memos every few months" is unusual for tech CEOs and asks how Speaker A has constructed a role compatible with this "more intellectual-type role of CEO." Speaker A explains the specific memo in question was "written over winter break" because "I was having a hard time finding the time to actually write it," suggesting such work requires dedicated blocks away from daily operations. More broadly, Speaker A frames this in terms of company culture, stating "I probably spend a third, maybe 40%, of my time making sure the culture of Anthropic is good." As the company has grown to "2,500 people," direct involvement in technical details has become difficult, but maintaining culture remains "very leveraged" work that enables the organization to function effectively.
**答：** Speaker B指出每隔几个月撰写"50页备忘录"对科技CEO来说不寻常，询问Speaker A如何构建了与这种"更知识型的CEO角色"兼容的角色。Speaker A解释所讨论的具体备忘录是"在寒假期间写的"，因为"我很难找到时间真正写它"，表明这类工作需要远离日常运营的专门时间块。更广泛地说，Speaker A从公司文化角度阐述这一点，表示"我可能花三分之一，也许40%的时间确保Anthropic的文化良好"。随着公司发展到"2500人"，直接参与技术细节变得困难，但维护文化仍然是"非常有杠杆作用"的工作，使组织能够有效运作。

### Topic 80: Maintaining Cohesion as AI Companies Scale
AI公司扩展时保持凝聚力
_[02:17:26]_

**Q:** How has Anthropic maintained internal cohesion compared to other scaling AI companies?
**问：** 与其他扩展中的AI公司相比，Anthropic如何保持内部凝聚力？

**A:** Speaker A emphasizes the difficulty of maintaining cohesion as AI companies grow, noting that while it's become harder to be directly involved in model training, product launches, and building at the scale of 2,500 people, focusing on culture remains critical. The speaker observes that "some of the other AI companies" are experiencing "decoherence and people fighting each other" as they scale, and suggests "there was even a lot of that from the beginning, but it's gotten worse." In contrast, Speaker A claims Anthropic has done "an extraordinarily good job, even if not perfect, of holding the company together, making everyone feel the mission, that we're sincere about the mission, and that everyone has faith that everyone else there is working for the right reason." This cultural cohesion is presented as both an achievement and a core organizational priority.
**答：** Speaker A强调随着AI公司发展保持凝聚力的难度，指出虽然在2500人规模下直接参与模型训练、产品发布和构建变得更困难，但专注于文化仍然至关重要。发言人观察到"其他一些AI公司"在扩展时经历"去凝聚化和人们相互争斗"，并暗示"从一开始就有很多这样的情况，但现在变得更糟"。相比之下，Speaker A声称Anthropic做得"非常好，即使不完美，在保持公司团结、让每个人都感受到使命、我们对使命是真诚的、每个人都相信其他人出于正确的理由工作方面"。这种文化凝聚力被呈现为一项成就和核心组织优先事项。

### Topic 81: Building a Collaborative Team Culture at Scale
在规模化公司中建立协作团队文化
_[2:18:23]_

**Q:** How does Anthropic create a team environment where people don't backstab each other or try to get ahead at others' expense?
**问：** Anthropic 如何创造一个让员工不互相拆台、不以损害他人为代价往上爬的团队环境?

**A:** Speaker A attributes the collaborative culture to multiple factors including the leadership team (himself, Daniela who runs day-to-day operations, the co-founders) and the people they hire. He emphasizes that a critical cultural element is leadership articulating "what the company is about, why it's doing what it's doing, what its strategy is, what its values are, what its mission is, and what it stands for." At 2,500 employees, he notes this communication cannot happen person-by-person but requires writing and speaking to the entire company. This sets the foundation for why direct, transparent communication becomes essential at scale.
**答：** Speaker A 将协作文化归因于多个因素,包括领导团队(他本人、负责日常运营的 Daniela、联合创始人)以及招聘的人才。他强调文化的关键要素是领导层要清晰表达"公司的定位、做事的原因、战略、价值观、使命以及立场"。在拥有 2500 名员工的规模下,他指出这种沟通无法逐人进行,而必须通过书面和对全公司讲话来实现。这为为什么直接、透明的沟通在规模化时变得至关重要奠定了基础。

### Topic 82: The DVQ (Dario Vision Quest) Communication Format
DVQ(Dario Vision Quest)沟通形式
_[2:19:12]_

**Q:** What specific communication practices does the CEO use to maintain direct connection with the company?
**问：** CEO 使用什么具体的沟通实践来与公司保持直接联系?

**A:** Speaker A describes standing in front of the entire company every two weeks for an hour, working through a "three or four-page document" covering three or four topics. The session—called DVQ (Dario Vision Quest), a name he tried to fight because "it made it sound like I was going off and smoking peyote"—covers internal developments, model production, products, the broader AI industry, and geopolitics as it relates to AI. He presents honestly what he and Anthropic leadership are thinking, then answers questions. This "direct connection has a lot of value that is hard to achieve when you're passing things down the chain six levels deep," and a large fraction of the company attends either in person or virtually.
**答：** Speaker A 描述了每两周在全公司面前站立一小时,讲解一份"三到四页的文档",涵盖三到四个主题。这个会议被称为 DVQ(Dario Vision Quest),这个名字他曾试图反对,因为"听起来像是我要去吸食仙人掌致幻剂"——内容涵盖内部发展、模型生产、产品、更广泛的 AI 行业以及与 AI 相关的地缘政治。他诚实地呈现自己和 Anthropic 领导层的想法,然后回答问题。这种"直接联系具有很大价值,这在信息经过六级传递后很难实现",公司的很大一部分员工会亲自或远程参加。

### Topic 83: Slack Channel and Unfiltered Internal Communication
Slack 频道与内部无过滤沟通
_[2:20:27]_

**Q:** How does the CEO communicate outside of the formal bi-weekly meetings, and what philosophy guides that communication?
**问：** CEO 如何在正式的双周会议之外进行沟通,什么理念指导这种沟通?

**A:** Speaker A maintains a Slack channel where he writes extensively and comments frequently, often responding to what he observes at the company or questions people ask. When internal surveys reveal concerns, he writes them up directly. His philosophy is being "very honest" and direct, aiming to "get a reputation of telling the company the truth about what's happening, to call things what they are, to acknowledge problems, to avoid the sort of corpo speak, the kind of defensive communication that often is necessary in public." With a company of trusted people, he can be "entirely unfiltered," which he views as "an enormous strength" that makes it a better workplace, makes people "more than the sum of their parts," and increases the likelihood of accomplishing the mission because everyone debates and discusses how to achieve it from the same shared understanding.
**答：** Speaker A 维护一个 Slack 频道,在那里他大量写作和频繁评论,通常是回应他在公司观察到的事情或员工提出的问题。当内部调查显示员工的担忧时,他会直接写出来。他的理念是"非常诚实"和直接,目标是"获得向公司说实话的声誉,直呼其名,承认问题,避免那种企业官话、那种在公开场合往往必要的防御性沟通"。在一个由值得信任的人组成的公司里,他可以"完全不经过滤",他认为这是"公司的巨大优势",让工作场所更好,让员工"超越个体之和",并提高完成使命的可能性,因为每个人都从相同的共识出发辩论和讨论如何实现使命。

### Topic 84: Interview Closing and Meta-Commentary
访谈结束与元评论
_[2:21:46]_

**Q:** How do the interviewer and CEO characterize this conversation as it concludes?
**问：** 访谈者和 CEO 在访谈结束时如何描述这次对话?

**A:** Speaker B (the interviewer, Dwarkesh) characterizes the interview itself as serving a similar function to the internal DVQ sessions—"in lieu of an external Dario Vision Quest, we have this interview." Speaker A agrees with this framing, acknowledging "this interview is a little like that." The exchange provides meta-commentary on how the interview has functioned as a public-facing analog to Dario's internal transparent communication practices, offering external audiences similar direct access to his thinking that Anthropic employees receive internally.
**答：** Speaker B(访谈者 Dwarkesh)将访谈本身描述为与内部 DVQ 会议具有类似功能——"作为外部 Dario Vision Quest 的替代,我们有这次访谈"。Speaker A 同意这一框架,承认"这次访谈有点像那样"。这段交流对访谈本身进行了元评论,说明访谈如何充当了 Dario 内部透明沟通实践的公开版本,为外部受众提供了与 Anthropic 员工在内部获得的类似的直接接触他思维的机会。

---

## Vocabulary (CEFR B2+)

### exponential  /ˌek.spəˈnen.ʃəl/
**CEFR:** C1 | **Part of speech:** n./adj. | **Occurrences:** 11

**EN:** a rate of growth or change that becomes increasingly rapid in proportion to the growing total; relating to such growth  
**CN:** 指数增长；指数的

**Original examples:**
- [00:10] Broadly speaking, the **exponential** of the underlying technology has gone about as I expected it to go.  
  从广义上讲，底层技术的指数增长与我预期的基本一致。
- [00:44] What has been the most surprising thing is the lack of public recognition of how close we are to the end of the **exponential**.  
  最令人惊讶的是公众缺乏认识，不知道我们离指数增长的终点有多近。
- [01:02] To me, it is absolutely wild that you have people — within the bubble and outside the bubble — talking about the same tired, old hot-button political issues, when we are near the end of the **exponential**.  
  对我来说，当我们接近指数增长的终点时，圈内和圈外的人还在讨论那些老掉牙的政治热点问题，这简直太疯狂了。
- [01:31] I have a similar question now, but it feels more complicated. At least from the public's point of view, three years ago there were well-known public trends across many orders of magnitude of compute where you could see how the loss improves.  
  我现在有一个类似的问题，但感觉更复杂了。至少从公众的角度来看，三年前在许多数量级的计算中有众所周知的公共趋势，你可以看到损失是如何改善的。
- [21:00] Can't you just draw an **exponential** line on the curve?  
  你不能就在曲线上画一条指数线吗？
- [21:23] But what we've seen from the beginning, at least if you look within Anthropic, there's this bizarre 10x per year growth in revenue that we've seen.  
  但从一开始我们就看到，至少如果你看Anthropic内部，我们看到了这种奇怪的每年10倍的收入增长。
- [21:50] And the first month of this year, that **exponential** is...  
  今年第一个月，那个指数增长是……
- [23:10] So I think everything we've seen so far is compatible with the idea that there's one fast **exponential** that's the capability of the model. Then there's another fast **exponential** that's downstream of that, which is the diffusion of the model into the economy.  
  所以我认为到目前为止我们看到的一切都与这个想法兼容：有一个快速的指数增长，即模型的能力。然后还有另一个快速的指数增长，这是其下游，即模型在经济中的扩散。
- [38:41] My theme in all of this is all of this is soft takeoff, soft, smooth **exponentials**, although the **exponentials** are relatively steep.  
  我在所有这些问题上的主题是，这一切都是软起飞，平滑的指数增长，尽管指数曲线相对陡峭。
- [01:51:42] I think the **exponential** of the underlying technology will continue as it has before.  
  我认为底层技术的指数增长将像以前一样继续。
- [01:52:12] Putting that aside, I do think the **exponential** will continue, but there will be certain distinguished points on the **exponential**.  
  撇开这一点，我确实认为指数增长会继续，但在指数曲线上会有某些显著的点。

**Extra example:**
- The company's user base grew at an **exponential** rate after the product launch.  
  产品发布后，公司的用户群呈指数级增长。

### frontier  /frʌnˈtɪr/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 4

**EN:** the extreme limit of understanding or achievement in a particular area  
**CN:** 前沿，边界

**Original examples:**
- [00:44] The **frontier** is a little bit uneven, but it's roughly what I expected.  
  前沿有点不均衡，但大致符合我的预期。
- [01:11:05] But of course, a big part of the production function of being a **frontier** lab is training the next model, right?  
  但当然，作为前沿实验室的生产函数的一个重要部分是训练下一个模型，对吧？
- [01:12:22] The world where **frontier** labs are making money is one where they continue to make fast progress.  
  前沿实验室赚钱的世界是一个它们继续快速进步的世界。
- [01:12:34] So you are able to make money because you have a **frontier** model.  
  所以你能够赚钱是因为你有一个前沿模型。

**Extra example:**
- Quantum computing represents the next **frontier** in computational power.  
  量子计算代表了计算能力的下一个前沿。

### recognition  /ˌrek.əɡˈnɪʃ.ən/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** acknowledgment or awareness of something; the act of identifying something previously known  
**CN:** 认识，承认，识别

**Original examples:**
- [00:44] What has been the most surprising thing is the lack of public **recognition** of how close we are to the end of the exponential.  
  最令人惊讶的是公众缺乏认识，不知道我们离指数增长的终点有多近。

**Extra example:**
- The CEO finally gave **recognition** to the engineering team's contributions.  
  首席执行官终于认可了工程团队的贡献。

### hypothesis  /haɪˈpɑː.θə.sɪs/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 3

**EN:** a proposed explanation or theory that can be tested through research  
**CN:** 假设，假说

**Original examples:**
- [01:45] What is the scaling **hypothesis** at this point?  
  目前的扩展假设是什么？
- [01:59] I actually have the same **hypothesis** I had even all the way back in 2017.  
  实际上，我现在的假设和2017年时的一样。
- [04:11] That was the **hypothesis**, and it's a **hypothesis** I still hold.  
  这就是那个假设，也是我仍然坚持的假设。

**Extra example:**
- The research team developed a **hypothesis** to explain the unexpected results.  
  研究团队提出了一个假设来解释这些意外结果。

### meta-learning  /ˈmet.ə ˈlɜːr.nɪŋ/
**CEFR:** C2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** learning how to learn; the ability to acquire learning strategies and adapt them to new contexts  
**CN:** 元学习，学习如何学习

**Original examples:**
- [01:45] Is it supposed to be teaching the model skills? Is it supposed to be teaching **meta-learning**?  
  它是应该教模型技能，还是应该教元学习？

**Extra example:**
- **Meta-learning** enables models to quickly adapt to new tasks with minimal examples.  
  元学习使模型能够用最少的示例快速适应新任务。

### blob  /blɑːb/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 4

**EN:** an indistinct or shapeless form or object; in technical contexts, a large undifferentiated mass  
**CN:** 一团，一块；大而无形的物体

**Original examples:**
- [01:59] I think I talked about it last time, but I wrote a doc called 'The Big **Blob** of Compute Hypothesis.'  
  我想我上次谈过，但我写了一篇名为《大计算团假设》的文档。
- [04:11] Then the sixth and seventh were things around normalization or conditioning, just getting the numerical stability so that the big **blob** of compute flows in this laminar way instead of running into problems.  
  第六和第七是关于归一化或条件化的事情，只是为了获得数值稳定性，使大计算团以层流方式流动，而不是遇到问题。
- [01:20:40] I think there just might not be such a thing at all. In fact, I would point to the history In ML, of people coming up with things that are barriers that end up kind of dissolving within the big **blob** of compute.  
  我认为可能根本就没有这样的东西。事实上，我会指出机器学习的历史，人们提出的一些被认为是障碍的东西最终都在计算的大团中消解了。
- [01:27:19] I take your point that people will have to try things to figure out what is the best way to use this **blob** of intelligence.  
  我接受你的观点，人们必须尝试不同的方法来找出使用这团智能的最佳方式。

**Extra example:**
- The neural network processes data as one large **blob** rather than discrete units.  
  神经网络将数据作为一个大块而不是离散单元来处理。

### objective function  /əbˈdʒek.tɪv ˈfʌŋk.ʃən/
**CEFR:** C2 | **Part of speech:** n. | **Occurrences:** 2

**EN:** in machine learning, the mathematical function that the algorithm aims to optimize during training  
**CN:** 目标函数，优化函数

**Original examples:**
- [03:20] The fifth is that you need an **objective function** that can scale to the moon.  
  第五是你需要一个可以无限扩展的目标函数。
- [03:27] The pre-training **objective function** is one such **objective function**.  
  预训练目标函数就是这样一个目标函数。

**Extra example:**
- Researchers carefully designed the **objective function** to minimize prediction errors.  
  研究人员精心设计了目标函数以最小化预测误差。

### laminar  /ˈlæm.ɪ.nər/
**CEFR:** C2 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** flowing smoothly in parallel layers without disruption; orderly and stable  
**CN:** 层流的，平稳流动的

**Original examples:**
- [04:11] Then the sixth and seventh were things around normalization or conditioning, just getting the numerical stability so that the big blob of compute flows in this **laminar** way instead of running into problems.  
  第六和第七是关于归一化或条件化的事情，只是为了获得数值稳定性，使大计算团以层流方式流动，而不是遇到问题。

**Extra example:**
- The team optimized the pipeline to ensure **laminar** data flow through all processing stages.  
  团队优化了管道以确保数据在所有处理阶段平稳流动。

### log-linear  /lɑːɡ ˈlɪn.i.ər/
**CEFR:** C2 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** describing a relationship where one variable changes linearly with the logarithm of another  
**CN:** 对数线性的

**Original examples:**
- [04:55] We train the model on math contests — AIME or other things — and how well the model does is **log-linear** in how long we've trained it.  
  我们在数学竞赛——AIME或其他项目上训练模型，模型的表现与训练时间呈对数线性关系。

**Extra example:**
- The performance improvement showed a **log-linear** relationship with computational resources.  
  性能改进与计算资源呈对数线性关系。

### paraphrase  /ˈper.ə.freɪz/
**CEFR:** B2 | **Part of speech:** v. | **Occurrences:** 1

**EN:** to express the meaning of something using different words  
**CN:** 改述，转述

**Original examples:**
- [05:31] I don't know if this is his perspective, but one way to **paraphrase** his objection is: Something which possesses the true core of human learning would not require all these billions of dollars of data and compute and these bespoke environments, to learn how to use Excel, how to use PowerPoint, how to navigate a web browser.  
  我不知道这是否是他的观点，但改述他的反对意见的一种方式是：真正拥有人类学习核心的东西不需要所有这些数十亿美元的数据和计算以及这些定制环境来学习如何使用Excel、如何使用PowerPoint、如何浏览网页。

**Extra example:**
- Let me **paraphrase** your argument to make sure I understand it correctly.  
  让我改述一下你的论点，以确保我理解正确。

### bespoke  /bɪˈspoʊk/
**CEFR:** C1 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** custom-made or specially designed for a particular purpose or client  
**CN:** 定制的，特制的

**Original examples:**
- [05:31] Something which possesses the true core of human learning would not require all these billions of dollars of data and compute and these **bespoke** environments, to learn how to use Excel, how to use PowerPoint, how to navigate a web browser.  
  真正拥有人类学习核心的东西不需要所有这些数十亿美元的数据和计算以及这些定制环境来学习如何使用Excel、如何使用PowerPoint、如何浏览网页。

**Extra example:**
- The startup developed a **bespoke** solution tailored to each client's specific needs.  
  这家初创公司开发了针对每个客户特定需求量身定制的解决方案。

### hint  /hɪnt/
**CEFR:** B2 | **Part of speech:** v. | **Occurrences:** 2

**EN:** to suggest or indicate something indirectly  
**CN:** 暗示，提示

**Original examples:**
- [05:57] The fact that we have to build in these skills using these RL environments **hints** that we are actually lacking a core human learning algorithm.  
  我们必须使用这些强化学习环境来构建这些技能这一事实暗示我们实际上缺少核心的人类学习算法。
- [15:48] But the emphasis on verification **hints** to me a lack of belief that these models are generalized.  
  但对验证的强调暗示我，这些模型缺乏泛化性。

**Extra example:**
- The recent hiring freeze **hints** at potential budget cuts next quarter.  
  最近的招聘冻结暗示下个季度可能会削减预算。

### red herring  /red ˈher.ɪŋ/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** something that misleads or distracts from the relevant or important issue  
**CN:** 转移注意力的事物，误导性线索

**Original examples:**
- [06:29] Let me take the RL out of it for a second, because I actually think it's a **red herring** to say that RL is any different from pre-training in this matter.  
  让我暂时把强化学习排除在外，因为我实际上认为说强化学习在这个问题上与预训练不同是一个误导。

**Extra example:**
- The discussion about coding style was a **red herring** — the real issue was the architecture design.  
  关于编码风格的讨论是一个误导——真正的问题是架构设计。

### generalization  /ˌdʒen.ər.əl.ɪˈzeɪ.ʃən/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 6

**EN:** the ability to apply learned knowledge or skills to new, unseen situations  
**CN:** 泛化，推广能力

**Original examples:**
- [07:32] It didn't generalize well. If you did better on some fanfiction corpus, it wouldn't **generalize** that well to other tasks.  
  它的泛化能力不好。如果你在某个同人小说语料库上做得更好，它也不会很好地泛化到其他任务。
- [08:01] It was only when you trained over all the tasks on the internet — when you did a general internet scrape from something like Common Crawl or scraping links in Reddit, which is what we did for GPT-2 — that you started to get **generalization**.  
  只有当你在互联网上的所有任务上进行训练时——当你从Common Crawl之类的东西或Reddit中的链接进行一般性网络抓取时，也就是我们为GPT-2所做的——你才开始获得泛化。
- [08:31] I think then we're going to increasingly get **generalization**.  
  我认为那样我们将越来越多地获得泛化。
- [12:08] So to the extent that we are building these RL environments, the goal is very similar to what was done five or ten years ago with pre-training. We're trying to get a whole bunch of data, not because we want to cover a specific document or a specific skill, but because we want to **generalize**.  
  因此，就我们正在构建这些强化学习环境而言，目标与五到十年前预训练所做的非常相似。我们试图获得大量数据，不是因为我们想覆盖特定的文档或特定的技能，而是因为我们想要泛化。
- [16:03] We already see substantial **generalization** from things that verify to things that don't.  
  我们已经看到从可验证的事物到不可验证的事物之间有实质性的泛化。
- [16:40] Even if **generalization** is weak and you can only do verifiable domains, it's not clear to me you could automate software engineering in such a world.  
  即使泛化能力很弱，你只能做可验证的领域，我也不清楚在这样的世界里你能否自动化软件工程。

**Extra example:**
- The model's strong **generalization** allowed it to perform well on tasks it had never seen during training.  
  该模型强大的泛化能力使其在训练期间从未见过的任务上表现良好。

### sample efficiency  /ˈsæm.pəl ɪˈfɪʃ.ən.si/
**CEFR:** C2 | **Part of speech:** n. | **Occurrences:** 2

**EN:** the ability to learn effectively from a small amount of training data  
**CN:** 样本效率，数据效率

**Original examples:**
- [08:50] So there is an actual **sample efficiency** difference here.  
  所以这里确实存在样本效率差异。
- [10:40] For example, if the analogy is that this is like evolution so it's fine that it's not **sample efficient**, then if we're going to get super **sample-efficient** agents from in-context learning, why are we bothering to build all these RL environments?  
  例如，如果这个类比就像进化一样，所以样本效率不高也没关系，那么如果我们要从上下文学习中获得超高样本效率的智能体，我们为什么还要费心构建所有这些强化学习环境？

**Extra example:**
- The new algorithm demonstrates remarkable **sample efficiency**, requiring only 10% of the training data.  
  新算法展示了卓越的样本效率，只需要10%的训练数据。

### in-context learning  /ɪn ˈkɑːn.tekst ˈlɜːr.nɪŋ/
**CEFR:** C2 | **Part of speech:** n. | **Occurrences:** 3

**EN:** the ability of a model to learn and adapt within a single interaction or prompt without parameter updates  
**CN:** 上下文学习，情境学习

**Original examples:**
- [09:06] But we also see that once they're trained, if we give them a long context length of a million — the only thing blocking long context is inference — they're very good at learning and adapting within that context.  
  但我们也看到，一旦它们被训练好，如果我们给它们一百万的长上下文长度——阻碍长上下文的唯一因素是推理——它们非常擅长在该上下文中学习和适应。
- [10:02] And we should think of the **in-context learning** that the models do as something between long-term human learning and short-term human learning.  
  我们应该把模型进行的上下文学习看作是介于人类长期学习和短期学习之间的东西。
- [10:40] For example, if the analogy is that this is like evolution so it's fine that it's not sample efficient, then if we're going to get super sample-efficient agents from **in-context learning**, why are we bothering to build all these RL environments?  
  例如，如果这个类比就像进化一样，所以样本效率不高也没关系，那么如果我们要从上下文学习中获得超高样本效率的智能体，我们为什么还要费心构建所有这些强化学习环境？

**Extra example:**
- Through **in-context learning**, the model can solve new math problems by studying a few examples in the prompt.  
  通过上下文学习，模型可以通过在提示中研究几个例子来解决新的数学问题。

### spectrum  /ˈspek.trəm/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 8

**EN:** a continuous range or sequence between two extremes  
**CN:** 范围，连续统，光谱

**Original examples:**
- [09:50] Maybe we should think of pre-training — and for that matter, RL as well — as something that exists in the middle space between human evolution and human on-the-spot learning.  
  也许我们应该把预训练——以及强化学习——看作是存在于人类进化和人类即时学习之间的中间空间的东西。
- [10:10] So there's this hierarchy. There's evolution, there's long-term learning, there's short-term learning, and there's just human reaction. The LLM phases exist along this **spectrum**, but not necessarily at exactly the same points.  
  所以有这样一个层次结构。有进化，有长期学习，有短期学习，还有人类的即时反应。大语言模型的阶段存在于这个连续统中，但不一定在完全相同的点上。
- [16:15] This as a **spectrum** which will split apart which domains in which we see more progress.  
  这是一个连续统，它将分裂出我们看到更多进展的领域。
- [18:03] Let me lay out the **spectrum**.  
  让我列出这个范围。
- [18:32] The **spectrum** is: 90% of code is written by the model, 100% of code is written by the model.  
  这个范围是：90%的代码由模型编写，100%的代码由模型编写。
- [19:10] Then further down the **spectrum**, there's 90% less demand for SWEs, which I think will happen but this is a **spectrum**.  
  然后在这个范围的更远端，对软件工程师的需求减少90%，我认为这会发生，但这是一个范围。
- [01:22:17] No, I gave this **spectrum**: 90% of code, 100% of code, 90% of end-to-end SWE, 100% of end-to-end SWE.  
  不，我给出了这个范围：90%的代码，100%的代码，90%的端到端软件工程，100%的端到端软件工程。
- [01:22:31] It's a long **spectrum** there, but we're traversing the **spectrum** very quickly.  
  这是一个很长的范围，但我们正在非常快速地穿越这个范围。

**Extra example:**
- The company's products target customers across the entire price **spectrum**.  
  该公司的产品面向整个价格范围的客户。

### analog  /ˈæn.ə.lɑːɡ/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** something that is comparable or similar to something else  
**CN:** 类似物，相似物

**Original examples:**
- [10:22] There's no **analog** to some of the human modes of learning.  
  人类的某些学习模式没有对应物。

**Extra example:**
- The CEO role in a startup is not a direct **analog** to the same position in a large corporation.  
  初创公司的CEO角色与大公司中的相同职位不是直接对应的。

### analogy  /əˈnæl.ə.dʒi/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a comparison between two things to show how they are similar  
**CN:** 类比，相似之处

**Original examples:**
- [10:40] For example, if the **analogy** is that this is like evolution so it's fine that it's not sample efficient, then if we're going to get super sample-efficient agents from in-context learning, why are we bothering to build all these RL environments?  
  例如，如果这个类比就像进化一样，所以样本效率不高也没关系，那么如果我们要从上下文学习中获得超高样本效率的智能体，我们为什么还要费心构建所有这些强化学习环境？

**Extra example:**
- She used the **analogy** of a highway to explain how network bandwidth works.  
  她用高速公路的类比来解释网络带宽是如何工作的。

### misunderstand  /ˌmɪs.ʌn.dərˈstænd/
**CEFR:** B2 | **Part of speech:** v. | **Occurrences:** 1

**EN:** to fail to understand something correctly  
**CN:** 误解，理解错误

**Original examples:**
- [17:57] I think people have repeatedly **misunderstood** them.  
  我认为人们反复地误解了它们。

**Extra example:**
- Many investors **misunderstood** the company's long-term strategy.  
  许多投资者误解了公司的长期战略。

### downstream  /ˌdaʊnˈstriːm/
**CEFR:** C1 | **Part of speech:** adj./adv. | **Occurrences:** 2

**EN:** occurring or situated later in a sequence or process  
**CN:** 下游的，后续的

**Original examples:**
- [18:16] It happened at Anthropic, happened with many people **downstream** using our models.  
  它在Anthropic发生了，在许多下游使用我们模型的人那里发生了。
- [23:10] Then there's another fast exponential that's **downstream** of that, which is the diffusion of the model into the economy.  
  然后还有另一个快速的指数增长，这是其下游，即模型在经济中的扩散。

**Extra example:**
- The manufacturing delays had **downstream** effects on product launches and customer satisfaction.  
  制造延迟对产品发布和客户满意度产生了下游影响。

### criterion  /kraɪˈtɪr.i.ən/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a standard or principle by which something is judged or decided  
**CN:** 标准，准则

**Original examples:**
- [18:21] But that's actually a very weak **criterion**.  
  但那实际上是一个非常弱的标准。

**Extra example:**
- The main **criterion** for hiring is relevant experience in the field.  
  招聘的主要标准是在该领域的相关经验。

### productivity  /ˌproʊ.dʌkˈtɪv.ə.t̬i/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 3

**EN:** the rate at which goods are produced or work is completed  
**CN:** 生产力，生产率

**Original examples:**
- [18:32] That's a big difference in **productivity**.  
  这是生产力的巨大差异。
- [19:32] Part of your vision is that going from 90 to 100 is going to happen fast, and that it leads to huge **productivity** improvements.  
  你愿景的一部分是从90到100会很快发生，并且会带来巨大的生产力提升。
- [26:35] This is why it makes us more **productive**.  
  这就是为什么它让我们更有生产力。

**Extra example:**
- Remote work has increased **productivity** for many knowledge workers.  
  远程工作提高了许多知识工作者的生产力。

### end-to-end  /ˌend.tuˈend/
**CEFR:** C1 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** covering or including the entire process from beginning to completion  
**CN:** 端到端的，全流程的

**Original examples:**
- [18:41] 90% of the **end-to-end** SWE tasks — including things like compiling, setting up clusters and environments, testing features, writing memos — are done by the models.  
  90%的端到端软件工程任务——包括编译、设置集群和环境、测试功能、编写备忘录——都由模型完成。

**Extra example:**
- The company provides **end-to-end** solutions for supply chain management.  
  该公司为供应链管理提供端到端解决方案。

### benchmark  /ˈbentʃ.mɑːrk/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 2

**EN:** a standard or point of reference against which things may be compared or assessed  
**CN:** 基准，标准

**Original examples:**
- [19:29] These are very different **benchmarks** from each other, but we're proceeding through them super fast.  
  这些是彼此非常不同的基准，但我们正在非常快速地通过它们。
- [32:20] We've seen this climb in **benchmarks**, and **benchmarks** are always imperfect measures.  
  我们看到了在基准测试中的攀升，而基准测试总是不完美的衡量标准。

**Extra example:**
- The new processor exceeded industry **benchmarks** for speed and efficiency.  
  新处理器超过了行业速度和效率基准。

### greenfield  /ˈɡriːn.fiːld/
**CEFR:** C2 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** describing a project that lacks constraints from prior work or existing infrastructure  
**CN:** 绿地的，全新的（项目）

**Original examples:**
- [19:45] But what I notice is that even in **greenfield** projects people start with Claude Code or something, people report starting a lot of projects…  
  但我注意到的是，即使在绿地项目中，人们用Claude Code或类似工具开始，人们报告启动了很多项目……

**Extra example:**
- Building a **greenfield** factory allows companies to implement the latest technology from day one.  
  建造绿地工厂使公司能够从第一天起就实施最新技术。

### renaissance  /ˈren.ə.sɑːns/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 2

**EN:** a period of new growth, activity, or interest in something  
**CN:** 复兴，新生

**Original examples:**
- [19:54] A **renaissance** of software, all these new features that wouldn't exist otherwise?  
  软件的复兴，所有这些否则不会存在的新功能？
- [35:37] Where is this **renaissance** of software?  
  软件的复兴在哪里？

**Extra example:**
- The city is experiencing a cultural **renaissance** with new museums and art galleries opening.  
  随着新博物馆和艺术画廊的开放，这座城市正在经历文化复兴。

### intervene  /ˌɪn.t̬ərˈviːn/
**CEFR:** B2 | **Part of speech:** v. | **Occurrences:** 1

**EN:** to become involved in a situation in order to improve or help it  
**CN:** 干预，介入

**Original examples:**
- [20:02] Even if I never had to **intervene** with Claude Code, the world is complicated.  
  即使我从不需要干预Claude Code，世界也是复杂的。

**Extra example:**
- The manager had to **intervene** when the team conflict escalated.  
  当团队冲突升级时，经理不得不介入。

### self-contained  /ˌself.kənˈteɪnd/
**CEFR:** C1 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** complete in itself and not needing outside help or support  
**CN:** 独立的，自给自足的

**Original examples:**
- [20:09] Closing the loop on **self-contained** systems, whether it's just writing software or something, how much broader gains would we see just from that?  
  在自给自足的系统上闭环，无论是仅仅编写软件还是其他，仅从这一点我们能看到多大的更广泛收益？

**Extra example:**
- Each apartment is **self-contained** with its own kitchen and bathroom.  
  每套公寓都是独立的，配有自己的厨房和浴室。

### dilute  /daɪˈluːt/
**CEFR:** C1 | **Part of speech:** v. | **Occurrences:** 1

**EN:** to make something weaker or less effective  
**CN:** 稀释，削弱

**Original examples:**
- [20:20] Maybe that should **dilute** our estimation of the 'country of geniuses'.  
  也许这应该削弱我们对'天才之国'的估计。

**Extra example:**
- Adding too many priorities will **dilute** the team's focus and effectiveness.  
  增加太多优先事项会削弱团队的专注力和效率。

### simultaneously  /ˌsaɪ.məlˈteɪ.ni.əs.li/
**CEFR:** B2 | **Part of speech:** adv. | **Occurrences:** 1

**EN:** at the same time  
**CN:** 同时地

**Original examples:**
- [20:24] I **simultaneously** agree with you that it's a reason why these things don't happen instantly, but at the same time, I think the effect is gonna be very fast.  
  我同时同意你的观点，这是这些事情不会立即发生的原因，但同时，我认为效果会非常快。

**Extra example:**
- The company is **simultaneously** expanding into new markets while cutting costs.  
  该公司在削减成本的同时，正在同时扩展到新市场。

### pole  /poʊl/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** either of two opposite or contradictory positions or viewpoints  
**CN:** 极端，两极之一

**Original examples:**
- [20:41] You could have these two **poles**.  
  你可以有这两个极端。

**Extra example:**
- The debate tends to swing between two **poles**: complete regulation or total freedom.  
  辩论往往在两个极端之间摇摆：完全监管或完全自由。

### diffusion  /dɪˈfjuː.ʒən/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 15

**EN:** the process by which something spreads more widely  
**CN:** 扩散，传播

**Original examples:**
- [20:41] It's slow. It's going to take forever to diffuse within the economy.  
  它很慢。在经济中扩散需要永远。
- [20:52] Economic **diffusion** has become one of these buzzwords that's a reason why we're not going to make AI progress, or why AI progress doesn't matter.  
  经济扩散已经成为这些流行词之一，成为我们不会取得AI进步或AI进步无关紧要的原因。
- [22:25] So I think we should be thinking about this middle world where things are extremely fast, but not instant, where they take time because of economic **diffusion**, because of the need to close the loop.  
  所以我认为我们应该思考这个中间世界，在那里事情非常快，但不是瞬间的，它们需要时间是因为经济扩散，因为需要闭环。
- [23:10] Then there's another fast exponential that's downstream of that, which is the **diffusion** of the model into the economy.  
  然后还有另一个快速的指数增长，这是其下游，即模型在经济中的扩散。
- [23:45] I feel like **diffusion** is cope that people say.  
  我觉得扩散是人们说的应对说辞。
- [24:34] I think **diffusion** is very real and doesn't exclusively have to do with limitations on the AI models.  
  我认为扩散非常真实，并不完全与AI模型的局限性有关。
- [24:49] I'm not talking about how AI will diffuse at the speed of previous technologies.  
  我不是在谈论AI将以以前技术的速度扩散。
- [24:58] I think AI will diffuse much faster than previous technologies have, but not infinitely fast.  
  我认为AI的扩散速度会比以前的技术快得多，但不是无限快。
- [50:20] I don't expect most of the economic **diffusion** to be as difficult as that.  
  我不认为大部分的经济扩散会像那样困难。
- [01:19:32] My answer there is yes, but there will be the same extremely fast, but not infinitely fast **diffusion**.  
  我的答案是肯定的，但将会有同样极快但不是无限快的扩散。
- [01:31:30] It seems like whatever vision we have about how AI goes well has to be compatible with two things: 1) the ability to build and run AIs is **diffusing** extremely rapidly and 2) the population of AIs, the amount we have and their intelligence, will also increase very rapidly.  
  似乎无论我们对AI如何良好发展有什么愿景，都必须与两件事兼容：1）构建和运行AI的能力正在极快地扩散，2）AI的数量、我们拥有的数量及其智能也将非常迅速地增加。
- [01:40:42] The benefits are, as you say because of **diffusion** lag, slow enough that I really do think this patchwork of state laws would prohibit.  
  正如你所说，由于扩散滞后，好处来得足够慢，我真的认为这种零散拼凑的州法律会起到禁止作用。
- [01:48:43] But this seems like a fully general argument against the **diffusion** of AI technology.  
  但这似乎是一个反对人工智能技术扩散的完全普遍性论点。
- [01:48:52] Because I think we will get **diffusion** eventually.  
  因为我认为我们最终会实现扩散。
- [01:50:55] In fact, being that far out, it just seems like progress continues, AI improves, AI is more **diffused**, and people will use it for more things.  
  事实上，展望那么远，似乎进展会继续，人工智能会改进，人工智能会更加扩散，人们会用它做更多的事情。

**Extra example:**
- The **diffusion** of smartphones into developing countries took less than a decade.  
  智能手机在发展中国家的扩散用了不到十年时间。

### recursive  /rɪˈkɜːr.sɪv/
**CEFR:** C2 | **Part of speech:** adj. | **Occurrences:** 3

**EN:** characterized by a process in which each stage depends on or builds upon the previous one, often in a self-referential way  
**CN:** 递归的，循环的

**Original examples:**
- [21:00] The other axis is that we'll get **recursive** self-improvement, the whole thing.  
  另一个轴是我们会得到递归的自我改进，整个过程。
- [21:08] We're going to have Dyson spheres around the sun so many nanoseconds after we get **recursive**.  
  在我们获得递归之后的许多纳秒内，我们将在太阳周围建造戴森球。
- [37:04] The idea was supposed to be that with **recursive** self-improvement, you make a better AI, the AI helps you build a better next AI, et cetera, et cetera.  
  这个想法本应是通过递归自我改进，你创造一个更好的AI，这个AI帮助你构建下一个更好的AI，如此循环。

**Extra example:**
- The algorithm uses a **recursive** function to search through nested data structures.  
  该算法使用递归函数来搜索嵌套的数据结构。

### caricature  /ˈker.ɪ.kə.tʃʊr/
**CEFR:** C1 | **Part of speech:** v./n. | **Occurrences:** 1

**EN:** to represent someone or something in an exaggerated or simplified way  
**CN:** 夸张地描述，讽刺画

**Original examples:**
- [21:08] I'm completely **caricaturing** the view here, but there are these two extremes.  
  我在这里完全是在夸张地描述这个观点，但存在这两个极端。

**Extra example:**
- Critics accused the film of **caricaturing** corporate executives as one-dimensional villains.  
  批评者指责这部电影将企业高管夸张地描绘成单一的反派。

### bizarre  /bɪˈzɑːr/
**CEFR:** B2 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** very strange and unusual  
**CN:** 奇异的，古怪的

**Original examples:**
- [21:23] But what we've seen from the beginning, at least if you look within Anthropic, there's this **bizarre** 10x per year growth in revenue that we've seen.  
  但从一开始我们就看到，至少如果你看Anthropic内部，我们看到了这种奇异的每年10倍的收入增长。

**Extra example:**
- It was **bizarre** that the company's stock price doubled despite declining profits.  
  尽管利润下降，但公司股价翻倍是很奇怪的。

### bend  /bend/
**CEFR:** B2 | **Part of speech:** v. | **Occurrences:** 1

**EN:** to change direction or shape; in this context, to slow down or deviate from a trend  
**CN:** 弯曲，改变（趋势）

**Original examples:**
- [22:10] I would even guess that it **bends** somewhat this year, but that is a fast curve.  
  我甚至猜测今年它会有所弯曲，但那是一条快速的曲线。

**Extra example:**
- The growth curve began to **bend** as the market reached saturation.  
  随着市场达到饱和，增长曲线开始弯曲。

### fiddly  /ˈfɪd.li/
**CEFR:** C1 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** needing careful attention to small details; complicated in an annoying way  
**CN:** 需要精细处理的，繁琐的

**Original examples:**
- [22:39] Because it's **fiddly**: 'I have to do change management within my enterprise...  
  因为它很繁琐：'我必须在我的企业内部进行变更管理……

**Extra example:**
- Setting up the authentication system was more **fiddly** than we expected.  
  设置身份验证系统比我们预期的更繁琐。

### compatible  /kəmˈpæt.ə.bəl/
**CEFR:** B2 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** able to exist or be used together without problems or conflict  
**CN:** 兼容的，一致的

**Original examples:**
- [23:10] So I think everything we've seen so far is **compatible** with the idea that there's one fast exponential that's the capability of the model.  
  所以我认为到目前为止我们看到的一切都与这个想法兼容：有一个快速的指数增长，即模型的能力。

**Extra example:**
- The new software is **compatible** with both Windows and Mac operating systems.  
  新软件与Windows和Mac操作系统都兼容。

### cope  /koʊp/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** (informal) an excuse or rationalization used to avoid facing an uncomfortable reality  
**CN:** 应对说辞，借口

**Original examples:**
- [23:45] I feel like diffusion is **cope** that people say.  
  我觉得扩散是人们说的应对说辞。

**Extra example:**
- Calling the criticism 'just jealousy' sounds like **cope** to avoid addressing the real issues.  
  把批评称为'只是嫉妒'听起来像是为了避免解决真正问题的应对说辞。

### qualitative  /ˈkwɑː.lə.teɪ.tɪv/
**CEFR:** C1 | **Part of speech:** adj. | **Occurrences:** 4

**EN:** relating to the quality or character of something rather than its quantity  
**CN:** 定性的，质量上的（而非数量上的）

**Original examples:**
- [35:05] There are people who **qualitatively** report what you're saying.  
  有些人在定性层面报告了你所说的情况。
- [35:37] I'm trying to square the **qualitative** feeling that people feel with these models versus, one, on a macro level, where is this renaissance of software?  
  我试图将人们对这些模型的主观感受与宏观层面的问题相协调：软件的复兴在哪里？
- [36:54] One, people feeling like they're productive is **qualitatively** predicted by studies like this.  
  首先，人们感觉自己很高效，这在定性上是此类研究所预测的。
- [43:37] People report **qualitative** degradation in the ability of the model to consider that full context.  
  人们报告说模型考虑全部上下文的能力出现了质量上的下降。

**Extra example:**
- The **qualitative** feedback from users suggested design issues that weren't visible in the quantitative data.  
  来自用户的定性反馈揭示了在定量数据中看不到的设计问题。

### uplift  /ˈʌp.lɪft/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** an improvement or increase in something, especially in performance or productivity  
**CN:** 提升，改善（尤指表现或生产力）

**Original examples:**
- [35:16] Those developers reported an **uplift**.  
  那些开发者报告了生产力的提升。

**Extra example:**
- The new training program resulted in a 30% **uplift** in employee satisfaction scores.  
  新的培训计划使员工满意度得分提升了30%。

### downlift  /ˈdaʊn.lɪft/
**CEFR:** C2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a decrease or decline in performance or productivity  
**CN:** 下降，降低（表现或生产力）

**Original examples:**
- [35:35] There was a 20% **downlift**.  
  结果出现了20%的下降。

**Extra example:**
- The rushed deployment caused a 15% **downlift** in user engagement metrics.  
  仓促的部署导致用户参与度指标下降了15%。

### unambiguous  /ˌʌn.æmˈbɪɡ.ju.əs/
**CEFR:** C1 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** clear and having only one possible meaning or interpretation  
**CN:** 明确的，毫不含糊的

**Original examples:**
- [35:53] Within Anthropic, this is just really **unambiguous**.  
  在Anthropic内部，这一点非常明确。

**Extra example:**
- The data provided **unambiguous** evidence that the new algorithm outperformed the baseline.  
  数据提供了明确的证据，表明新算法的表现优于基准。

### commercial  /kəˈmɜː.ʃəl/
**CEFR:** B2 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** relating to the buying and selling of goods and services; intended to make a profit  
**CN:** 商业的，营利的

**Original examples:**
- [35:53] We're under an incredible amount of **commercial** pressure.  
  我们面临着巨大的商业压力。

**Extra example:**
- The **commercial** viability of the project depends on securing funding within the next quarter.  
  该项目的商业可行性取决于在下个季度获得资金。

### curve  /kɜːrv/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a line or pattern that gradually bends; in business, a graph showing growth or decline  
**CN:** 曲线；（商业中的）增长曲线

**Original examples:**
- [36:03] We're trying to keep this 10x revenue **curve** going.  
  我们正在努力保持这条10倍收入增长曲线。

**Extra example:**
- The company's growth **curve** flattened after three years of rapid expansion.  
  经过三年的快速扩张后，公司的增长曲线趋于平缓。

### bullshit  /ˈbʊl.ʃɪt/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** (informal, vulgar) nonsense; things that are not true or not useful  
**CN:** 废话，胡扯（非正式、粗俗）

**Original examples:**
- [36:18] There is zero time for **bullshit**.  
  我们没有时间搞那些虚的。

**Extra example:**
- The CEO cut through the **bullshit** and told everyone exactly what the financial situation was.  
  首席执行官不说废话，直接告诉大家财务状况到底如何。

### podium  /ˈpoʊ.di.əm/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a platform or stand, especially one used by winners in a competition; metaphorically, a position of leadership or prominence  
**CN:** 领奖台；（比喻）领先地位

**Original examples:**
- [37:14] What I see instead—if I look at you, OpenAI, DeepMind—is that people are just shifting around the **podium** every few months.  
  相反，我看到的是——如果我观察你们、OpenAI、DeepMind——大家只是每隔几个月就在领奖台上换位置。

**Extra example:**
- The startup scene is competitive, with new companies constantly vying for a spot on the **podium**.  
  创业圈竞争激烈，新公司不断争夺领先地位。

### lasting  /ˈlæs.tɪŋ/
**CEFR:** B2 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** continuing to exist or have an effect for a long time  
**CN:** 持久的，长期的

**Original examples:**
- [37:22] Why are we not seeing the person with the best coding model have this **lasting** advantage?  
  为什么我们没有看到拥有最佳编码模型的人获得这种持久的优势？

**Extra example:**
- First-mover advantage rarely provides a **lasting** competitive edge in fast-moving tech markets.  
  在快速发展的科技市场中，先发优势很少能提供持久的竞争优势。

### factor  /ˈfæk.tɚ/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 2

**EN:** an element or circumstance that contributes to a result; in economics, a multiplier  
**CN:** 因素；（经济学中的）系数，倍数

**Original examples:**
- [37:38] The coding models give maybe, I don't know, a 15-20% total **factor** speed up.  
  编码模型带来的总体加速系数大概是15-20%。
- [38:06] It's now just getting to the point where it's one of several **factors** that kind of matters.  
  现在它刚刚达到成为几个重要因素之一的程度。

**Extra example:**
- Employee retention is a critical **factor** in maintaining institutional knowledge and team cohesion.  
  员工留任是保持机构知识和团队凝聚力的关键因素。

### register  /ˈredʒ.ɪ.stɚ/
**CEFR:** B2 | **Part of speech:** v. | **Occurrences:** 1

**EN:** to be noticed or recognized; to have a noticeable effect  
**CN:** 产生明显效果，被注意到

**Original examples:**
- [38:01] 5% doesn't **register**.  
  5%的提升不会产生明显效果。

**Extra example:**
- Small improvements won't **register** with users unless they cross a certain threshold of noticeable difference.  
  除非跨越某个可察觉差异的阈值，否则小的改进不会被用户注意到。

### snowball  /ˈsnoʊ.bɔːl/
**CEFR:** B2 | **Part of speech:** n./v. | **Occurrences:** 2

**EN:** a process or situation that grows rapidly, gaining momentum like a snowball rolling down a hill  
**CN:** 滚雪球（快速增长并获得动力的过程）

**Original examples:**
- [38:41] My theme in all of this is all of this is soft takeoff, soft, smooth exponentials, although the exponentials are relatively steep.  
  我在所有这些问题上的主题是，这一切都是软起飞，平滑的指数增长，尽管指数曲线相对陡峭。
- [39:00] So we're seeing this **snowball** gather momentum where it's like 10%, 20%, 25%, 40%.  
  所以我们看到这个雪球在积聚动力，从10%、20%、25%到40%。

**Extra example:**
- Once the viral marketing campaign started, user growth began to **snowball**, doubling every week.  
  一旦病毒式营销活动启动，用户增长就开始滚雪球般加速，每周翻倍。

### takeoff  /ˈteɪk.ɔːf/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** the moment when something begins to be successful or when growth accelerates; in aviation, when a plane leaves the ground  
**CN:** 起飞；（比喻）开始成功或加速增长的时刻

**Original examples:**
- [38:41] My theme in all of this is all of this is soft **takeoff**, soft, smooth exponentials.  
  我在所有这些问题上的主题是，这一切都是软起飞，平滑的指数增长。

**Extra example:**
- Many AI researchers debate whether artificial general intelligence will involve a gradual **takeoff** or sudden breakthrough.  
  许多AI研究人员争论人工通用智能是会经历渐进式起飞还是突然的突破。

### steep  /stiːp/
**CEFR:** B2 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** rising or falling sharply; (of a curve or increase) very rapid  
**CN:** 陡峭的；（增长）急剧的

**Original examples:**
- [38:41] Although the exponentials are relatively **steep**.  
  尽管指数曲线相对陡峭。

**Extra example:**
- The learning curve for the new framework is quite **steep**, but developers become productive after a few weeks.  
  新框架的学习曲线相当陡峭，但开发者在几周后就能提高效率。

### momentum  /moʊˈmen.təm/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** the force that keeps something moving or developing; the strength or speed of continuous growth  
**CN:** 动力，势头（使某事物持续发展的力量）

**Original examples:**
- [39:00] So we're seeing this snowball gather **momentum** where it's like 10%, 20%, 25%, 40%.  
  所以我们看到这个雪球在积聚动力，从10%、20%、25%到40%。

**Extra example:**
- The project lost **momentum** after the lead engineer left, and progress slowed significantly.  
  首席工程师离开后，项目失去了动力，进展明显放缓。

### Amdahl's law  /ˈæm.dɑːlz lɔː/
**CEFR:** C2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a formula in computer science showing that the speedup of a task is limited by the portion that cannot be parallelized  
**CN:** 阿姆达尔定律（计算机科学中的公式，表明任务的加速受不可并行化部分的限制）

**Original examples:**
- [39:00] As you go, **Amdahl's law**, you have to get all the things that are preventing you from closing the loop out of the way.  
  随着进展，根据阿姆达尔定律，你必须排除所有阻止你闭环的因素。

**Extra example:**
- According to **Amdahl's law**, if 20% of your code cannot be parallelized, the maximum speedup is limited no matter how many processors you add.  
  根据阿姆达尔定律，如果你20%的代码无法并行化，那么无论添加多少处理器，最大加速都会受到限制。

### loop  /luːp/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a complete cycle or circuit; in this context, 'closing the loop' means completing an autonomous cycle of improvement  
**CN:** 循环，闭环；（在此语境中）完成自主改进的完整周期

**Original examples:**
- [39:00] You have to get all the things that are preventing you from closing the **loop** out of the way.  
  你必须排除所有阻止你闭环的因素。

**Extra example:**
- To close the **loop** on automated deployment, we need monitoring systems that can detect and rollback failed releases.  
  要实现自动部署的闭环，我们需要能够检测和回滚失败发布的监控系统。

### stack  /stæk/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a hierarchical arrangement of elements; in technology, layers of software or hardware components  
**CN:** 堆栈；（技术中的）层级结构

**Original examples:**
- [39:17] Stepping back, before in the **stack** we were talking about when do we get this on-the-job learning?  
  退一步说，之前在讨论层级中，我们谈到什么时候能获得这种在职学习能力？

**Extra example:**
- Our technology **stack** includes React for the frontend, Node.js for the backend, and PostgreSQL for the database.  
  我们的技术栈包括用于前端的React、用于后端的Node.js和用于数据库的PostgreSQL。

### tremendous  /trəˈmen.dəs/
**CEFR:** B2 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** very great in amount, scale, or intensity  
**CN:** 巨大的，极大的

**Original examples:**
- [39:29] You can have **tremendous** productivity improvements.  
  你可以获得巨大的生产力提升。

**Extra example:**
- The shift to cloud infrastructure represented a **tremendous** cost saving for the organization.  
  向云基础设施的转变为组织带来了巨大的成本节约。

### trillion  /ˈtrɪl.jən/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 3

**EN:** the number 1,000,000,000,000; used to indicate enormous scale  
**CN:** 万亿（1,000,000,000,000）

**Original examples:**
- [39:29] You can have potentially **trillions** of dollars of revenue for AI companies.  
  AI公司可能获得数万亿美元的收入。
- [42:04] I certainly think that just as things are, this is enough to generate **trillions** of dollars of revenue.  
  我当然认为就目前情况而言，这足以产生数万亿美元的收入。
- [42:36] The **trillions** of dollars a year market, maybe all of the national security implications.  
  每年数万亿美元的市场，也许还有所有的国家安全影响。

**Extra example:**
- The global cloud computing market is projected to reach **trillions** of dollars by 2030.  
  全球云计算市场预计到2030年将达到数万亿美元。

### domain  /doʊˈmeɪn/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a specific area of knowledge, activity, or interest  
**CN:** 领域，范畴

**Original examples:**
- [39:40] But in most **domains** of economic activity, people say, 'I hired somebody, they weren't that useful for the first few months.'  
  但在大多数经济活动领域，人们会说：'我雇了某人，他们在最初几个月并不太有用。'

**Extra example:**
- Machine learning models often struggle when applied to a new **domain** without sufficient training data.  
  机器学习模型在应用于缺乏充分训练数据的新领域时往往会遇到困难。

### powerhouse  /ˈpaʊ.ɚ.haʊs/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a person or organization with great energy, strength, or power; someone extremely capable  
**CN:** 强大的人或组织；精英，强者

**Original examples:**
- [39:58] And then over time they built up the context, understanding. It's actually hard to define what we're talking about here. But they got something and then now they're a **powerhouse** and they're so valuable to us.  
  然后随着时间的推移，他们积累了背景知识和理解。实际上很难定义我们在这里谈论的是什么。但他们获得了某些东西，现在他们成了精英，对我们非常有价值。

**Extra example:**
- After two years with the team, she became a **powerhouse**, consistently delivering high-impact projects.  
  在团队工作两年后，她成了一个强者，持续交付高影响力的项目。

### dilemma  /dɪˈlemə/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a difficult situation requiring a choice between equally undesirable alternatives  
**CN:** 困境，两难局面

**Original examples:**
- [50:20] But there's a real **dilemma** here.  
  但这里确实存在一个两难困境。

**Extra example:**
- The company faces a **dilemma**: expand aggressively or maintain financial stability.  
  公司面临一个两难困境：激进扩张还是保持财务稳定。

### settle  /ˈsetəl/
**CEFR:** B2 | **Part of speech:** v. | **Occurrences:** 1

**EN:** to reach a decision or conclusion after consideration  
**CN:** 决定，得出结论

**Original examples:**
- [50:32] Where I've **settled** on it is that it will be faster than anything we've seen in the world, but it still has its limits.  
  我得出的结论是，它会比我们在世界上见过的任何东西都快，但它仍然有其局限性。

**Extra example:**
- After months of debate, the team **settled** on a hybrid work model.  
  经过几个月的讨论，团队决定采用混合办公模式。

### annualized  /ˈænjuəlaɪzd/
**CEFR:** C1 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** calculated or expressed as a yearly rate or amount  
**CN:** 年化的，按年计算的

**Original examples:**
- [50:47] At the beginning of this year, we're looking at $10 billion in **annualized** revenue.  
  在今年年初，我们的年化收入约为100亿美元。

**Extra example:**
- The fund's **annualized** return over the past decade was 12%.  
  该基金过去十年的年化回报率为12%。

### reserve  /rɪˈzɜːrv/
**CEFR:** B2 | **Part of speech:** v. | **Occurrences:** 1

**EN:** to arrange for something to be kept for future use or commitment  
**CN:** 预留，预订

**Original examples:**
- [50:54] It takes a year or two to actually build out the data centers, to **reserve** the data center.  
  实际建设数据中心、预留数据中心需要一到两年的时间。

**Extra example:**
- The company needs to **reserve** manufacturing capacity well in advance of product launches.  
  公司需要在产品发布前提前预留制造产能。

### hedge  /hedʒ/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a strategy or action that protects against financial loss or risk  
**CN:** 对冲，风险防范措施

**Original examples:**
- [51:39] If my revenue is not $1 trillion dollars, if it's even $800 billion, there's no force on earth, there's no **hedge** on earth that could stop me from going bankrupt if I buy that much compute.  
  如果我的收入不是1万亿美元，即使是8000亿美元，如果我购买那么多算力，地球上没有任何力量，没有任何对冲手段能阻止我破产。

**Extra example:**
- Investors use options as a **hedge** against market volatility.  
  投资者使用期权作为对冲市场波动的手段。

### bankrupt  /ˈbæŋkrʌpt/
**CEFR:** B2 | **Part of speech:** adj./v. | **Occurrences:** 3

**EN:** unable to pay debts; to cause financial ruin  
**CN:** 破产的；使破产

**Original examples:**
- [51:39] There's no force on earth, there's no hedge on earth that could stop me from going **bankrupt** if I buy that much compute.  
  地球上没有任何力量，没有任何对冲手段能阻止我破产，如果我购买那么多算力。
- [51:56] If I'm just off by a year in that rate of growth, or if the growth rate is 5x a year instead of 10x a year, then you go **bankrupt**.  
  如果我在增长率上只是偏差一年，或者增长率是每年5倍而不是10倍，那你就会破产。
- [55:50] But second, what if the country of geniuses comes, but it comes in mid-2028 instead of mid-2027? You go **bankrupt**.  
  但其次，如果天才之国出现了，但它是在2028年中而不是2027年中出现呢？你就会破产。

**Extra example:**
- The aggressive expansion strategy nearly **bankrupted** the startup.  
  激进的扩张战略几乎使这家初创公司破产。

### fickle  /ˈfɪkəl/
**CEFR:** C1 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** changing frequently and unpredictably; unreliable  
**CN:** 善变的，不稳定的

**Original examples:**
- [52:51] We're an enterprise business. Therefore, we can rely more on revenue. It's less **fickle** than consumer.  
  我们是企业业务。因此，我们可以更多地依赖收入。它不像消费者业务那么善变。

**Extra example:**
- Consumer trends are notoriously **fickle**, making long-term planning difficult.  
  消费者趋势出了名地善变，这使得长期规划变得困难。

### margin  /ˈmɑːrdʒɪn/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 3

**EN:** the difference between the cost of producing something and the price at which it is sold; profit margin  
**CN:** 利润率，毛利

**Original examples:**
- [52:55] We have better **margins**, which is the buffer between buying too much and buying too little.  
  我们有更好的利润率，这是购买过多和购买过少之间的缓冲。
- [01:00:02] The inference has some gross **margin** that's more than 50%.  
  推理有一些毛利率，超过50%。
- [01:00:45] Basically you're profitable and you make $50 billion of profit. Those are the economics of the industry today, or not today but where we're projecting forward in a year or two.  
  基本上你是盈利的，你赚了500亿美元的利润。这就是今天行业的经济状况，或者不是今天，而是我们向前预测一两年的情况。

**Extra example:**
- Software companies typically have higher **margins** than hardware manufacturers.  
  软件公司通常比硬件制造商有更高的利润率。

### buffer  /ˈbʌfər/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** something that reduces shock or protects against adverse conditions  
**CN:** 缓冲，缓冲物

**Original examples:**
- [52:55] We have better margins, which is the **buffer** between buying too much and buying too little.  
  我们有更好的利润率，这是购买过多和购买过少之间的缓冲。

**Extra example:**
- The company maintains a cash **buffer** to handle unexpected expenses.  
  公司保持现金缓冲以应对意外支出。

### upside  /ˈʌpsaɪd/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** the potential for profit, gain, or positive outcome  
**CN:** 上行空间，潜在收益

**Original examples:**
- [53:01] I think we bought an amount that allows us to capture pretty strong **upside** worlds.  
  我认为我们购买的数量让我们能够抓住相当强劲的上行空间。

**Extra example:**
- The stock has limited **upside** at current valuations.  
  按当前估值，这只股票的上行空间有限。

### capture  /ˈkæptʃər/
**CEFR:** B2 | **Part of speech:** v. | **Occurrences:** 2

**EN:** to take or seize (opportunity, value, market share); to obtain or record  
**CN:** 抓住，获取（机会、价值、市场份额）

**Original examples:**
- [53:01] I think we bought an amount that allows us to **capture** pretty strong upside worlds.  
  我认为我们购买的数量让我们能够抓住相当强劲的上行空间。
- [53:05] It won't **capture** the full 10x a year.  
  它不会抓住全部每年10倍的增长。

**Extra example:**
- The new product aims to **capture** 20% of the enterprise market within two years.  
  新产品旨在两年内抓住20%的企业市场。

### bottleneck  /ˈbɑːtəlnek/
**CEFR:** C1 | **Part of speech:** n./v. | **Occurrences:** 1

**EN:** a constraint or limiting factor that slows down a process or system  
**CN:** 瓶颈，制约因素

**Original examples:**
- [54:14] If they can't start their own company and they're **bottlenecked** by clinical trials…  
  如果他们不能创办自己的公司，而且受到临床试验的制约……

**Extra example:**
- Database queries became the main **bottleneck** limiting application performance.  
  数据库查询成为限制应用程序性能的主要瓶颈。

### efficacy  /ˈefɪkəsi/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** the ability to produce the desired result or effect; effectiveness  
**CN:** 功效，有效性

**Original examples:**
- [54:18] It is worth stating that with clinical trials, most clinical trials fail because the drug doesn't work. There's no **efficacy**.  
  值得说明的是，对于临床试验，大多数临床试验失败是因为药物不起作用。没有疗效。

**Extra example:**
- The vaccine demonstrated high **efficacy** in preventing severe disease.  
  该疫苗在预防严重疾病方面表现出很高的有效性。

### self-reinforcing  /self-riːɪnˈfɔːrsɪŋ/
**CEFR:** C1 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** describing a process that strengthens or amplifies itself through positive feedback  
**CN:** 自我强化的，形成良性循环的

**Original examples:**
- [54:50] You also think there are these **self-reinforcing** gains from smart people working on AI tech.  
  你还认为聪明人从事AI技术工作会产生这些自我强化的收益。

**Extra example:**
- Network effects create a **self-reinforcing** cycle where more users attract even more users.  
  网络效应创造了一个自我强化的循环，更多的用户会吸引更多的用户。

### comparable  /ˈkɑːmpərəbəl/
**CEFR:** B2 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** similar in size, amount, quality, or degree; able to be compared  
**CN:** 可比的，类似的

**Original examples:**
- [55:30] We're buying an amount that's **comparable** to what the biggest players in the game are buying.  
  我们购买的数量与这个领域最大的参与者购买的数量相当。

**Extra example:**
- The startup's growth rate is **comparable** to that of unicorn companies in previous years.  
  这家初创公司的增长率与往年独角兽公司的增长率相当。

### projection  /prəˈdʒekʃən/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** an estimate or forecast of a future situation based on current trends  
**CN:** 预测，预计

**Original examples:**
- [56:11] So if your **projection** is one to three years, it seems like you should want $10 trillion of compute by 2029 at the latest?  
  所以如果你的预测是一到三年，似乎你应该最迟在2029年想要10万亿美元的算力？

**Extra example:**
- Financial **projections** show the company reaching profitability by Q3.  
  财务预测显示公司将在第三季度实现盈利。

### timeline  /ˈtaɪmlaɪn/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a schedule or sequence of events; the expected duration for something to occur  
**CN:** 时间表，时间线

**Original examples:**
- [56:11] Even in the longest version of the **timelines** you state, the compute you are ramping up to build doesn't seem in accordance.  
  即使在你所说的最长版本的时间表中，你正在增加建设的算力似乎也不符合。

**Extra example:**
- The product launch **timeline** has been pushed back by three months.  
  产品发布时间表已被推迟三个月。

### ramp up  /ræmp ʌp/
**CEFR:** C1 | **Part of speech:** phrasal v. | **Occurrences:** 1

**EN:** to increase or scale up gradually; to accelerate production or activity  
**CN:** 逐步增加，加速增长

**Original examples:**
- [56:11] Even in the longest version of the timelines you state, the compute you are **ramping up** to build doesn't seem in accordance.  
  即使在你所说的最长版本的时间表中，你正在逐步增加建设的算力似乎也不符合。

**Extra example:**
- The factory is **ramping up** production to meet holiday demand.  
  工厂正在加速生产以满足假日需求。

### accordance  /əˈkɔːrdəns/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** agreement or conformity with something; being in harmony with  
**CN:** 一致，符合

**Original examples:**
- [56:16] Even in the longest version of the timelines you state, the compute you are ramping up to build doesn't seem in **accordance**.  
  即使在你所说的最长版本的时间表中，你正在逐步增加建设的算力似乎也不符合。

**Extra example:**
- All decisions must be made in **accordance** with company policy.  
  所有决定必须符合公司政策。

### gigawatt  /ˈɡɪɡəwɑːt/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 3

**EN:** a unit of power equal to one billion watts, often used to measure large-scale energy production or consumption  
**CN:** 吉瓦（10亿瓦特的功率单位）

**Original examples:**
- [56:27] So I won't talk about Anthropic in particular, but if you talk about the industry, the amount of compute the industry is building this year is probably, call it, 10-15 **gigawatts**.  
  所以我不会特别谈论Anthropic，但如果你谈论这个行业，今年该行业建设的算力大概是，可以说，10-15吉瓦。
- [56:48] It goes up by roughly 3x a year. So next year's 30-40 **gigawatts**. 2028 might be 100 **gigawatts**. 2029 might be like 300 **gigawatts**.  
  它每年大约增长3倍。所以明年是30-40吉瓦。2028年可能是100吉瓦。2029年可能像300吉瓦。
- [57:26] Suppose Anthropic's compute keeps 3x-ing a year, and then by 2027-28, you have 10 **gigawatts**.  
  假设Anthropic的算力每年保持3倍增长，那么到2027-28年，你有10吉瓦。

**Extra example:**
- The new solar farm will generate 2 **gigawatts** of clean energy.  
  新的太阳能发电场将产生2吉瓦的清洁能源。

### TAM  /tæm/ or /tiː-eɪ-em/
**CEFR:** C1 | **Part of speech:** n. (acronym) | **Occurrences:** 1

**EN:** Total Addressable Market; the total revenue opportunity available for a product or service  
**CN:** 总可获得市场（产品或服务的总收入机会）

**Original examples:**
- [57:40] But then you're saying the **TAM** by 2028 is $200 billion.  
  但你说的是到2028年TAM是2000亿美元。

**Extra example:**
- Investors want to see a large **TAM** before committing to Series A funding.  
  投资者希望在承诺A轮融资之前看到一个庞大的总可获得市场。

### profitable  /ˈprɑːfɪtəbəl/
**CEFR:** B2 | **Part of speech:** adj. | **Occurrences:** 9

**EN:** yielding profit or financial gain; making more money than is spent  
**CN:** 盈利的，有利可图的

**Original examples:**
- [58:48] You've told investors that you plan to be **profitable** starting in 2028.  
  你告诉投资者，你计划从2028年开始盈利。
- [59:16] Profitability is this kind of weird thing in this field.  
  在这个领域，盈利能力是一件有点奇怪的事情。
- [59:21] I don't think in this field profitability is actually a measure of spending down versus investing in the business.  
  我认为在这个领域，盈利能力实际上不是衡量削减支出与投资业务的标准。
- [01:00:36] Basically you're **profitable** and you make $50 billion of profit.  
  基本上你是盈利的，你赚了500亿美元的利润。
- [01:00:49] Then you have more than 50% of your data center for research and you're not **profitable**.  
  那么你有超过50%的数据中心用于研究，你就不盈利。
- [01:01:01] If you get more demand than you thought, then research gets squeezed, but you're kind of able to support more inference and you're more **profitable**.  
  如果你获得的需求超过预期，那么研究就会被压缩，但你能够支持更多的推理，你会更盈利。
- [01:01:50] We could be **profitable** in 2026 if the revenue grows fast enough.  
  如果收入增长足够快，我们可能在2026年就盈利。
- [01:02:04] What I'm trying to get at is that you have a model in your head of a business that invests, invests, invests, gets scale and then becomes **profitable**.  
  我想说的是，你脑海中有一个商业模式，即不断投资、投资、投资，获得规模，然后盈利。
- [01:02:44] If every year we predict exactly what the demand is going to be, we'll be **profitable** every year.  
  如果我们每年都准确预测需求，我们每年都会盈利。

**Extra example:**
- Most startups take several years to become **profitable**.  
  大多数初创公司需要几年时间才能盈利。

### diminishing returns  /dih-MIN-ish-ing ri-TURNS/
**CEFR:** C1 | **Part of speech:** n. phrase | **Occurrences:** 5

**EN:** a situation in which the additional benefit or output gained from an input decreases as you continue to invest more  
**CN:** 收益递减，边际效益递减

**Original examples:**
- [01:04:05] Then you get **diminishing returns**.  
  然后你会遇到收益递减的情况。
- [01:04:34] Okay, you don't invest in research because it has **diminishing returns**, but you invest in the other things you mentioned.  
  好吧，你不投资研究是因为它有收益递减，但你投资你提到的其他事情。
- [01:04:37] I think profit at a sort of macro level— Again, I'm talking about **diminishing returns**, but after you're spending $50 billion a year.  
  我认为从宏观层面来看利润——我又在讨论收益递减，但这是在你每年花费500亿美元之后。
- [01:04:46] This is a point I'm sure you would make, but **diminishing returns** on a genius could be quite high.  
  这是一个我相信你会提出的观点，但天才的收益递减可能相当高。
- [01:51:56] There's a question of getting **diminishing returns** on their value in the world.  
  存在一个问题，即它们在世界上的价值会出现收益递减。

**Extra example:**
- After hiring the fifth engineer, the team experienced **diminishing returns** on productivity.  
  在雇用第五名工程师后，团队在生产力上经历了收益递减。

### equilibrium  /ee-kwuh-LIB-ree-um/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 6

**EN:** a state of balance between opposing forces or factors, especially in economics or systems  
**CN:** 平衡，均衡（尤指经济或系统中的）

**Original examples:**
- [01:05:02] But let's just derive the **equilibrium** of the industry.  
  但让我们来推导一下这个行业的均衡状态。
- [01:05:27] So there's going to be an **equilibrium** where every company spends less than 100% on training and certainly less than 100% on inference.  
  所以会有一个均衡点，每家公司在训练上的花费都少于100%，在推理上的花费当然也少于100%。
- [01:05:38] So there's some **equilibrium**.  
  所以存在某种均衡状态。
- [01:05:55] I think we're gonna be in a position where that **equilibrium** of how much you spend on training is less than the gross margins that you're able to get on compute.  
  我认为我们将处于这样一个位置：训练支出的均衡点将低于你在算力上能够获得的毛利率。
- [01:09:00] The Cournot **equilibrium**, I think, is what the small number of firm **equilibrium** is.  
  古诺均衡，我认为，就是少数企业的均衡状态。
- [01:10:31] The **equilibrium** I'm talking about is an **equilibrium** where we have the 'country of geniuses in a data center', but that model training scale-up has equilibrated more.  
  我所说的均衡是这样一种均衡：我们拥有'数据中心里的天才国度'，但模型训练的扩展已经更加趋于平衡。

**Extra example:**
- The market will eventually reach an **equilibrium** where supply equals demand.  
  市场最终会达到供需相等的均衡状态。

### stylized  /STY-lyzd/
**CEFR:** C1 | **Part of speech:** adj. | **Occurrences:** 3

**EN:** simplified or presented in a non-realistic way to illustrate a principle; not reflecting actual specifics  
**CN:** 风格化的，简化的（用于说明原理而非反映实际细节）

**Original examples:**
- [01:05:02] I don't want to give information about Anthropic. That's why I'm giving these **stylized** numbers.  
  我不想透露关于Anthropic的信息。这就是为什么我给出这些简化的数字。
- [01:05:38] Let's just say as a **stylized** fact, it's 50%.  
  让我们就说作为一个简化的事实，是50%。
- [01:10:02] Again, I'm using **stylized** numbers here, but that would be 75% gross margins and this 25% tax.  
  再说一次，我这里使用的是简化的数字，但那将是75%的毛利率和25%的税。

**Extra example:**
- The economist presented a **stylized** model to explain inflation dynamics without getting into specifics.  
  经济学家提出了一个简化模型来解释通货膨胀动态，而不涉及具体细节。

### inference  /IN-fer-ens/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 3

**EN:** in AI/ML context, the process of using a trained model to make predictions or generate outputs  
**CN:** 推理（在AI/机器学习语境中，指使用训练好的模型进行预测或生成输出的过程）

**Original examples:**
- [01:05:27] So there's going to be an equilibrium where every company spends less than 100% on training and certainly less than 100% on **inference**.  
  所以会有一个均衡点，每家公司在训练上的花费都少于100%，在推理上的花费当然也少于100%。
- [01:08:38] They have some marginal cost to serve. The gross profit margins on that marginal cost are very high because **inference** is efficient.  
  他们有一些边际服务成本。这种边际成本的毛利率非常高，因为推理是高效的。
- [01:10:02] Then this year it produced $4 billion of revenue and cost $1 billion to **inference** from.  
  然后今年它产生了40亿美元的收入，推理成本为10亿美元。

**Extra example:**
- Running **inference** on large language models requires significant computational resources.  
  在大型语言模型上运行推理需要大量的计算资源。

### gross margin  /grohs MAR-jin/
**CEFR:** C1 | **Part of speech:** n. phrase | **Occurrences:** 4

**EN:** the difference between revenue and cost of goods sold, expressed as a percentage of revenue  
**CN:** 毛利率，毛利润

**Original examples:**
- [01:05:55] I think we're gonna be in a position where that equilibrium of how much you spend on training is less than the **gross margins** that you're able to get on compute.  
  我认为我们将处于这样一个位置：训练支出的均衡点将低于你在算力上能够获得的毛利率。
- [01:08:38] The **gross profit margins** on that marginal cost are very high because inference is efficient.  
  这种边际成本的毛利率非常高，因为推理是高效的。
- [01:09:33] Again, the **gross margins** right now are very positive.  
  再说一次，目前的毛利率非常可观。
- [01:10:02] Again, I'm using stylized numbers here, but that would be 75% **gross margins** and this 25% tax.  
  再说一次，我这里使用的是简化的数字，但那将是75%的毛利率和25%的税。

**Extra example:**
- Software companies typically have higher **gross margins** than hardware manufacturers.  
  软件公司通常比硬件制造商有更高的毛利率。

### marginal cost  /MAR-jin-ul kawst/
**CEFR:** C1 | **Part of speech:** n. phrase | **Occurrences:** 2

**EN:** the cost of producing one additional unit of output  
**CN:** 边际成本

**Original examples:**
- [01:08:33] Each can invest some fraction in R&D. They have some **marginal cost** to serve.  
  每家都可以在研发上投入一定比例。它们有一定的边际服务成本。
- [01:08:38] The gross profit margins on that **marginal cost** are very high because inference is efficient.  
  这种边际成本的毛利率非常高，因为推理是高效的。

**Extra example:**
- For digital products, the **marginal cost** of serving an additional user is close to zero.  
  对于数字产品，服务额外用户的边际成本接近于零。

### differentiated  /dif-er-EN-shee-ay-ted/
**CEFR:** B2 | **Part of speech:** adj. | **Occurrences:** 3

**EN:** made distinct or different from others, especially in a way that creates competitive advantage  
**CN:** 差异化的，有区别的

**Original examples:**
- [01:08:47] There's some competition, but the models are also **differentiated**.  
  存在一些竞争，但模型也是有差异化的。
- [01:14:51] Models are more **differentiated** than cloud.  
  模型比云服务更加差异化。
- [01:15:05] I think these things are actually quite different from each other, and so I would expect more **differentiation** than you see in cloud.  
  我认为这些东西实际上彼此之间相当不同，所以我预期会有比云服务更多的差异化。

**Extra example:**
- The company's **differentiated** product features helped it stand out in a crowded market.  
  该公司差异化的产品特性帮助它在拥挤的市场中脱颖而出。

### Cournot equilibrium  /koor-NOH ee-kwuh-LIB-ree-um/
**CEFR:** C2 | **Part of speech:** n. phrase | **Occurrences:** 1

**EN:** an economic model describing a market with a small number of firms that compete on quantity, resulting in an equilibrium between monopoly and perfect competition  
**CN:** 古诺均衡（少数企业在产量上竞争达成的市场均衡状态）

**Original examples:**
- [01:09:00] The **Cournot equilibrium**, I think, is what the small number of firm equilibrium is.  
  古诺均衡，我认为，就是少数企业的均衡状态。

**Extra example:**
- In a **Cournot equilibrium**, each firm chooses its output level assuming competitors' outputs are fixed.  
  在古诺均衡中，每家企业在假设竞争对手产量固定的情况下选择自己的产出水平。

### equilibrate  /ee-KWIL-uh-brayt/
**CEFR:** C1 | **Part of speech:** v. | **Occurrences:** 3

**EN:** to reach or bring into a state of balance or equilibrium  
**CN:** 达到平衡，使平衡

**Original examples:**
- [01:09:05] The point is it doesn't **equilibrate** to perfect competition with zero margins.  
  重点是它不会平衡到零利润的完全竞争状态。
- [01:09:15] If there's three firms in the economy and all are kind of independently behaving rationally, it doesn't **equilibrate** to zero.  
  如果经济中有三家企业，并且都相对独立地理性行事，它不会平衡到零。
- [01:10:31] The equilibrium I'm talking about is an equilibrium where we have the 'country of geniuses in a data center', but that model training scale-up has **equilibrated** more.  
  我所说的均衡是这样一种均衡：我们拥有'数据中心里的天才国度'，但模型训练的扩展已经更加趋于平衡。

**Extra example:**
- The system will **equilibrate** over time as supply adjusts to meet demand.  
  随着供应调整以满足需求，系统将随时间达到平衡。

### scale-up  /SKAYL-up/
**CEFR:** B2 | **Part of speech:** n./v. | **Occurrences:** 3

**EN:** the process of increasing the size, capacity, or scope of operations, especially rapidly  
**CN:** 扩大规模，升级

**Original examples:**
- [01:09:38] One is that we're still in the exponential **scale-up** phase of compute.  
  一个是我们仍处于算力的指数级扩张阶段。
- [01:10:23] But at the same time, we're spending $10 billion to train the next model because there's an exponential **scale-up**.  
  但与此同时，我们花费100亿美元来训练下一个模型，因为存在指数级扩张。
- [01:10:31] The equilibrium I'm talking about is an equilibrium where we have the 'country of geniuses in a data center', but that model training **scale-up** has equilibrated more.  
  我所说的均衡是这样一种均衡：我们拥有'数据中心里的天才国度'，但模型训练的扩展已经更加趋于平衡。

**Extra example:**
- The startup successfully managed to **scale up** operations from 10 to 1000 customers in six months.  
  这家初创公司成功地在六个月内将运营规模从10个客户扩大到1000个客户。

### production function  /pruh-DUK-shun FUNGK-shun/
**CEFR:** C1 | **Part of speech:** n. phrase | **Occurrences:** 1

**EN:** in economics, the relationship between inputs used and outputs produced in a process  
**CN:** 生产函数（经济学中投入与产出之间的关系）

**Original examples:**
- [01:11:05] But of course, a big part of the **production function** of being a frontier lab is training the next model, right?  
  但当然，作为前沿实验室的生产函数的一个重要部分是训练下一个模型，对吧？

**Extra example:**
- The company's **production function** relies heavily on skilled labor and advanced technology.  
  该公司的生产函数严重依赖于熟练劳动力和先进技术。

### fixed lump of labor fallacy  /fikst lump uv LAY-ber FAL-uh-see/
**CEFR:** C2 | **Part of speech:** n. phrase | **Occurrences:** 1

**EN:** the mistaken belief that there is a fixed amount of work or economic activity in an economy  
**CN:** 劳动总量恒定谬误（认为经济中工作或经济活动总量固定的错误观念）

**Original examples:**
- [01:11:37] A **fixed lump of labor fallacy**… The economy is going to grow, right?  
  劳动总量恒定谬误……经济会增长，对吧？

**Extra example:**
- The **fixed lump of labor fallacy** leads people to wrongly believe that automation will inevitably cause permanent unemployment.  
  劳动总量恒定谬误导致人们错误地认为自动化将不可避免地造成永久性失业。

### steady state  /STED-ee stayt/
**CEFR:** C1 | **Part of speech:** n. phrase | **Occurrences:** 1

**EN:** a condition where key variables remain constant over time, with no net change  
**CN:** 稳态，稳定状态

**Original examples:**
- [01:12:39] So this model requires there never to be a **steady state**.  
  所以这个模型要求永远不会有稳定状态。

**Extra example:**
- The population reached a **steady state** where births and deaths balanced out.  
  人口达到了稳态，出生和死亡相互平衡。

### monopoly  /muh-NAH-puh-lee/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 3

**EN:** exclusive control of a market by a single company or entity  
**CN:** 垄断，专营

**Original examples:**
- [01:13:03] So no, I don't think this field's going to be a **monopoly**.  
  所以不，我不认为这个领域会成为垄断。
- [01:13:12] All my lawyers never want me to say the word '**monopoly**'.  
  我所有的律师都不希望我说'垄断'这个词。
- [01:13:15] But I don't think this field's going to be a **monopoly**.  
  但我不认为这个领域会成为垄断。

**Extra example:**
- The government broke up the company's **monopoly** to encourage competition.  
  政府打破了该公司的垄断以鼓励竞争。

### network effect  /NET-wurk ih-FEKT/
**CEFR:** C1 | **Part of speech:** n. phrase | **Occurrences:** 1

**EN:** a phenomenon where a product or service gains value as more people use it  
**CN:** 网络效应（产品或服务随着使用者增多而增值的现象）

**Original examples:**
- [01:13:21] Ordinarily, the way you get monopolies like Facebook or Meta—I always call them Facebook—is these kinds of **network effects**.  
  通常，你获得像Facebook或Meta这样的垄断——我总是叫它们Facebook——就是通过这些网络效应。

**Extra example:**
- Social media platforms benefit from strong **network effects** because users want to be where their friends are.  
  社交媒体平台受益于强大的网络效应，因为用户希望在他们的朋友所在的地方。

### barrier to entry  /BAIR-ee-er too EN-tree/
**CEFR:** C1 | **Part of speech:** n. phrase | **Occurrences:** 1

**EN:** obstacles that make it difficult for new competitors to enter a market  
**CN:** 进入壁垒，准入门槛

**Original examples:**
- [01:13:37] The way you get industries in which there are a small number of players is very high costs of entry.  
  你得到少数玩家的行业的方式是非常高的进入成本。

**Extra example:**
- High capital requirements create a significant **barrier to entry** in the semiconductor industry.  
  高资本要求在半导体行业创造了显著的进入壁垒。

### disrupt  /dis-RUPT/
**CEFR:** B2 | **Part of speech:** v. | **Occurrences:** 1

**EN:** to fundamentally change or revolutionize an industry or market, typically through innovation  
**CN:** 颠覆，扰乱（通常通过创新从根本上改变行业或市场）

**Original examples:**
- [01:14:11] So if you go to someone and you're like, 'I want to **disrupt** this industry, here's $100 billion.'  
  所以如果你去找某人说，'我想要颠覆这个行业，这是1000亿美元。'

**Extra example:**
- The startup aims to **disrupt** the traditional banking sector with its innovative mobile-first approach.  
  这家初创公司旨在通过其创新的移动优先方法颠覆传统银行业。

### astronomical  /as-truh-NAH-mih-kul/
**CEFR:** B2 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** extremely large or great, especially referring to amounts or numbers  
**CN:** 天文数字的，极其巨大的

**Original examples:**
- [01:14:29] So we have equilibria like this all the time in the economy where we have a few players. Profits are not **astronomical**. Margins are not **astronomical**, but they're not zero.  
  所以我们在经济中一直有这样的均衡，我们有几个玩家。利润不是天文数字。利润率不是天文数字，但也不是零。

**Extra example:**
- The company was valued at an **astronomical** $50 billion despite having no profits.  
  尽管没有利润，该公司的估值达到了天文数字般的500亿美元。

### dependent  /dɪˈpɛndənt/
**CEFR:** B2 | **Part of speech:** adj. | **Occurrences:** 2

**EN:** relying on or determined by something else  
**CN:** 依赖的，取决于的

**Original examples:**
- [01:18:19] I don't think it's **dependent** on learning like a human.  
  我认为这不依赖于像人类那样学习。
- [01:18:34] So it will happen... it's not necessarily **dependent** on human-like learning.  
  所以它会发生......不一定依赖于类人的学习。

**Extra example:**
- The project's success is **dependent** on securing adequate funding.  
  项目的成功取决于获得充足的资金。

### generalize  /ˈdʒɛnrəlaɪz/
**CEFR:** B2 | **Part of speech:** v. | **Occurrences:** 4

**EN:** to apply learning or conclusions from specific cases to broader contexts  
**CN:** 泛化，推广

**Original examples:**
- [01:18:21] Again, we could have trained the model on many different video games, which are like robotic controls, or many different simulated robotics environments, or just train them to control computer screens, and they learn to **generalize**.  
  我们可以在许多不同的视频游戏上训练模型，这些游戏类似于机器人控制，或者在许多不同的模拟机器人环境中训练，或者只是训练它们控制计算机屏幕，然后它们学会泛化。
- [01:18:50] That could also happen because we trained the model on a bunch of environments and then **generalized**, or it could happen because the model learns that in the context length.  
  这也可能发生，因为我们在一堆环境上训练了模型，然后它泛化了，或者可能因为模型在上下文长度内学会了这一点。
- [01:20:28] I think we may just get there by pre-training **generalization** and RL **generalization**.  
  我认为我们可能只需通过预训练泛化和强化学习泛化就能达到目标。
- [02:06:58] It doesn't really understand the rules, and it's hard to **generalize** from them.  
  它并不真正理解规则，而且很难从中**泛化**。

**Extra example:**
- Students often struggle to **generalize** concepts learned in the classroom to real-world problems.  
  学生们常常难以将课堂上学到的概念泛化到现实世界的问题中。

### continual  /kənˈtɪnjuəl/
**CEFR:** B2 | **Part of speech:** adj. | **Occurrences:** 4

**EN:** happening repeatedly over a period of time; ongoing  
**CN:** 持续的，不断的

**Original examples:**
- [01:18:41] If the model's like, 'Oh, I pick up a robot, I don't know how to use it, I learn,' that could happen because we discovered **continual** learning.  
  如果模型像是'哦，我拿起一个机器人，我不知道如何使用它，我学习'，这可能会发生，因为我们发现了持续学习。
- [01:19:52] It sounds like you are going to solve **continual** learning one way or another within a matter of years.  
  听起来你将在几年内以某种方式解决持续学习问题。
- [01:20:28] Well, to be clear, I think **continual** learning, as I've said before, might not be a barrier at all.  
  嗯，明确地说，我认为持续学习，正如我之前所说，可能根本不是障碍。
- [01:21:23] Some of them are real. The need for data is real, maybe **continual** learning is a real thing.  
  其中一些是真实的。对数据的需求是真实的，也许持续学习是真实存在的。

**Extra example:**
- The company's **continual** improvement efforts have led to significant gains in efficiency.  
  公司持续改进的努力已带来效率的显著提升。

### revolutionize  /ˌrɛvəˈluʃənaɪz/
**CEFR:** C1 | **Part of speech:** v. | **Occurrences:** 2

**EN:** to completely change something in a dramatic and fundamental way  
**CN:** 彻底改革，使发生革命性变化

**Original examples:**
- [01:19:10] But I do think when for whatever reason the models have those skills, then robotics will be **revolutionized**—both the design of robots, because the models will be much better than humans at that, and also the ability to control robots.  
  但我确实认为，当模型无论出于何种原因拥有这些技能时，机器人技术将被彻底改革——包括机器人的设计，因为模型在这方面会比人类好得多，以及控制机器人的能力。
- [01:19:40] So will robotics be **revolutionized**?  
  那么机器人技术会被彻底改革吗？

**Extra example:**
- The internet has **revolutionized** the way we communicate and access information.  
  互联网已经彻底改变了我们沟通和获取信息的方式。

### skepticism  /ˈskɛptɪsɪzəm/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 2

**EN:** doubt or disbelief about something; a questioning attitude  
**CN:** 怀疑，怀疑态度

**Original examples:**
- [01:19:52] There's a general **skepticism** about extremely fast progress.  
  对极快进步存在普遍的怀疑。
- [01:32:06] I think in 'The Adolescence of Technology', I was **skeptical** of the balance of power.  
  我认为在《技术的青春期》中，我对权力平衡持怀疑态度。

**Extra example:**
- There's widespread **skepticism** about whether the company can meet its ambitious growth targets.  
  对于该公司能否实现其雄心勃勃的增长目标，存在广泛的怀疑。

### barrier  /ˈbæriər/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** an obstacle that prevents progress or achievement  
**CN:** 障碍，壁垒

**Original examples:**
- [01:20:28] Well, to be clear, I think continual learning, as I've said before, might not be a **barrier** at all.  
  嗯，明确地说，我认为持续学习，正如我之前所说，可能根本不是障碍。

**Extra example:**
- Language differences can be a significant **barrier** to effective international collaboration.  
  语言差异可能是有效国际合作的重大障碍。

### dissolve  /dɪˈzɑlv/
**CEFR:** B2 | **Part of speech:** v. | **Occurrences:** 2

**EN:** to cause to disappear or fade away; to break down  
**CN:** 消解，消失

**Original examples:**
- [01:20:40] In fact, I would point to the history In ML, of people coming up with things that are barriers that end up kind of **dissolving** within the big blob of compute.  
  事实上，我会指出机器学习的历史，人们提出的一些被认为是障碍的东西最终都在计算的大团中消解了。
- [01:21:23] So I think there's actually a stronger history of some of these things seeming like a big deal and then kind of **dissolving**.  
  所以我认为实际上有更强的历史表明，这些东西中的一些看似很重要，然后就消解了。

**Extra example:**
- Many of the initial concerns about cloud security have **dissolved** as best practices have matured.  
  随着最佳实践的成熟，许多关于云安全的初步担忧已经消解了。

### syntactically  /sɪnˈtæktɪkli/
**CEFR:** C2 | **Part of speech:** adv. | **Occurrences:** 1

**EN:** in a manner related to the grammatical arrangement of words and phrases  
**CN:** 在语法上，从句法角度

**Original examples:**
- [01:20:51] People talked about, 'How do your models keep track of nouns and verbs?' 'They can understand **syntactically**, but they can't understand semantically? It's only statistical correlations.'  
  人们谈论'你们的模型如何跟踪名词和动词？''它们可以在语法上理解，但不能在语义上理解？这只是统计相关性。'

**Extra example:**
- The sentence is **syntactically** correct, but it doesn't make semantic sense in this context.  
  这个句子在语法上是正确的，但在这种语境下没有语义意义。

### semantically  /sɪˈmæntɪkli/
**CEFR:** C2 | **Part of speech:** adv. | **Occurrences:** 1

**EN:** in a manner related to meaning in language or logic  
**CN:** 在语义上，从意义角度

**Original examples:**
- [01:20:51] People talked about, 'How do your models keep track of nouns and verbs?' 'They can understand syntactically, but they can't understand **semantically**? It's only statistical correlations.'  
  人们谈论'你们的模型如何跟踪名词和动词？''它们可以在语法上理解，但不能在语义上理解？这只是统计相关性。'

**Extra example:**
- The two phrases are **semantically** equivalent even though they use different words.  
  这两个短语在语义上是等价的，尽管它们使用了不同的词汇。

### correlation  /ˌkɔrəˈleɪʃən/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a mutual relationship or connection between two or more things  
**CN:** 相关性，关联

**Original examples:**
- [01:20:51] People talked about, 'How do your models keep track of nouns and verbs?' 'They can understand syntactically, but they can't understand semantically? It's only statistical **correlations**.'  
  人们谈论'你们的模型如何跟踪名词和动词？''它们可以在语法上理解，但不能在语义上理解？这只是统计相关性。'

**Extra example:**
- Researchers found a strong **correlation** between exercise frequency and improved mental health outcomes.  
  研究人员发现锻炼频率与改善心理健康结果之间存在很强的相关性。

### traverse  /trəˈvɜrs/
**CEFR:** C1 | **Part of speech:** v. | **Occurrences:** 1

**EN:** to travel or move across, over, or through something  
**CN:** 穿越，横跨

**Original examples:**
- [01:22:31] It's a long spectrum there, but we're **traversing** the spectrum very quickly.  
  这是一个很长的范围，但我们正在非常快速地穿越这个范围。

**Extra example:**
- The team must **traverse** several technical challenges before the product can launch.  
  在产品可以发布之前，团队必须克服几个技术挑战。

### insight  /ˈɪnsaɪt/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 2

**EN:** a deep understanding of a situation or problem; a revelation  
**CN:** 洞察力，见解

**Original examples:**
- [01:23:04] These days that probably has more to do with seeing a bunch of stuff within Anthropic and having to make a bunch of decisions than I have any great research **insight** that others don't.  
  如今这可能更多地与在Anthropic内看到很多东西并必须做出很多决定有关，而不是我有其他人没有的任何重大研究见解。
- [01:23:13] It's actually pretty hard for me to have concrete research **insight**, much harder than it would have been 10 years ago or even two or three years ago.  
  对我来说，要有具体的研究见解实际上相当困难，比10年前甚至两三年前要困难得多。

**Extra example:**
- The consultant's **insight** into market trends helped the company avoid a costly strategic mistake.  
  顾问对市场趋势的洞察帮助公司避免了代价高昂的战略错误。

### drop-in  /ˈdrɑp ɪn/
**CEFR:** C1 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** able to replace something directly without modification or adaptation  
**CN:** 可直接替换的，即插即用的

**Original examples:**
- [01:23:27] As we go towards a world of a full **drop-in** remote worker replacement, does an API pricing model still make the most sense?  
  当我们走向一个完全可直接替换的远程工作者的世界时，API定价模式仍然最有意义吗？

**Extra example:**
- The new component is designed as a **drop-in** replacement for the legacy system module.  
  新组件被设计为旧系统模块的即插即用替代品。

### durable  /ˈdʊrəbəl/
**CEFR:** B2 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** able to last or continue for a long time; resilient  
**CN:** 持久的，耐用的

**Original examples:**
- [01:23:45] I actually do think that the API model is more **durable** than many people think.  
  我确实认为API模式比许多人认为的更持久。

**Extra example:**
- Building a **durable** competitive advantage requires continuous innovation and adaptation.  
  建立持久的竞争优势需要持续的创新和适应。

### exponentially  /ˌɛkspəˈnɛnʃəli/
**CEFR:** C1 | **Part of speech:** adv. | **Occurrences:** 1

**EN:** at a rate that increases rapidly in proportion to the growing total  
**CN:** 呈指数地，急剧地

**Original examples:**
- [01:23:59] One way I think about it is if the technology is advancing quickly, if it's advancing **exponentially**, what that means is there's always a surface area of new use cases that have been developed in the last three months.  
  我思考它的一种方式是，如果技术进步很快，如果它呈指数级进步，那意味着总是有在过去三个月开发的新用例的表面积。

**Extra example:**
- Computing power has grown **exponentially** over the past five decades, following Moore's Law.  
  在过去五十年中，计算能力呈指数级增长，遵循摩尔定律。

### surface area  /ˈsɜrfɪs ˈɛriə/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** the extent or scope of opportunities or possibilities (metaphorical usage)  
**CN:** 表面积，（比喻）范围，机会面

**Original examples:**
- [01:23:59] One way I think about it is if the technology is advancing quickly, if it's advancing exponentially, what that means is there's always a **surface area** of new use cases that have been developed in the last three months.  
  我思考它的一种方式是，如果技术进步很快，如果它呈指数级进步，那意味着总是有在过去三个月开发的新用例的表面积。

**Extra example:**
- Expanding into new markets increases the company's **surface area** for potential customer acquisition.  
  拓展新市场增加了公司潜在客户获取的机会面。

### irrelevant  /ɪˈrɛləvənt/
**CEFR:** B2 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** not connected with or relevant to something; not important  
**CN:** 不相关的，无关紧要的

**Original examples:**
- [01:24:20] Any kind of product surface you put in place is always at risk of sort of becoming **irrelevant**.  
  你设置的任何产品界面总是有变得无关紧要的风险。

**Extra example:**
- With the rise of streaming services, physical media storage has become largely **irrelevant**.  
  随着流媒体服务的兴起，物理媒体存储已基本变得无关紧要。

### limitation  /ˌlɪmɪˈteɪʃən/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a restriction or constraint on capability or effectiveness  
**CN:** 限制，局限

**Original examples:**
- [01:24:32] The chatbot is already running into **limitations** where making it smarter doesn't really help the average consumer that much.  
  聊天机器人已经遇到了限制，让它更智能并不能真正帮助普通消费者太多。

**Extra example:**
- Understanding the **limitations** of current battery technology is crucial for electric vehicle development.  
  了解当前电池技术的局限对于电动汽车的发展至关重要。

### societal  /səˈsaɪ.ə.t̬əl/
**CEFR:** C1 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** relating to society or the way society is organized  
**CN:** 社会的，社会性的

**Original examples:**
- [01:35:58] We may have to talk to AIs about building **societal** structures in such a way that these defenses are possible.  
  我们可能需要与人工智能讨论如何构建社会结构，以使这些防御措施成为可能。

**Extra example:**
- The pandemic exposed deep **societal** inequalities in healthcare access.  
  疫情暴露了医疗保健获取方面的深层社会不平等。

### anticipate  /ænˈtɪs.ə.peɪt/
**CEFR:** B2 | **Part of speech:** v. | **Occurrences:** 1

**EN:** to expect something to happen and prepare for it  
**CN:** 预料，预期

**Original examples:**
- [01:35:58] It's so far ahead in technological ability that may happen over a short period of time, that it's hard for us to **anticipate** it in advance.  
  这在技术能力上如此超前，可能在短时间内发生，以至于我们很难提前预料到它。

**Extra example:**
- Successful startups **anticipate** market trends before their competitors.  
  成功的初创公司会在竞争对手之前预见市场趋势。

### legislature  /ˈledʒ.ɪs.leɪ.tʃɚ/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** the group of people in a country or state who have the power to make and change laws  
**CN:** 立法机构，立法机关

**Original examples:**
- [01:36:21] On December 26, the Tennessee **legislature** introduced a bill which said, 'It would be an offense for a person to knowingly train artificial intelligence to provide emotional support.'  
  12月26日，田纳西州立法机构提出了一项法案，称"故意训练人工智能提供情感支持将构成违法行为。"

**Extra example:**
- The state **legislature** voted to increase funding for public education.  
  州立法机构投票决定增加公共教育经费。

### offense  /əˈfens/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** an illegal act; a crime  
**CN:** 违法行为，犯罪

**Original examples:**
- [01:36:21] It would be an **offense** for a person to knowingly train artificial intelligence to provide emotional support.  
  故意训练人工智能提供情感支持将构成违法行为。

**Extra example:**
- Hacking into someone's computer without permission is a criminal **offense**.  
  未经许可入侵他人计算机是刑事犯罪。

### patchwork  /ˈpætʃ.wɝːk/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 2

**EN:** something made up of different parts that do not fit together well  
**CN:** 拼凑物，杂乱无章的混合体

**Original examples:**
- [01:36:48] In general, it seems like we're going to have this **patchwork** of state laws.  
  总的来说，我们似乎将会有这种零散拼凑的州法律体系。
- [01:40:42] I really do think this **patchwork** of state laws, on the current trajectory, would prohibit.  
  我真的认为这种零散拼凑的州法律，按照目前的轨迹，会起到禁止作用。

**Extra example:**
- The healthcare system is a **patchwork** of public and private providers.  
  医疗保健系统是由公共和私人提供者拼凑而成的混合体。

### curtail  /kɝːˈteɪl/
**CEFR:** C1 | **Part of speech:** v. | **Occurrences:** 1

**EN:** to reduce or limit something  
**CN:** 削减，限制

**Original examples:**
- [01:36:48] A lot of the benefits that normal people could experience as a result of AI are going to be **curtailed**.  
  普通人因人工智能而能够体验到的许多好处将会被削减。

**Extra example:**
- The company had to **curtail** its expansion plans due to economic uncertainty.  
  由于经济不确定性，公司不得不削减其扩张计划。

### whac-a-mole  /ˌwæk.əˈmoʊl/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a situation in which solving one problem causes another problem to appear, requiring constant effort  
**CN:** 打地鼠式（解决一个问题又出现另一个问题的反复循环）

**Original examples:**
- [01:37:02] It seems easy to imagine worlds in which these get **whac-a-moled** away by different laws.  
  很容易想象这些好处会被不同的法律像打地鼠一样一个个消灭掉。

**Extra example:**
- Cybersecurity teams face a **whac-a-mole** situation with new threats constantly emerging.  
  网络安全团队面临打地鼠式的局面，新威胁不断出现。

### existential  /ˌeɡ.zɪˈsten.ʃəl/
**CEFR:** C1 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** relating to existence; threatening the very existence or survival of something  
**CN:** 存在的；事关生死存亡的

**Original examples:**
- [01:37:02] Whereas bills like this don't seem to address the actual **existential** threats that you're concerned about.  
  然而这样的法案似乎并没有解决你所担心的真正的存在性威胁。

**Extra example:**
- Climate change poses an **existential** threat to many island nations.  
  气候变化对许多岛国构成生死存亡的威胁。

### moratorium  /ˌmɔːr.əˈtɔːr.i.əm/
**CEFR:** C2 | **Part of speech:** n. | **Occurrences:** 2

**EN:** a temporary stopping of an activity, especially by official agreement  
**CN:** 暂停，中止（尤指官方同意的）

**Original examples:**
- [01:37:15] I'm curious to understand, in the context of things like this, Anthropic's position against the federal **moratorium** on state AI laws.  
  我很想了解，在这样的背景下，Anthropic对联邦暂停州人工智能法律的立场。
- [01:38:36] So if that's the choice, if that's what you force us to choose, then we're going to choose not to have that **moratorium**.  
  所以如果这是选择，如果这是你迫使我们做出的选择，那么我们将选择不要那个暂停令。

**Extra example:**
- The city declared a **moratorium** on new construction permits until the infrastructure could be upgraded.  
  该市宣布暂停发放新建筑许可证，直到基础设施得到升级。

### autonomy  /ɔːˈtɑː.nə.mi/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 2

**EN:** the ability to make your own decisions without being controlled by anyone else; independence  
**CN:** 自主性，自治

**Original examples:**
- [01:38:11] Given the serious dangers that I lay out in 'Adolescence of Technology' around things like biological weapons and bioterrorism **autonomy** risk.  
  鉴于我在《技术的青春期》中阐述的关于生物武器和生物恐怖主义自主性风险等方面的严重危险。
- [01:39:29] In terms of what we would want, the things we've talked about are starting with transparency standards in order to monitor some of these **autonomy** risks and bioterrorism risks.  
  就我们想要的而言，我们讨论的事情是从透明度标准开始，以监测这些自主性风险和生物恐怖主义风险。

**Extra example:**
- Many regions are demanding greater political **autonomy** from the central government.  
  许多地区要求从中央政府获得更大的政治自治权。

### bioterrorism  /ˌbaɪ.oʊˈter.ə.rɪ.zəm/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 4

**EN:** the use of harmful bacteria or viruses as a weapon to cause illness or death  
**CN:** 生物恐怖主义

**Original examples:**
- [01:38:11] Given the serious dangers that I lay out in 'Adolescence of Technology' around things like biological weapons and **bioterrorism** autonomy risk.  
  鉴于我在《技术的青春期》中阐述的关于生物武器和生物恐怖主义自主性风险等方面的严重危险。
- [01:39:29] The things we've talked about are starting with transparency standards in order to monitor some of these autonomy risks and **bioterrorism** risks.  
  我们讨论的事情是从透明度标准开始，以监测这些自主性风险和生物恐怖主义风险。
- [01:39:46] Hey, AI **bioterrorism** is really a threat. Let's pass a law that forces people to have classifiers.  
  嘿，人工智能生物恐怖主义确实是一个威胁。让我们通过一项法律，强制人们使用分类器。
- [01:40:12] But I could certainly imagine, with the pace that things are going at, a world where later this year we say, 'Hey, this AI **bioterrorism** stuff is really serious.'  
  但我当然可以想象，以目前的发展速度，今年晚些时候我们会说，'嘿，这个人工智能生物恐怖主义的事情真的很严重。'

**Extra example:**
- Governments worldwide are developing protocols to respond to potential **bioterrorism** attacks.  
  世界各国政府正在制定应对潜在生物恐怖主义袭击的协议。

### eternity  /iˈtɝː.nə.t̬i/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** time that never ends or that seems to have no end; a very long time  
**CN:** 永恒；极长的时间

**Original examples:**
- [01:38:11] Given the serious dangers that I lay out in 'Adolescence of Technology' around things like biological weapons and bioterrorism autonomy risk, and the timelines we've been talking about—10 years is an **eternity**.  
  鉴于我在《技术的青春期》中阐述的关于生物武器和生物恐怖主义自主性风险等方面的严重危险，以及我们一直在讨论的时间表——10年就是永恒。

**Extra example:**
- In the fast-paced tech industry, waiting six months for a decision feels like an **eternity**.  
  在快节奏的科技行业，等待六个月做决定感觉像是永恒。

### preemption  /priˈemp.ʃən/
**CEFR:** C2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** the act of taking action to prevent something from happening, especially by acting first; in law, the invalidation of state law by federal law  
**CN:** 先发制人；（法律）联邦法优先权

**Original examples:**
- [01:39:02] I think **preemption** is fine in the sense of saying that the federal government says, 'Here is our standard. This applies to everyone. States can't do something different.'  
  我认为在联邦政府说'这是我们的标准，适用于所有人，各州不能做不同的事情'这个意义上，联邦优先权是可以的。

**Extra example:**
- Federal **preemption** prevents states from passing laws that conflict with national environmental standards.  
  联邦优先权阻止各州通过与国家环境标准相冲突的法律。

### backlash  /ˈbæk.læʃ/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a strong negative reaction by a large number of people, especially to a social or political development  
**CN:** 强烈反对，反弹

**Original examples:**
- [01:39:22] I think it will not age well, it is already starting to not age well with all the **backlash** that you've seen.  
  我认为它不会经得起时间考验，从你看到的所有反对声浪来看，它已经开始经不起考验了。

**Extra example:**
- The company's decision to raise prices triggered a **backlash** from customers on social media.  
  该公司提价的决定引发了社交媒体上客户的强烈反对。

### transparency  /trænsˈper.ən.si/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 3

**EN:** the quality of being done in an open way without secrets; openness  
**CN:** 透明度，公开性

**Original examples:**
- [01:39:29] In terms of what we would want, the things we've talked about are starting with **transparency** standards in order to monitor some of these autonomy risks and bioterrorism risks.  
  就我们想要的而言，我们讨论的事情是从透明度标准开始，以监测这些自主性风险和生物恐怖主义风险。
- [01:43:35] At the same time, I think we should be ramping up quite significantly the safety and security legislation. Like I've said, starting with **transparency** is my view.  
  同时，我认为我们应该大幅加强安全和保障立法。正如我所说，从透明度开始是我的观点。
- [01:43:50] Well, basically, I think the last six months and maybe the next few months are going to be about **transparency**.  
  基本上，我认为过去六个月和也许接下来的几个月将围绕透明度展开。

**Extra example:**
- The organization is committed to **transparency** in all its financial dealings.  
  该组织致力于在所有财务交易中保持透明。

### emerge  /iˈmɝːdʒ/
**CEFR:** B2 | **Part of speech:** v. | **Occurrences:** 3

**EN:** to appear or become known; to begin to exist  
**CN:** 出现，显现

**Original examples:**
- [01:39:46] As the risks become more serious, as we get more evidence for them, then I think we could be more aggressive in some targeted ways.  
  随着风险变得更加严重，随着我们获得更多证据，那么我认为我们可以在一些有针对性的方面变得更加积极。
- [01:40:07] We need to pursue this in an intellectually honest way where we say that ahead of time, the risk has not **emerged** yet.  
  我们需要以一种智识上诚实的方式来追求这一点，即提前说明风险尚未出现。
- [01:43:58] Then, if these risks **emerge** when we're more certain of them—which I think we might be as soon as later this year—then I think we need to act very fast.  
  然后，如果这些风险在我们更加确定的时候出现——我认为可能就在今年晚些时候——那么我认为我们需要非常快速地采取行动。

**Extra example:**
- New security vulnerabilities continue to **emerge** as software systems become more complex.  
  随着软件系统变得更加复杂，新的安全漏洞不断出现。

### classifier  /ˈklæs.ə.faɪ.ɚ/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a system or algorithm that categorizes data into predefined classes  
**CN:** 分类器（机器学习算法）

**Original examples:**
- [01:39:46] Hey, AI bioterrorism is really a threat. Let's pass a law that forces people to have **classifiers**.  
  嘿，人工智能生物恐怖主义确实是一个威胁。让我们通过一项法律，强制人们使用分类器。

**Extra example:**
- The spam filter uses a machine learning **classifier** to identify unwanted emails.  
  垃圾邮件过滤器使用机器学习分类器来识别不需要的电子邮件。

### intellectually  /ˌɪn.t̬əlˈek.tʃu.ə.li/
**CEFR:** C1 | **Part of speech:** adv. | **Occurrences:** 1

**EN:** in a way that relates to the ability to think and understand ideas and information; honestly and rationally  
**CN:** 智识上地；理智地

**Original examples:**
- [01:40:07] We need to pursue this in an **intellectually** honest way where we say that ahead of time, the risk has not emerged yet.  
  我们需要以一种智识上诚实的方式来追求这一点，即提前说明风险尚未出现。

**Extra example:**
- The debate should be conducted **intellectually** without resorting to emotional appeals.  
  辩论应该以理智的方式进行，而不是诉诸情感诉求。

### trajectory  /trəˈdʒek.tɚ.i/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 2

**EN:** the path followed by something moving through space or time; the expected development of something  
**CN:** 轨迹；发展轨迹

**Original examples:**
- [01:40:42] I really do think this patchwork of state laws, on the current **trajectory**, would prohibit.  
  我真的认为这种零散拼凑的州法律，按照目前的轨迹，会起到禁止作用。
- [01:51:13] But on the current **trajectory**, everybody will have more AI.  
  但按照目前的发展轨迹，每个人都会拥有更多的人工智能。

**Extra example:**
- The company needs to change its **trajectory** if it wants to remain competitive.  
  如果公司想保持竞争力，就需要改变其发展轨迹。

### lag  /læɡ/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a period of time between two events; a delay  
**CN:** 延迟，滞后

**Original examples:**
- [01:40:42] The benefits are, as you say because of diffusion **lag**, slow enough that I really do think this patchwork of state laws would prohibit.  
  正如你所说，由于扩散滞后，好处来得足够慢，我真的认为这种零散拼凑的州法律会起到禁止作用。

**Extra example:**
- There's always a **lag** between implementing a policy and seeing its effects.  
  在实施政策和看到其效果之间总是存在滞后。

### authoritarian  /ɔː-θɔːr-ɪ-ˈter-i-ən/
**CEFR:** C1 | **Part of speech:** adj./n. | **Occurrences:** 20

**EN:** favoring or enforcing strict obedience to authority at the expense of personal freedom; a person who supports such a system  
**CN:** 专制的；独裁主义者

**Original examples:**
- [01:51:18] Some of that AI will be used by **authoritarian** countries.  
  其中一些AI将被专制国家使用。
- [01:51:20] Some of that within the **authoritarian** countries will be used by private actors versus state actors.  
  在这些专制国家内部，一些AI将被私人行为者而非国家行为者使用。
- [01:51:26] It seems like the internet privileged **authoritarian** countries more than you would've expected.  
  互联网似乎比你预期的更有利于专制国家。
- [01:55:13] We have to worry a lot about authoritarians and we should try to check them and limit their power.  
  我们必须非常担心独裁者，并应该尝试制约和限制他们的权力。
- [01:55:43] If you were to make a commitment to overthrowing every **authoritarian** country, they would take a bunch of actions now that could lead to instability.  
  如果你承诺推翻每一个专制国家，他们现在就会采取一系列可能导致不稳定的行动。
- [01:56:16] But if a country's **authoritarian**, we don't react the way we'd react if they committed a genocide or something.  
  但如果一个国家是专制的，我们不会像他们犯下种族灭绝罪行那样做出反应。
- [01:56:27] I'm a little worried that in the age of AGI, authoritarianism will have a different meaning.  
  我有点担心在AGI时代，专制主义会有不同的含义。
- [01:57:26] But these problems with authoritarianism get deeper.  
  但专制主义的这些问题会变得更深。
- [01:57:38] Because authoritarianism becomes worse, people are more afraid of it.  
  因为专制主义变得更糟，人们更害怕它。
- [01:59:11] We decided that even though it's an **authoritarian** system, we will engage with it.  
  我们决定即使它是一个专制体系，我们也会与之接触。
- [01:59:15] I think in retrospect that was the right call, because it's a state **authoritarian** system but a billion-plus people are much wealthier and better off than they would've otherwise been.  
  我认为回顾起来那是正确的决定，因为它是一个国家专制体系，但十亿多人比原本更富裕、生活更好。
- [01:59:23] It's not clear that it would've stopped being an **authoritarian** country otherwise.  
  不清楚否则它会不会停止成为一个专制国家。
- [01:59:30] I don't know if it takes that much intelligence to remain an **authoritarian** country that continues to coalesce its own power.  
  我不知道保持一个继续巩固自己权力的专制国家是否需要那么多智慧。
- [01:59:54] Historically, we have decided it's good to spread the benefits of technology widely, even to people whose governments are **authoritarian**.  
  从历史上看，我们决定广泛传播技术的好处是好的，即使是对政府专制的人。
- [02:00:32] The cures are fine to sell to **authoritarian** countries, but the data centers just aren't.  
  治疗方法可以卖给专制国家，但数据中心不行。
- [02:00:49] Could there be developments we can make that create an equilibrium where it becomes infeasible for **authoritarian** countries to deny their people private use of the benefits of the technology?  
  我们能否做出一些发展，创造一种平衡，使专制国家无法阻止其人民私下使用技术的好处？
- [02:01:12] Are there equilibria where we can give everyone in an **authoritarian** country their own AI model that defends them from surveillance and there isn't a way for the **authoritarian** country to crack down on this while retaining power?  
  是否存在这样的平衡：我们可以给专制国家的每个人提供自己的AI模型来保护他们免受监视，而专制国家在保留权力的同时无法镇压这一点？
- [02:01:35] But maybe there's a middle world where there's an equilibrium where, if they want to hold on to power, the authoritarians can't deny individualized access to the technology.  
  但也许存在一个中间世界，在那里有一种平衡，如果他们想保住权力，独裁者就不能拒绝个性化的技术访问。
- [02:01:50] Is it possible that the technology might inherently have properties that have this dissolving effect on **authoritarian** structures?  
  技术是否可能本质上具有对专制结构产生这种瓦解效应的特性？
- [02:02:26] There are first principles reasons why authoritarianism might be privileged.  
  有一些第一性原理的原因说明为什么专制主义可能会占优势。

**Extra example:**
- Many citizens fled the **authoritarian** regime to seek freedom in neighboring democracies.  
  许多公民逃离了专制政权，在邻近的民主国家寻求自由。

### privilege  /ˈprɪv-ə-lɪdʒ/
**CEFR:** B2 | **Part of speech:** v. | **Occurrences:** 2

**EN:** to give an advantage or favor to someone or something  
**CN:** 给予优势，使受益

**Original examples:**
- [01:51:26] It seems like the internet **privileged** authoritarian countries more than you would've expected.  
  互联网似乎比你预期的更有利于专制国家。
- [02:02:26] There are first principles reasons why authoritarianism might be **privileged**.  
  有一些第一性原理的原因说明为什么专制主义可能会占优势。

**Extra example:**
- The new policy **privileges** large corporations over small businesses.  
  新政策使大公司比小企业更有优势。

### abstruse  /æb-ˈstruːs/
**CEFR:** C2 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** difficult to understand; obscure  
**CN:** 深奥的，难懂的

**Original examples:**
- [01:52:07] At some point you can do harder, more **abstruse** math problems, but nothing after that matters.  
  在某个时刻，你可以解决更难、更深奥的数学问题，但之后就没什么重要的了。

**Extra example:**
- The professor's lecture on quantum mechanics was too **abstruse** for most undergraduate students.  
  教授关于量子力学的讲座对大多数本科生来说太深奥了。

### distinguished  /dɪ-ˈstɪŋ-gwɪʃt/
**CEFR:** B2 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** notable or important; standing out from others  
**CN:** 显著的，重要的

**Original examples:**
- [01:52:12] Putting that aside, I do think the exponential will continue, but there will be certain **distinguished** points on the exponential.  
  撇开这一点，我确实认为指数增长会继续，但在指数曲线上会有某些显著的点。

**Extra example:**
- The researcher identified several **distinguished** phases in the product development cycle.  
  研究人员在产品开发周期中确定了几个显著的阶段。

### deterrent  /dɪ-ˈter-ənt/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** something that discourages or prevents an action, especially through fear of consequences  
**CN:** 威慑力，遏制因素

**Original examples:**
- [01:52:24] Is a nuclear **deterrent** still stable in the world of AI?  
  在AI世界中，核威慑仍然稳定吗？

**Extra example:**
- Strict penalties serve as a **deterrent** to would-be offenders.  
  严厉的惩罚对潜在的违法者起到威慑作用。

### offensive  /ə-ˈfen-sɪv/
**CEFR:** B2 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** relating to attack rather than defense  
**CN:** 进攻性的，攻击性的

**Original examples:**
- [01:52:50] There are points where if you reach a certain level, maybe you have **offensive** cyber dominance, and every computer system is transparent to you after that unless the other side has an equivalent defense.  
  有一些点，如果你达到某个水平，也许你就拥有进攻性的网络优势，之后每个计算机系统对你来说都是透明的，除非对方有相应的防御。

**Extra example:**
- The military developed new **offensive** capabilities to counter emerging threats.  
  军方开发了新的进攻能力来对抗新兴威胁。

### dominance  /ˈdɑː-mɪ-nəns/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** power and control over others; superiority  
**CN:** 优势，支配地位

**Original examples:**
- [01:52:50] There are points where if you reach a certain level, maybe you have offensive cyber **dominance**, and every computer system is transparent to you after that unless the other side has an equivalent defense.  
  有一些点，如果你达到某个水平，也许你就拥有进攻性的网络优势，之后每个计算机系统对你来说都是透明的，除非对方有相应的防御。

**Extra example:**
- The company's market **dominance** allows it to set industry standards.  
  该公司的市场支配地位使其能够制定行业标准。

### confer  /kən-ˈfɜr/
**CEFR:** C1 | **Part of speech:** v. | **Occurrences:** 1

**EN:** to grant or bestow (a right, benefit, or advantage)  
**CN:** 授予，赋予

**Original examples:**
- [01:53:04] But I think there will be either a critical moment, a small number of critical moments, or some critical window where AI **confers** some large advantage from the perspective of national security, and one country or coalition has reached it before others.  
  但我认为会有一个关键时刻、少数几个关键时刻，或某个关键窗口期，在那里AI从国家安全的角度赋予某些巨大优势，而一个国家或联盟在其他国家之前达到了这个时刻。

**Extra example:**
- The new degree will **confer** professional certification upon graduation.  
  新学位将在毕业时授予专业认证。

### coalition  /ˌkoʊ-ə-ˈlɪ-ʃən/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a temporary alliance of groups or countries for a common purpose  
**CN:** 联盟，联合

**Original examples:**
- [01:53:04] But I think there will be either a critical moment, a small number of critical moments, or some critical window where AI confers some large advantage from the perspective of national security, and one country or **coalition** has reached it before others.  
  但我认为会有一个关键时刻、少数几个关键时刻，或某个关键窗口期，在那里AI从国家安全的角度赋予某些巨大优势，而一个国家或联盟在其他国家之前达到了这个时刻。

**Extra example:**
- A **coalition** of tech companies formed to establish common privacy standards.  
  科技公司联盟成立以建立共同的隐私标准。

### blast radius  /blæst ˈreɪ-di-əs/
**CEFR:** C1 | **Part of speech:** n. phrase | **Occurrences:** 1

**EN:** the extent of damage or impact from a destructive event; the scope of potential harm  
**CN:** 影响范围，破坏半径

**Original examples:**
- [01:53:04] Operations with broad **blast radius**: recursive deletes, bulk updates, mass permission changes.  
  具有广泛影响范围的操作：递归删除、批量更新、大规模权限更改。

**Extra example:**
- We need to minimize the **blast radius** of this database migration by testing it in stages.  
  我们需要通过分阶段测试来最小化这次数据库迁移的影响范围。

### advocate  /ˈæd-və-keɪt/
**CEFR:** B2 | **Part of speech:** v. | **Occurrences:** 1

**EN:** to publicly recommend or support a policy or course of action  
**CN:** 主张，提倡

**Original examples:**
- [01:53:30] I'm not **advocating** that they just say, 'Okay, we're in charge now.'  
  我并不主张他们只是说'好的，我们现在负责了'。

**Extra example:**
- Many researchers **advocate** for stricter data privacy regulations.  
  许多研究人员主张实施更严格的数据隐私法规。

### negotiation  /nɪ-ˌgoʊ-ʃi-ˈeɪ-ʃən/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** discussion aimed at reaching an agreement  
**CN:** 谈判，协商

**Original examples:**
- [01:53:52] There's going to be some **negotiation**, implicit or explicit, about what the post-AI world order looks like.  
  关于后AI世界秩序的样子，将会有一些隐性或显性的谈判。

**Extra example:**
- The contract **negotiation** took several months to finalize all terms.  
  合同谈判花了几个月时间才最终确定所有条款。

### liberal democracy  /ˈlɪb-ər-əl dɪ-ˈmɑː-krə-si/
**CEFR:** C1 | **Part of speech:** n. phrase | **Occurrences:** 1

**EN:** a political system that combines democratic elections with protection of individual rights and freedoms  
**CN:** 自由民主制

**Original examples:**
- [01:54:05] My interest is in making that negotiation be one in which classical **liberal democracy** has a strong hand.  
  我的兴趣是让那场谈判成为古典自由民主制占有力地位的谈判。

**Extra example:**
- **Liberal democracy** depends on both electoral accountability and constitutional protections.  
  自由民主制既依赖于选举问责制，也依赖于宪法保护。

### endorse  /ɪn-ˈdɔrs/
**CEFR:** B2 | **Part of speech:** v. | **Occurrences:** 2

**EN:** to declare one's public approval or support of  
**CN:** 认可，支持

**Original examples:**
- [01:55:02] I wasn't necessarily **endorsing** that view.  
  我不一定认可那个观点。
- [01:56:02] But the point I was making that I do **endorse** is that it is quite possible that...  
  但我要说的我确实认可的观点是，很可能...

**Extra example:**
- The committee decided to **endorse** the new safety protocol after thorough review.  
  委员会经过彻底审查后决定认可新的安全协议。

### interventionist  /ˌɪn-tər-ˈven-ʃə-nɪst/
**CEFR:** C1 | **Part of speech:** adj./n. | **Occurrences:** 2

**EN:** favoring intervention, especially by a government in the affairs of other countries or in economic matters  
**CN:** 干预主义的；干预主义者

**Original examples:**
- [01:55:13] You could take this much further and have a more **interventionist** view that says authoritarian countries with AI are these self-fulfilling cycles that are very hard to displace, so you just need to get rid of them from the beginning.  
  你可以更进一步，采取更干预主义的观点，认为拥有AI的专制国家是这些自我实现的循环，很难取代，所以你需要从一开始就摆脱它们。
- [01:56:39] The **interventionist** view is one possible view.  
  干预主义观点是一种可能的观点。

**Extra example:**
- Critics argue that an **interventionist** foreign policy often leads to unintended consequences.  
  批评者认为干预主义外交政策往往会导致意想不到的后果。

### self-fulfilling  /self-fʊl-ˈfɪl-ɪŋ/
**CEFR:** C1 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** (of a prophecy or expectation) coming true because people believe it will and act accordingly  
**CN:** 自我实现的

**Original examples:**
- [01:55:13] You could take this much further and have a more interventionist view that says authoritarian countries with AI are these **self-fulfilling** cycles that are very hard to displace, so you just need to get rid of them from the beginning.  
  你可以更进一步，采取更干预主义的观点，认为拥有AI的专制国家是这些自我实现的循环，很难取代，所以你需要从一开始就摆脱它们。

**Extra example:**
- Fear of a bank run can become a **self-fulfilling** prophecy as depositors rush to withdraw their money.  
  对银行挤兑的恐惧可能成为自我实现的预言，因为储户会争相提款。

### displace  /dɪs-ˈpleɪs/
**CEFR:** B2 | **Part of speech:** v. | **Occurrences:** 1

**EN:** to take the place of; to remove from a position  
**CN:** 取代，替换

**Original examples:**
- [01:55:13] You could take this much further and have a more interventionist view that says authoritarian countries with AI are these self-fulfilling cycles that are very hard to **displace**, so you just need to get rid of them from the beginning.  
  你可以更进一步，采取更干预主义的观点，认为拥有AI的专制国家是这些自我实现的循环，很难取代，所以你需要从一开始就摆脱它们。

**Extra example:**
- New automation technologies may **displace** workers in traditional manufacturing jobs.  
  新的自动化技术可能会取代传统制造业工作中的工人。

### principle  /ˈprɪn.sə.pəl/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 10

**EN:** a fundamental truth or proposition that serves as the foundation for a system of belief or behavior  
**CN:** 原则，准则

**Original examples:**
- [02:06:31] Should we give the model a set of **principles** for how to act?  
  我们应该给模型一套行为**原则**吗？
- [02:06:48] By teaching the model **principles**, getting it to learn from **principles**, its behavior is more consistent.  
  通过教模型**原则**，让它从**原则**中学习，它的行为会更加一致。
- [02:07:15] Whereas if you give it **principles**—it has some hard guardrails like 'Don't make biological weapons' but—overall you're trying to understand what it should be aiming to do.  
  而如果你给它**原则**——它有一些硬性防护措施，比如'不要制造生物武器'——但总体上你是在试图理解它应该致力于做什么。
- [02:07:35] That's the rules versus **principles** trade-off.  
  这就是规则与**原则**的权衡。
- [02:09:01] So I actually think of it as a mostly corrigible model that has some limits, but those limits are based on **principles**.  
  所以我实际上认为它是一个基本可纠正的模型，有一些限制，但这些限制是基于**原则**的。
- [02:09:07] Then the fundamental question is, how are those **principles** determined?  
  那么根本问题是，这些**原则**是如何确定的？
- [02:09:25] But because you have been the ones to actually write down the **principles**, I get to ask you this question.  
  但因为你们是真正写下这些**原则**的人，我才能问你这个问题。
- [02:09:50] How do you think about how those **principles** should be set?  
  你如何看待这些**原则**应该如何设定？
- [02:11:29] At the level of **principles**, it has to have a certain amount of coherence.  
  在**原则**层面，它必须具有一定的连贯性。
- [02:12:13] But that is a thing you could try to do in **principle**.  
  但**原则上**这是你可以尝试做的事情。

**Extra example:**
- The company operates on the **principle** that customer satisfaction comes first.  
  公司的经营**原则**是顾客满意度第一。

### empirical  /ɪmˈpɪr.ɪ.kəl/
**CEFR:** C1 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** based on observation or experience rather than theory or pure logic  
**CN:** 经验的，实证的

**Original examples:**
- [02:06:44] It's kind of purely a practical and **empirical** thing that we've observed.  
  这纯粹是我们观察到的一个实践性和**实证性**的事情。

**Extra example:**
- The researchers needed **empirical** evidence to support their hypothesis.  
  研究人员需要**实证**证据来支持他们的假设。

### consistent  /kənˈsɪs.tənt/
**CEFR:** B2 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** acting or behaving in the same way over time; not contradictory  
**CN:** 一致的，连贯的

**Original examples:**
- [02:06:48] By teaching the model principles, getting it to learn from principles, its behavior is more **consistent**.  
  通过教模型原则，让它从原则中学习，它的行为会更加**一致**。

**Extra example:**
- Her performance has been **consistent** throughout the season.  
  她整个赛季的表现一直很**稳定**。

### edge case  /edʒ keɪs/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a problem or situation that occurs only at an extreme (maximum or minimum) operating parameter  
**CN:** 边缘情况，极端情况

**Original examples:**
- [02:06:48] It's easier to cover **edge cases**, and the model is more likely to do what people want it to do.  
  更容易覆盖**边缘情况**，模型更有可能做人们想让它做的事。

**Extra example:**
- We need to test for **edge cases** before launching the product.  
  我们需要在产品发布前测试**边缘情况**。

### guardrail  /ˈɡɑːrd.reɪl/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** a protective barrier or constraint that prevents dangerous or undesired behavior  
**CN:** 护栏，防护措施，安全限制

**Original examples:**
- [02:07:15] It has some hard **guardrails** like 'Don't make biological weapons'.  
  它有一些硬性**防护措施**，比如'不要制造生物武器'。

**Extra example:**
- The company put **guardrails** in place to prevent excessive risk-taking.  
  公司设置了**防护措施**以防止过度冒险。

### trade-off  /ˈtreɪd.ɔːf/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 2

**EN:** a balance achieved between two desirable but incompatible features; a compromise  
**CN:** 权衡，取舍

**Original examples:**
- [02:07:35] That's the rules versus principles **trade-off**.  
  这就是规则与原则的**权衡**。
- [02:07:35] Then there's another thing you're talking about, which is the corrigibility versus intrinsic motivation **trade-off**.  
  然后还有你说的另一件事，那就是可纠正性与内在动机的**权衡**。

**Extra example:**
- There's always a **trade-off** between speed and accuracy in machine learning models.  
  机器学习模型中速度和准确性之间总是存在**权衡**。

### corrigibility  /ˌkɔr.ɪ.dʒəˈbɪl.ə.ti/
**CEFR:** C2 | **Part of speech:** n. | **Occurrences:** 3

**EN:** the quality of being correctable or open to correction; willingness to accept instruction  
**CN:** 可纠正性，可改正性

**Original examples:**
- [02:07:35] Then there's another thing you're talking about, which is the **corrigibility** versus intrinsic motivation trade-off.  
  然后还有你说的另一件事，那就是**可纠正性**与内在动机的权衡。
- [02:08:29] We're actually pretty far on the **corrigible** side.  
  我们实际上在**可纠正**这一边走得很远。
- [02:09:01] So I actually think of it as a mostly **corrigible** model that has some limits.  
  所以我实际上认为它是一个基本**可纠正**的模型，有一些限制。

**Extra example:**
- The **corrigibility** of the AI system ensures it can be safely shut down if needed.  
  人工智能系统的**可纠正性**确保必要时可以安全关闭。

### intrinsic  /ɪnˈtrɪn.zɪk/
**CEFR:** C1 | **Part of speech:** adj. | **Occurrences:** 2

**EN:** belonging naturally; essential; inherent to the nature of something  
**CN:** 内在的，固有的，本质的

**Original examples:**
- [02:07:35] Then there's another thing you're talking about, which is the corrigibility versus **intrinsic** motivation trade-off.  
  然后还有你说的另一件事，那就是可纠正性与**内在**动机的权衡。
- [02:07:51] How much should the model have an inherent set of values and go off and do things on its own versus how much should the model be a kind of 'skin suit' where it just directly follows the instructions?  
  模型应该在多大程度上拥有**固有的**价值观并自行行事，而不是应该在多大程度上成为一种'外壳'，直接遵循指令？

**Extra example:**
- The **intrinsic** value of education goes beyond just earning a degree.  
  教育的**内在**价值远不止获得学位。

### constitution  /ˌkɑn.stəˈtuː.ʃən/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 13

**EN:** a set of fundamental principles or established precedents that govern an organization or system  
**CN:** 宪法，章程，基本原则

**Original examples:**
- [02:09:07] I think we say it in various ways in the **constitution**.  
  我认为我们在**章程**中以各种方式表达了这一点。
- [02:09:15] Normally, a **constitution** is written down, set in stone, and there's a process of updating it and changing it.  
  通常，**宪法**是写下来的，一成不变的，有一个更新和修改的过程。
- [02:09:58] One is we iterate within Anthropic. We train the model, we're not happy with it, and we change the **constitution**.  
  一个是我们在Anthropic内部迭代。我们训练模型，对它不满意，然后我们改变**章程**。
- [02:10:06] Putting out public updates to the **constitution** every once in a while is good because people can comment on it.  
  不时发布**章程**的公开更新是好的，因为人们可以对其发表评论。
- [02:10:10] The second level of loop is different companies having different **constitutions**.  
  第二层循环是不同的公司有不同的**章程**。
- [02:10:28] Anthropic puts out a **constitution**, Gemini puts out a **constitution**, and other companies put out a **constitution**.  
  Anthropic发布了一个**章程**，Gemini发布了一个**章程**，其他公司也发布了**章程**。
- [02:10:40] I like this thing from this **constitution** and this thing from that **constitution**.  
  我喜欢这个**章程**中的这一点和那个**章程**中的那一点。
- [02:11:15] We did an experiment with the Collective Intelligence Project to basically poll people and ask them what should be in our AI **constitution**.  
  我们与集体智慧项目进行了一项实验，基本上是调查人们，询问他们认为我们的人工智能**章程**中应该包含什么。
- [02:11:17] So you could imagine doing something like that with the new approach we've taken to the **constitution**.  
  所以你可以想象用我们对**章程**采取的新方法做类似的事情。
- [02:11:23] It was an easier approach to take when the **constitution** was a list of dos and don'ts.  
  当**章程**是一系列该做和不该做的清单时，这是一种更容易采用的方法。
- [02:12:00] But there's no reason you couldn't, in principle, say, 'All AI models have to have a **constitution** that starts with these things.'  
  但原则上，你完全可以说，'所有人工智能模型都必须有一个以这些内容开头的**章程**。'
- [02:12:42] There's not this vague sense in which the Supreme Court will feel out how people are feeling—what are the vibes—and update the **constitution** accordingly.  
  不存在这种模糊的感觉，即最高法院会感受人们的感受——什么是氛围——并相应地更新**宪法**。
- [02:12:55] But you have a vision of competition between **constitutions**, which is actually very reminiscent of how some libertarian charter cities people used to talk about what an archipelago of different kinds of governments would look like.  
  但你有一个**章程**之间竞争的愿景，这实际上让人想起一些自由主义宪章城市的人过去如何谈论不同类型政府的群岛会是什么样子。

**Extra example:**
- The company's **constitution** outlines its core values and operating principles.  
  公司的**章程**概述了其核心价值观和运营原则。

### iterate  /ˈɪt.ə.reɪt/
**CEFR:** C1 | **Part of speech:** v. | **Occurrences:** 2

**EN:** to perform or repeat a process with the aim of approaching a desired goal or result  
**CN:** 迭代，重复改进

**Original examples:**
- [02:09:50] I think there are maybe three sizes of loop here, three ways to **iterate**.  
  我认为这里可能有三种规模的循环，三种**迭代**方式。
- [02:09:58] One is we **iterate** within Anthropic.  
  一个是我们在Anthropic内部**迭代**。

**Extra example:**
- The team will **iterate** on the design based on user feedback.  
  团队将根据用户反馈对设计进行**迭代**。

### critique  /krɪˈtiːk/
**CEFR:** B2 | **Part of speech:** v./n. | **Occurrences:** 1

**EN:** to evaluate or analyze something in a detailed and analytical way  
**CN:** 批评，评论，评价

**Original examples:**
- [02:10:28] People can look at them and compare. Outside observers can **critique** and say, 'I like this thing from this constitution and this thing from that constitution.'  
  人们可以查看并比较。外部观察者可以**评论**并说，'我喜欢这个章程中的这一点和那个章程中的那一点。'

**Extra example:**
- The professor asked students to **critique** each other's research proposals.  
  教授要求学生们相互**评价**对方的研究提案。

### incentive  /ɪnˈsen.tɪv/
**CEFR:** B2 | **Part of speech:** n. | **Occurrences:** 1

**EN:** something that motivates or encourages someone to do something  
**CN:** 激励，刺激，动机

**Original examples:**
- [02:10:40] That creates a soft **incentive** and feedback for all the companies to take the best of each element and improve.  
  这为所有公司创造了一种软性**激励**和反馈，让它们吸取每个元素的精华并改进。

**Extra example:**
- The company offers financial **incentives** to employees who exceed their sales targets.  
  公司为超额完成销售目标的员工提供经济**激励**。

### coherence  /koʊˈhɪr.əns/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** the quality of being logical and consistent; the state of forming a unified whole  
**CN:** 连贯性，一致性，条理性

**Original examples:**
- [02:11:29] At the level of principles, it has to have a certain amount of **coherence**.  
  在原则层面，它必须具有一定的**连贯性**。

**Extra example:**
- The argument lacks **coherence** and jumps between unrelated topics.  
  这个论点缺乏**连贯性**，在不相关的话题之间跳跃。

### representative  /ˌrep.rɪˈzen.tə.tɪv/
**CEFR:** B2 | **Part of speech:** adj./n. | **Occurrences:** 1

**EN:** serving to represent a larger group; typical of a class or group  
**CN:** 代表性的，典型的；代表

**Original examples:**
- [02:11:37] You could also imagine—and this is a crazy idea, but this whole interview is about crazy ideas—systems of **representative** government having input.  
  你也可以想象——这是一个疯狂的想法，但整个采访都是关于疯狂的想法——**代议制**政府系统有发言权。

**Extra example:**
- The survey used a **representative** sample of the population.  
  这项调查使用了具有**代表性**的人口样本。

### legislative  /ˈledʒ.ɪ.sleɪ.tɪv/
**CEFR:** C1 | **Part of speech:** adj. | **Occurrences:** 2

**EN:** relating to the making of laws; having the power to make laws  
**CN:** 立法的，制定法律的

**Original examples:**
- [02:11:52] I wouldn't do this today because the **legislative** process is so slow.  
  我今天不会这样做，因为**立法**过程太慢了。
- [02:11:55] This is exactly why I think we should be careful about the **legislative** process and AI regulation.  
  这正是我认为我们应该谨慎对待**立法**过程和人工智能监管的原因。

**Extra example:**
- The **legislative** branch is responsible for creating new laws.  
  **立法**部门负责制定新法律。

### precedence  /ˈpres.ə.dəns/
**CEFR:** C1 | **Part of speech:** n. | **Occurrences:** 1

**EN:** the condition of being considered more important than someone or something else; priority  
**CN:** 优先权，优先地位

**Original examples:**
- [02:12:13] All AI models have to have a constitution that starts with these things, and then you can append other things after it, but there has to be this special section that takes **precedence**.  
  所有人工智能模型都必须有一个以这些内容开头的章程，然后你可以在后面附加其他内容，但必须有这个优先的特殊部分。

**Extra example:**
- Safety regulations take **precedence** over production speed in this factory.  
  在这家工厂，安全法规优先于生产速度。

### rigid  /ˈrɪdʒ.ɪd/
**CEFR:** B2 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** unable to bend or be forced out of shape; not flexible; strict and inflexible  
**CN:** 僵硬的，死板的，严格的

**Original examples:**
- [02:12:13] I wouldn't do that. That's too **rigid** and sounds overly prescriptive in a way that I think overly aggressive legislation is.  
  我不会那样做。那太**死板**了，听起来过于规定性，我认为过于激进的立法就是这样。

**Extra example:**
- The company's **rigid** policies don't allow for any flexibility in work hours.  
  公司**死板的**政策不允许工作时间有任何灵活性。

### prescriptive  /prɪˈskrɪp.tɪv/
**CEFR:** C1 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** giving explicit rules or directions; authoritative in giving directions  
**CN:** 规定性的，指令性的

**Original examples:**
- [02:12:13] That's too rigid and sounds overly **prescriptive** in a way that I think overly aggressive legislation is.  
  那太死板了，听起来过于**规定性**，我认为过于激进的立法就是这样。

**Extra example:**
- The manual is highly **prescriptive**, leaving no room for creative problem-solving.  
  这本手册极具**规定性**，没有留下创造性解决问题的空间。

### in lieu of  /ɪn ˈluː əv/
**CEFR:** C1 | **Part of speech:** prep. phrase | **Occurrences:** 1

**EN:** instead of; in place of  
**CN:** 代替，取代

**Original examples:**
- [2:21:46] Well, **in lieu of** an external Dario Vision Quest, we have this interview.  
  好吧，**作为**外部Dario愿景探索的替代，我们有这次访谈。

**Extra example:**
- **In lieu of** a cash bonus, employees were offered additional vacation days.  
  **作为**现金奖金的替代，员工们获得了额外的假期。

### external  /ɪkˈstɜːrnəl/
**CEFR:** B2 | **Part of speech:** adj. | **Occurrences:** 1

**EN:** coming from or relating to the outside; not internal or inherent  
**CN:** 外部的，外来的

**Original examples:**
- [2:21:46] Well, in lieu of an **external** Dario Vision Quest, we have this interview.  
  好吧，作为**外部的**Dario愿景探索的替代，我们有这次访谈。

**Extra example:**
- The company hired **external** consultants to audit their security practices.  
  公司聘请了**外部**顾问来审计他们的安全措施。

---

## Useful Phrases

### plus or minus
**Type:** collocation

**EN:** approximately, give or take a certain amount  
**CN:** 上下浮动，大约

**Original examples:**
- [00:10] There's **plus or minus** a year or two here and there.  
  这里那里有一两年的上下浮动。

**Extra example:**
- The project will take six months, **plus or minus** a few weeks.  
  这个项目需要六个月，上下浮动几周。

### within the bubble
**Type:** collocation

**EN:** inside a particular industry or community that is isolated from broader society  
**CN:** 在（某个行业或圈子的）圈内

**Original examples:**
- [01:02] To me, it is absolutely wild that you have people — **within the bubble** and outside the bubble — talking about the same tired, old hot-button political issues.  
  对我来说，真是太疯狂了，无论是圈内还是圈外的人都在讨论那些陈旧的热点政治问题。

**Extra example:**
- **Within the bubble** of Silicon Valley, these ideas seem obvious, but they're not to most people.  
  在硅谷圈内，这些想法似乎显而易见，但对大多数人来说并非如此。

### scale to the moon
**Type:** idiom

**EN:** to expand or grow indefinitely or to an extreme degree  
**CN:** 无限扩展，极大程度地增长

**Literal:** 扩展到月球  
**Figurative EN:** to grow or expand without apparent limits, to achieve extreme scalability  
**Figurative CN:** 无限制地增长或扩展，实现极致的可扩展性

**Original examples:**
- [03:20] The fifth is that you need an objective function that can **scale to the moon**.  
  第五点是你需要一个能够无限扩展的目标函数。

**Extra example:**
- We need a business model that can **scale to the moon** as demand grows.  
  我们需要一个能够随着需求增长而无限扩展的商业模式。

### red herring
**Type:** idiom

**EN:** something that misleads or distracts from the relevant issue  
**CN:** 转移注意力的事物，无关紧要的线索

**Literal:** 红色的鲱鱼  
**Figurative EN:** a misleading clue or distraction that diverts attention from the main issue  
**Figurative CN:** 转移注意力的误导性线索，分散对主要问题关注的干扰

**Original examples:**
- [06:29] Let me take the RL out of it for a second, because I actually think it's a **red herring** to say that RL is any different from pre-training in this matter.  
  让我先把强化学习放在一边，因为我认为说强化学习在这件事上与预训练有何不同纯属转移注意力。

**Extra example:**
- The focus on syntax errors was a **red herring** - the real problem was in the logic.  
  对语法错误的关注是个误导——真正的问题在于逻辑。

### blank slate
**Type:** idiom

**EN:** something with no prior knowledge or experience; a fresh start  
**CN:** 白板，没有先验知识的状态

**Literal:** 空白的石板  
**Figurative EN:** a state with no preexisting knowledge, biases, or predetermined characteristics  
**Figurative CN:** 没有预先存在的知识、偏见或预定特征的状态

**Original examples:**
- [09:38] Our brain isn't just a **blank slate**.  
  我们的大脑不仅仅是一块白板。
- [09:43] The language models are much more like **blank slates**.  
  语言模型更像是白板。

**Extra example:**
- Each new employee isn't a **blank slate** - they bring experience from previous roles.  
  每个新员工都不是白板——他们从之前的职位带来了经验。

### up close
**Type:** collocation

**EN:** from a very near distance; with close observation or direct experience  
**CN:** 近距离地，亲身体验地

**Original examples:**
- [11:29] That was the transition from GPT-1 to GPT-2 that I saw **up close**.  
  那就是我近距离见证的从GPT-1到GPT-2的转变。

**Extra example:**
- Having worked with the team, I've seen their challenges **up close**.  
  和团队一起工作过，我近距离看到了他们的挑战。

### to the extent that
**Type:** collocation

**EN:** insofar as; to the degree that something is true  
**CN:** 在……范围内，就……而言

**Original examples:**
- [12:08] So **to the extent that** we are building these RL environments, the goal is very similar to what was done five or ten years ago with pre-training.  
  所以就我们构建这些强化学习环境而言，目标与五到十年前预训练的做法非常相似。

**Extra example:**
- **To the extent that** the data is accurate, we can rely on these predictions.  
  就数据准确而言，我们可以依赖这些预测。

### color in
**Type:** phrasal_verb

**EN:** to complete or fill in details; to fully develop something  
**CN:** 填充完整，补充细节

**Original examples:**
- [16:34] We don't fully **color in** the other side of the box.  
  我们没有完全填充方框的另一边。

**Extra example:**
- Once we have the framework, we can **color in** the specific implementation details.  
  一旦我们有了框架，就可以填充具体的实现细节。

### lay out
**Type:** phrasal_verb

**EN:** to explain or present something clearly and systematically  
**CN:** 清楚系统地解释或呈现某事

**Original examples:**
- [18:03] Let me **lay out** the spectrum.  
  让我来清楚地说明这个范围。

**Extra example:**
- She **laid out** her plan for the project in detail.  
  她详细地阐述了她的项目计划。

### worlds apart
**Type:** idiom

**EN:** completely different, vastly dissimilar  
**CN:** 完全不同，天壤之别

**Literal:** 世界分离  
**Figurative EN:** extremely different or separated by a great degree  
**Figurative CN:** 极其不同或有巨大差异

**Original examples:**
- [18:27] Those things are **worlds apart**.  
  这些事情完全是两码事。

**Extra example:**
- Their political views are **worlds apart**.  
  他们的政治观点天壤之别。

### end-to-end
**Type:** collocation

**EN:** covering the entire process from beginning to completion  
**CN:** 覆盖从开始到完成的整个过程

**Original examples:**
- [18:41] 90% of the **end-to-end** SWE tasks — including things like compiling, setting up clusters and environments, testing features, writing memos — are done by the models.  
  90%的端到端软件工程任务——包括编译、设置集群和环境、测试功能、写备忘录——由模型完成。

**Extra example:**
- We need an **end-to-end** solution for our supply chain management.  
  我们需要一个端到端的供应链管理解决方案。

### close the loop
**Type:** idiom

**EN:** to complete a process or provide feedback  
**CN:** 完成流程或提供反馈，形成闭环

**Literal:** 闭合环路  
**Figurative EN:** to complete a cycle by connecting the end back to the beginning, often through feedback or follow-up  
**Figurative CN:** 通过反馈或后续行动将结果与开始连接起来，形成完整循环

**Original examples:**
- [20:09] **Closing the loop** on self-contained systems, whether it's just writing software or something, how much broader gains would we see just from that?  
  在自包含系统上形成闭环，无论是编写软件还是其他，仅凭这一点我们能看到多大的收益？
- [22:25] So I think we should be thinking about this middle world where things are extremely fast, but not instant, where they take time because of economic diffusion, because of the need to **close the loop**.  
  所以我认为我们应该思考这个中间状态，事物发展极快但并非瞬时，需要时间是因为经济扩散，因为需要形成闭环。

**Extra example:**
- Let me **close the loop** by sending you a summary of our discussion.  
  让我给你发一份讨论总结来完成这个流程。

### bend
**Type:** collocation

**EN:** (of a curve or trend) to change direction or slow down  
**CN:** （曲线或趋势）改变方向或放缓

**Original examples:**
- [22:10] I would even guess that it **bends** somewhat this year, but that is a fast curve.  
  我甚至猜测今年会有所放缓，但那仍是一条快速增长的曲线。

**Extra example:**
- The growth curve will eventually **bend** as we reach market saturation.  
  随着市场饱和，增长曲线最终会放缓。

### hot take
**Type:** idiom

**EN:** a provocative or controversial opinion, often stated boldly  
**CN:** 有争议的大胆观点，辣评

**Literal:** 热辣的看法  
**Figurative EN:** a bold, often controversial opinion expressed quickly without much deliberation  
**Figurative CN:** 一个大胆的、通常有争议的、未经深思熟虑快速表达的观点

**Original examples:**
- [23:44] Can I try a **hot take** on you?  
  我能提一个尖锐的看法吗？

**Extra example:**
- Here's my **hot take**: remote work is overrated.  
  我的辣评是：远程工作被高估了。

### cope
**Type:** collocation

**EN:** (slang) an excuse or rationalization to avoid facing reality  
**CN:** （俚语）逃避现实的借口或合理化

**Original examples:**
- [23:45] I feel like diffusion is **cope** that people say.  
  我觉得扩散是人们用来逃避的说辞。

**Extra example:**
- Saying 'it's just luck' is **cope** for not working hard enough.  
  说'只是运气好'是为努力不够找借口。

### talk past each other
**Type:** idiom

**EN:** to fail to communicate effectively because of misunderstanding  
**CN:** 因误解而无法有效沟通，答非所问

**Literal:** 互相说过去  
**Figurative EN:** to communicate without understanding each other, often by addressing different points or using different assumptions  
**Figurative CN:** 由于关注不同要点或使用不同假设而无法相互理解地交流

**Original examples:**
- [29:42] Because there are so many different things to disambiguate, it can be easy to **talk past each other** when we're talking about capabilities.  
  因为有太多需要澄清的事情，我们在谈论能力时很容易答非所问。

**Extra example:**
- We kept **talking past each other** until we defined our terms clearly.  
  直到我们清楚地定义术语后，才不再各说各话。

### get back to
**Type:** phrasal_verb

**EN:** to return to discussing or dealing with something  
**CN:** 回到（某个话题或问题）

**Original examples:**
- [33:20] This **gets back to** what we were talking about before with learning on the job.  
  这又回到了我们之前讨论的在职学习的话题。

**Extra example:**
- Let's **get back to** the main issue we need to resolve.  
  让我们回到需要解决的主要问题上。

### end to end
**Type:** collocation

**EN:** completely, from start to finish  
**CN:** 从头到尾，端到端

**Original examples:**
- [33:20] I don't think people would say that learning on the job is what is preventing the coding agents from doing everything **end to end**.  
  我认为人们不会说在职学习是阻碍编码代理从头到尾完成所有工作的原因。

**Extra example:**
- We need to test the system **end to end** before launch.  
  我们需要在发布前对系统进行端到端测试。

### high up on the list
**Type:** collocation

**EN:** among the most important or prioritized items  
**CN:** 在列表中排名靠前，属于重点事项

**Original examples:**
- [34:04] That's not **high up on the list** of complaints I see.  
  这在我看到的投诉清单中排名并不靠前。

**Extra example:**
- Security improvements are **high up on the list** of our priorities this quarter.  
  安全改进是我们本季度的优先事项之一。

### square
**Type:** idiom

**EN:** to reconcile or make consistent (two seemingly contradictory things)  
**CN:** 使一致，调和（两个看似矛盾的事物）

**Literal:** 使成方形  
**Figurative EN:** to reconcile or resolve an apparent contradiction or inconsistency  
**Figurative CN:** 调和或解决明显的矛盾或不一致

**Original examples:**
- [35:05] I'm trying to **square** the qualitative feeling that people feel with these models versus the macro level results.  
  我试图调和人们对这些模型的主观感受与宏观层面的结果之间的矛盾。
- [46:48] I'm trying to **square** these two views.  
  我试图调和这两种观点。

**Extra example:**
- I can't **square** his claim of poverty with his expensive lifestyle.  
  我无法调和他声称贫穷与奢侈生活方式之间的矛盾。

### zero time for bullshit
**Type:** idiom

**EN:** no tolerance for nonsense or wasted effort  
**CN:** 不容忍废话或无用功

**Literal:** 对废话没有时间  
**Figurative EN:** no patience or tolerance for inefficiency, pretense, or meaningless activity  
**Figurative CN:** 对低效、虚假或无意义的活动毫无耐心或容忍度

**Original examples:**
- [36:18] There is **zero time for bullshit**. There is zero time for feeling like we're productive when we're not.  
  我们没有时间搞那些没用的东西。没有时间自我感觉良好而实际上并不高效。

**Extra example:**
- In a startup environment, there's **zero time for bullshit** - we need real results.  
  在创业环境中，没时间搞虚的——我们需要真实的成果。

### shift around
**Type:** phrasal_verb

**EN:** to change positions or rankings among a group  
**CN:** （在一组中）改变位置或排名

**Original examples:**
- [37:14] What I see instead is that people are just **shifting around** the podium every few months.  
  我看到的是，人们每隔几个月就在领奖台上换位置。

**Extra example:**
- The top three teams keep **shifting around** in the league standings.  
  前三名的球队在联赛排名中不断变换位置。

### gather momentum
**Type:** collocation

**EN:** to gradually gain speed, force, or strength  
**CN:** 逐渐获得动力、力量或势头

**Original examples:**
- [39:00] We're seeing this snowball **gather momentum** where it's like 10%, 20%, 25%, 40%.  
  我们看到这个雪球正在积聚动力，从10%、20%、25%到40%。

**Extra example:**
- The reform movement is starting to **gather momentum** across the country.  
  改革运动开始在全国范围内积聚势头。

### on the fly
**Type:** idiom

**EN:** while in progress; without preparation or planning  
**CN:** 在进行中；即兴地，动态地

**Literal:** 在飞行中  
**Figurative EN:** during the process, spontaneously, or while something is happening  
**Figurative CN:** 在过程中，即兴地，或在事情发生时动态地

**Original examples:**
- [40:05] If AI doesn't develop this ability to learn **on the fly**, I'm a bit skeptical.  
  如果AI不能发展出这种即时学习的能力，我会持怀疑态度。

**Extra example:**
- She's great at making decisions **on the fly** when plans change unexpectedly.  
  当计划意外改变时，她很擅长即兴做决定。

### settle on
**Type:** phrasal_verb

**EN:** to decide on something after considering various options  
**CN:** 最终决定，确定（经过考虑后）

**Original examples:**
- [50:32] Where I've **settled on** it is that it will be faster than anything we've seen in the world, but it still has its limits.  
  我最终的结论是，它会比我们见过的任何事物都快，但仍然有其局限性。

**Extra example:**
- After weeks of debate, we finally **settled on** the new design.  
  经过几周的讨论，我们最终确定了新设计。

### build out
**Type:** phrasal_verb

**EN:** to expand or develop infrastructure fully  
**CN:** 扩建，全面建设（基础设施）

**Original examples:**
- [50:54] It takes a year or two to actually **build out** the data centers, to reserve the data center.  
  实际扩建数据中心、预订数据中心需要一到两年时间。

**Extra example:**
- The company plans to **build out** its network infrastructure over the next three years.  
  公司计划在未来三年内全面建设其网络基础设施。

### go bankrupt
**Type:** collocation

**EN:** to become unable to pay debts and have one's business fail financially  
**CN:** 破产，资不抵债

**Original examples:**
- [51:39] If my revenue is not $1 trillion dollars, if it's even $800 billion, there's no force on earth, there's no hedge on earth that could stop me from **going bankrupt** if I buy that much compute.  
  如果我的收入不是1万亿美元，哪怕是8000亿美元，如果我购买那么多算力，世界上没有任何力量、任何对冲手段能阻止我破产。
- [51:56] If I'm just off by a year in that rate of growth, or if the growth rate is 5x a year instead of 10x a year, then you **go bankrupt**.  
  如果我对增长率的判断哪怕只差一年，或者增长率是每年5倍而不是10倍，那你就会破产。
- [55:50] But second, what if the country of geniuses comes, but it comes in mid-2028 instead of mid-2027? You **go bankrupt**.  
  但其次，如果天才之国确实会到来，但它在2028年中而不是2027年中到来怎么办？你会破产。

**Extra example:**
- Many small businesses **went bankrupt** during the economic downturn.  
  许多小企业在经济衰退期间破产了。

### YOLO
**Type:** idiom

**EN:** to act recklessly without considering consequences (acronym for 'you only live once')  
**CN:** 鲁莽行事，不计后果（'你只活一次'的缩写）

**Literal:** 你只活一次  
**Figurative EN:** acting impulsively or recklessly without proper planning or risk assessment  
**Figurative CN:** 冲动行事或鲁莽行动，没有适当的计划或风险评估

**Original examples:**
- [52:33] It's actually the other things, like have we been thoughtful about it or are we **YOLOing** and saying, we're going to do $100 billion here or $100 billion there?  
  实际上是其他方面，比如我们是否经过深思熟虑，还是我们在鲁莽行事，说要在这里投1000亿或在那里投1000亿？

**Extra example:**
- Don't just **YOLO** your investments - do proper research first.  
  不要鲁莽投资——先做好适当的调研。

### write down
**Type:** phrasal_verb

**EN:** to record something in writing, especially calculations or plans  
**CN:** 写下，记录（尤指计算或计划）

**Original examples:**
- [52:38] I get the impression that some of the other companies have not **written down** the spreadsheet, that they don't really understand the risks they're taking.  
  我的印象是，其他一些公司还没有把电子表格写出来，他们并不真正理解自己所承担的风险。

**Extra example:**
- Before the meeting, **write down** all your key points so you don't forget anything.  
  开会前，把所有要点写下来，这样你就不会忘记任何事情。

### work out
**Type:** phrasal_verb

**EN:** to develop successfully or reach a conclusion  
**CN:** 成功进行，得出结果

**Original examples:**
- [54:30] Okay, and then suppose it takes a year for the clinical trials to **work out** so that you're getting revenue from that and can make more drugs.  
  好的，然后假设临床试验需要一年时间才能成功，这样你就能从中获得收入并制造更多药物。

**Extra example:**
- I hope the negotiations **work out** and we can close the deal.  
  我希望谈判能成功进行，我们能够完成交易。

### in accordance
**Type:** collocation

**EN:** in agreement or harmony with something; matching or consistent with  
**CN:** 与...一致，符合

**Original examples:**
- [56:11] Even in the longest version of the timelines you state, the compute you are ramping up to build doesn't seem **in accordance**.  
  即使在你所说的最长时间线版本中，你正在逐步建设的算力似乎也不相符。

**Extra example:**
- All decisions must be made **in accordance** with company policy.  
  所有决策必须符合公司政策。

### squeeze
**Type:** idiom

**EN:** to reduce or compress something, often by applying pressure or constraints  
**CN:** 压缩，挤压（资源、时间等）

**Literal:** 挤压，榨取  
**Figurative EN:** to reduce available resources, space, or time by applying pressure or constraints  
**Figurative CN:** 通过施加压力或限制来减少可用的资源、空间或时间

**Original examples:**
- [01:01:01] If you get more demand than you thought, then research gets **squeezed**, but you're kind of able to support more inference and you're more profitable.  
  如果需求超出预期，那么研究就会被压缩，但你能够支持更多推理，并且更有盈利能力。

**Extra example:**
- Budget cuts will **squeeze** the R&D department's ability to innovate.  
  预算削减将压缩研发部门的创新能力。

### of order
**Type:** collocation

**EN:** approximately, roughly (used in mathematical or scientific contexts)  
**CN:** 大约，量级为（用于数学或科学语境）

**Original examples:**
- [01:04:05] What I'm saying is the log-linear return, what it leads to is you spend **of order** one fraction of the business.  
  我的意思是对数线性回报导致你花费大约业务的一部分。

**Extra example:**
- The solution should take **of order** n-squared time complexity.  
  这个解决方案的时间复杂度应该是n的平方量级。

### put aside
**Type:** phrasal_verb

**EN:** to exclude from consideration; to set something to one side  
**CN:** 搁置不谈；把某事放在一边

**Original examples:**
- [01:05:02] **Put aside** Anthropic. I don't want to give information about Anthropic.  
  先不谈Anthropic。我不想透露关于Anthropic的信息。

**Extra example:**
- Let's **put aside** our disagreements and focus on the common goal.  
  让我们搁置分歧，专注于共同目标。

### fall behind
**Type:** phrasal_verb

**EN:** to fail to keep up with others; to lag  
**CN:** 落后；跟不上

**Original examples:**
- [01:05:38] It should be clear why you don't just serve the current models and never train another model, because then you don't have any demand because you'll **fall behind**.  
  很明显为什么你不能只服务现有模型而不训练新模型，因为那样你就会失去需求，因为你会落后。

**Extra example:**
- If we don't invest in R&D, we'll **fall behind** our competitors.  
  如果我们不投资研发，就会落后于竞争对手。

### get at
**Type:** phrasal_verb

**EN:** to try to express or explain something; to suggest or imply  
**CN:** 想要表达；暗示

**Original examples:**
- [01:05:55] That's what I'm **getting at**.  
  这就是我想说的。
- [01:06:57] So stepping back, the thing I'm trying to **get at** is more that it seems like your worldview is compatible...  
  所以退一步看，我想要表达的是，你的世界观似乎是兼容的……

**Extra example:**
- I'm not sure what you're **getting at** - could you be more specific?  
  我不确定你想表达什么——你能更具体一点吗？

### stepping back
**Type:** collocation

**EN:** pausing to consider the broader perspective; taking a more distant view  
**CN:** 退一步看；从更宏观的角度考虑

**Original examples:**
- [01:06:42] Maybe **stepping back**, I'm not saying I think the 'country of geniuses' is going to come in two years.  
  或许退一步讲，我并不是说'天才之国'会在两年内到来。
- [01:06:57] So **stepping back**, the thing I'm trying to get at is more that it seems like your worldview is compatible with somebody who says...  
  所以退一步看，我想说的是，你的世界观似乎与那些认为……的人是一致的。

**Extra example:**
- **Stepping back**, we need to ask whether this strategy aligns with our mission.  
  退一步看，我们需要问问这个策略是否符合我们的使命。

### equilibrate to
**Type:** collocation

**EN:** to reach a state of balance or equilibrium at a certain level  
**CN:** 达到平衡状态；趋于某个水平

**Original examples:**
- [01:09:05] The point is it doesn't **equilibrate to** perfect competition with zero margins.  
  关键是它不会达到零利润的完全竞争平衡状态。
- [01:09:15] If there's three firms in the economy and all are kind of independently behaving rationally, it doesn't **equilibrate to** zero.  
  如果经济中有三家公司，都独立理性地行事，它不会趋于零利润。

**Extra example:**
- In a free market, prices should **equilibrate to** the point where supply meets demand.  
  在自由市场中，价格应该达到供需平衡点。

### level out
**Type:** phrasal_verb

**EN:** to become stable or constant after a period of change  
**CN:** 趋于平稳；稳定下来

**Original examples:**
- [01:10:31] The equilibrium I'm talking about is an equilibrium where we have the 'country of geniuses in a data center', but that model training scale-up has equilibrated more. Maybe it's still going up. We're still trying to predict the demand, but it's more **leveled out**.  
  我说的平衡是指我们有了'数据中心里的天才之国'，但模型训练规模的扩张已经更加平衡了。也许还在增长，我们还在试图预测需求，但已经更加趋于平稳了。

**Extra example:**
- After the initial spike, user growth **leveled out** at around 10% per quarter.  
  在最初的激增之后，用户增长趋于平稳，保持在每季度10%左右。

### run out of
**Type:** phrasal_verb

**EN:** to use up the supply of something; to exhaust  
**CN:** 用完；耗尽

**Original examples:**
- [01:11:37] At some point you **run out of** money in the economy.  
  在某个时刻，经济中的资金会用完。

**Extra example:**
- We're **running out of** time to complete this project before the deadline.  
  我们快没时间在截止日期前完成这个项目了。

### tack on
**Type:** phrasal_verb

**EN:** to add something extra, especially time or cost  
**CN:** 额外添加（尤指时间或费用）

**Original examples:**
- [01:19:40] So will robotics be revolutionized? Yeah, maybe **tack on** another year or two.  
  机器人技术会被革新吗？会的，也许再加上一两年时间。

**Extra example:**
- The contractor **tacked on** an extra $5,000 for unexpected repairs.  
  承包商为意外维修额外加了5000美元。

### keep track of
**Type:** collocation

**EN:** to monitor or maintain awareness of something's status or location  
**CN:** 追踪，记录

**Original examples:**
- [01:20:51] People talked about, "How do your models **keep track of** nouns and verbs?"  
  人们讨论过，"你的模型如何追踪名词和动词？"

**Extra example:**
- I use a spreadsheet to **keep track of** my expenses.  
  我用电子表格来记录我的开支。

### turn out
**Type:** phrasal_verb

**EN:** to be discovered or proven to be; to result in a particular way  
**CN:** 结果是，证明是

**Original examples:**
- [01:21:16] But then suddenly it **turns out** you can do code and math very well.  
  但后来突然发现你可以很好地处理代码和数学。
- [01:27:06] I think because it's a new industry, a lot of things are going to be tried. I don't know what will **turn out** to be the right thing.  
  我认为因为这是一个新兴行业，会尝试很多东西。我不知道什么会被证明是正确的。

**Extra example:**
- The rumor **turned out** to be completely false.  
  这个谣言被证明是完全错误的。

### crack up
**Type:** phrasal_verb

**EN:** to laugh uncontrollably; to find something very funny  
**CN:** 大笑，忍不住笑

**Original examples:**
- [01:22:35] It always makes me **crack up** because you've been an AI researcher for 10 years.  
  这总是让我忍不住大笑，因为你已经做了10年的AI研究员了。

**Extra example:**
- His jokes always make the whole office **crack up**.  
  他的笑话总是让整个办公室的人笑翻。

### run into
**Type:** phrasal_verb

**EN:** to encounter or experience (a problem or difficulty)  
**CN:** 遇到（问题或困难）

**Original examples:**
- [01:24:32] The chatbot is already **running into** limitations where making it smarter doesn't really help the average consumer that much.  
  聊天机器人已经遇到了限制，让它变得更聪明对普通消费者并没有太大帮助。

**Extra example:**
- We **ran into** technical difficulties during the presentation.  
  我们在演示过程中遇到了技术困难。

### end up
**Type:** phrasal_verb

**EN:** to eventually reach a particular state or situation  
**CN:** 最终成为，最后处于

**Original examples:**
- [01:25:34] 100 of them become startups and ten of them become big successful startups. Two or three really **end up** being the way that people use the model of a given generation.  
  其中100个成为初创公司，10个成为大型成功的初创公司。其中两三个最终成为人们使用某一代模型的方式。
- [01:27:49] I wonder if you have an accounting of why it had to be Anthropic or how Anthropic **ended up** building an application in addition to the model underlying it that was successful.  
  我想知道你是否能解释为什么必须是Anthropic，或者Anthropic是如何最终在成功构建底层模型的同时还构建了一个应用程序。

**Extra example:**
- If you don't study, you'll **end up** failing the exam.  
  如果你不学习，最后会考试不及格。

### get used to
**Type:** collocation

**EN:** to become familiar or comfortable with something through repeated exposure  
**CN:** 习惯于，适应

**Original examples:**
- [01:34:34] My worry is, if we had 100 years for this to happen all very slowly, we'd **get used to** it.  
  我担心的是，如果我们有100年让这一切慢慢发生，我们会习惯它。
- [01:34:58] We would **get used to** it over 100 years and we'd develop governance mechanisms.  
  我们会在100年的时间里习惯它，并发展出治理机制。

**Extra example:**
- It took me a while to **get used to** the new software interface.  
  我花了一段时间才习惯新的软件界面。

### the only game in town
**Type:** idiom

**EN:** the only available option or the only thing worth considering  
**CN:** 唯一的选择，唯一可行的办法

**Literal:** 镇上唯一的游戏  
**Figurative EN:** the only available or viable option in a particular situation  
**Figurative CN:** 在特定情况下唯一可用或可行的选择

**Original examples:**
- [01:35:07] It seems like in an offense-dominant world, over the course of the next century—the idea is that AI is making the progress that would happen over the next century happen in some period of five to ten years—we would still need the same mechanisms, or balance of power would be similarly intractable, even if humans were **the only game in town**.  
  在一个进攻占主导的世界里，在下个世纪的过程中——这个想法是AI正在让下个世纪会发生的进步在五到十年内发生——我们仍然需要同样的机制，或者权力平衡同样难以解决，即使人类是唯一的参与者。

**Extra example:**
- After the competitors closed, our company became **the only game in town** for tech support.  
  竞争对手关闭后，我们公司成了技术支持领域唯一的选择。

### whac-a-moled away
**Type:** idiom

**EN:** eliminated one by one only to reappear elsewhere, like the arcade game  
**CN:** 像打地鼠游戏一样被逐个消除但又在别处出现

**Literal:** 像打地鼠游戏那样被打掉  
**Figurative EN:** to be eliminated repeatedly in one place only to pop up in another, making complete elimination impossible  
**Figurative CN:** 在一处被消除后又在另一处出现，难以彻底根除

**Original examples:**
- [01:37:02] It seems easy to imagine worlds in which these get **whac-a-moled away** by different laws.  
  很容易想象这些好处会被不同的法律像打地鼠一样逐个消除。

**Extra example:**
- We tried to fix the security vulnerabilities, but new ones kept getting **whac-a-moled away** - as soon as we patched one, another appeared.  
  我们试图修复安全漏洞，但新漏洞不断像打地鼠一样冒出来——刚修好一个，另一个就出现了。

### step in
**Type:** phrasal_verb

**EN:** to intervene or become involved in a situation  
**CN:** 介入，插手（某事）

**Original examples:**
- [01:38:47] Now, I think the thing that we should do, the thing that I would support, is the federal government should **step in**, not saying 'states you can't regulate', but 'Here's what we're going to do, and states you can't differ from this.'  
  我认为我们应该做的，我会支持的，是联邦政府应该介入，不是说'各州不能监管'，而是'这是我们要做的，各州不能有不同做法'。

**Extra example:**
- When the negotiations broke down, the manager had to **step in** to resolve the conflict.  
  谈判破裂时，经理不得不介入解决冲突。

### age well
**Type:** collocation

**EN:** to remain relevant, acceptable, or valid over time  
**CN:** 经得起时间考验，不过时

**Original examples:**
- [01:39:22] I think it will not **age well**, it is already starting to not **age well** with all the backlash that you've seen.  
  我认为它经不起时间考验，从你看到的所有反弹来看，它已经开始站不住脚了。

**Extra example:**
- His controversial comments did not **age well** - what seemed edgy then now looks tone-deaf.  
  他那些有争议的评论经不起时间考验——当时看似前卫，现在看来却很不合时宜。

### ramp up
**Type:** phrasal_verb

**EN:** to increase or intensify something gradually  
**CN:** 逐步增加，提升（强度、数量等）

**Original examples:**
- [01:43:21] At the same time, I think we should be **ramping up** quite significantly the safety and security legislation.  
  同时，我认为我们应该大幅提升安全和保障立法的力度。

**Extra example:**
- The company plans to **ramp up** production to meet the holiday demand.  
  公司计划加大生产力度以满足假日需求。

### get left behind
**Type:** phrasal_verb

**EN:** to fail to keep up with others; to be excluded from progress  
**CN:** 被落下，掉队，被排除在进步之外

**Original examples:**
- [01:46:58] I worry more that those folks will **get left behind**.  
  我更担心那些人会被落下。

**Extra example:**
- Small businesses that don't adopt digital tools risk **getting left behind** in today's economy.  
  不采用数字工具的小企业在当今经济中有被落下的风险。

### rules of the road
**Type:** idiom

**EN:** the basic principles or guidelines that govern a particular activity or situation  
**CN:** 行为准则，基本规则

**Literal:** 道路规则（交通规则）  
**Figurative EN:** the established principles, norms, or guidelines that govern behavior in a particular context  
**Figurative CN:** 在特定情境下规范行为的既定原则、规范或指导方针

**Original examples:**
- [01:49:45] At some point, we're going to need to set up the **rules of the road**.  
  在某个时候，我们需要建立行为准则。
- [01:50:00] I'm not saying that one country, either the United States or a coalition of democracies—which I think would be a better setup, although it requires more international cooperation than we currently seem to want to make—should just say, 'These are the **rules of the road**.'  
  我并不是说美国或民主国家联盟（我认为这是更好的设置，尽管这需要比我们目前愿意做的更多国际合作）应该单方面说'这些就是行为准则'。
- [01:50:22] The world is going to have to grapple with this. What I would like is for the democratic nations of the world—those whose governments represent closer to pro-human values—are holding the stronger hand and have more leverage when the **rules of the road** are set.  
  世界将不得不应对这个问题。我希望的是，当行为准则被制定时，世界上的民主国家——那些政府更贴近以人为本价值观的国家——能够掌握更强的筹码和更大的影响力。
- [01:51:05] It seems like you're imagining a world in the future where the countries get together, and 'Here's the **rules of the road**, here's the leverage we have, and here's the leverage you have.'  
  似乎你在想象未来的一个世界，各国聚在一起，'这是行为准则，这是我们的筹码，这是你们的筹码'。

**Extra example:**
- Before joining the team, new employees need to understand the **rules of the road** for how we communicate and collaborate.  
  在加入团队之前，新员工需要了解我们沟通和协作的行为准则。

### hold the stronger hand
**Type:** idiom

**EN:** to have a better position or more advantages in a negotiation or competition  
**CN:** 处于更有利的位置，掌握更强的筹码

**Literal:** 握有更强的牌（扑克牌用语）  
**Figurative EN:** to be in a position of greater power, advantage, or leverage in a situation  
**Figurative CN:** 在某种情况下处于更有权力、优势或影响力的位置

**Original examples:**
- [01:50:22] What I would like is for the democratic nations of the world—those whose governments represent closer to pro-human values—are **holding the stronger hand** and have more leverage when the rules of the road are set.  
  我希望的是，当行为准则被制定时，世界上的民主国家——那些政府更贴近以人为本价值观的国家——能够掌握更强的筹码和更大的影响力。

**Extra example:**
- In the merger talks, the smaller company actually **held the stronger hand** because they owned the key patents.  
  在并购谈判中，小公司实际上掌握了更强的筹码，因为他们拥有关键专利。

### fulcrum moment
**Type:** collocation

**EN:** a critical turning point or pivotal moment that determines future direction  
**CN:** 关键转折点，支点时刻

**Original examples:**
- [01:50:47] I was re-listening to the interview from three years ago, and one of the ways it aged poorly is that I kept asking questions assuming there was going to be some key **fulcrum moment** two to three years from now.  
  我重新听了三年前的采访，它没能经受住时间考验的一个方面是，我一直在提问时假设两三年后会有某个关键转折点。

**Extra example:**
- The election represented a **fulcrum moment** in the country's transition to democracy.  
  这次选举是该国向民主过渡的关键转折点。

### privilege [something/someone]
**Type:** collocation

**EN:** to give advantage or favor to something or someone  
**CN:** 使某事物或某人处于有利地位

**Original examples:**
- [01:51:26] It seems like the internet **privileged** authoritarian countries more than you would've expected.  
  看起来互联网使威权国家比预期更占优势。

**Extra example:**
- This policy **privileges** corporations over individual consumers.  
  这项政策使企业比个人消费者更占优势。

### put [something] aside
**Type:** phrasal_verb

**EN:** to temporarily ignore or not consider something  
**CN:** 暂时搁置，暂不考虑

**Original examples:**
- [01:52:12] **Putting that aside**, I do think the exponential will continue.  
  把这个问题暂时搁置不谈，我确实认为指数增长会继续。

**Extra example:**
- Let's **put aside** our differences and focus on the common goal.  
  让我们暂时搁置分歧，专注于共同目标。

### take [something] for granted
**Type:** collocation

**EN:** to assume something will always be available or true without appreciating it  
**CN:** 认为某事理所当然，不加珍惜

**Original examples:**
- [01:52:38] That's an example of one thing we've **taken for granted**.  
  这是我们认为理所当然的一个例子。

**Extra example:**
- We often **take for granted** the clean water we have access to every day.  
  我们经常把每天都能用到的干净水视为理所当然。

### have a strong hand
**Type:** idiom

**EN:** to be in a powerful or advantageous negotiating position  
**CN:** 处于有利地位，握有好牌

**Literal:** 拥有一手好牌  
**Figurative EN:** to be in a position of strength or advantage in negotiations or competition  
**Figurative CN:** 在谈判或竞争中处于优势地位

**Original examples:**
- [01:54:05] My interest is in making that negotiation be one in which classical liberal democracy **has a strong hand**.  
  我的兴趣在于让那场谈判成为古典自由民主占据有利地位的谈判。

**Extra example:**
- With our technological advantage, we **have a strong hand** in these trade negotiations.  
  凭借我们的技术优势，我们在这些贸易谈判中握有好牌。

### self-fulfilling cycle
**Type:** collocation

**EN:** a situation where predictions or beliefs cause themselves to become true through feedback loops  
**CN:** 自我实现的循环

**Original examples:**
- [01:55:13] authoritarian countries with AI are these **self-fulfilling cycles** that are very hard to displace  
  拥有人工智能的威权国家是这些很难被取代的自我实现循环

**Extra example:**
- Poverty can create a **self-fulfilling cycle** where lack of education leads to fewer opportunities.  
  贫困会造成一个自我实现的循环，教育缺乏导致机会更少。

### go either way
**Type:** idiom

**EN:** to have two possible outcomes; could result in either of two directions  
**CN:** 可能朝两个方向发展，结果不确定

**Literal:** 朝任一方向走  
**Figurative EN:** could result in either of two different outcomes; the situation is uncertain  
**Figurative CN:** 可能产生两种不同结果之一；局势不确定

**Original examples:**
- [01:57:26] Right, it **could go either way**.  
  是的，结果可能朝两个方向发展。

**Extra example:**
- The election is so close, it **could go either way**.  
  选举非常胶着，可能朝任何一个方向发展。

### come out one way or another
**Type:** idiom

**EN:** to make a decision or reach a conclusion between alternatives  
**CN:** 做出某种决定，得出某种结论

**Literal:** 以某种方式出来  
**Figurative EN:** to eventually make a choice or reach a decision between different options  
**Figurative CN:** 最终在不同选项中做出选择或达成决定

**Original examples:**
- [01:59:07] We've had to **come out one way or another** on it through history.  
  在历史上我们不得不在这个问题上做出某种决定。

**Extra example:**
- The committee will have to **come out one way or another** on this proposal by Friday.  
  委员会必须在周五前对这个提案做出决定。

### net out to
**Type:** phrasal_verb

**EN:** to result in or lead to a particular conclusion after considering everything  
**CN:** 最终归结为，净结果是

**Original examples:**
- [02:02:46] But I guess that **nets out to** today, as you say, that we will not sell data centers  
  但我想这最终归结为今天的情况，就像你说的，我们不会出售数据中心

**Extra example:**
- After all the expenses, the event **netted out to** a small profit.  
  扣除所有费用后，这次活动的净结果是小额盈利。

### go off
**Type:** phrasal_verb

**EN:** to leave and do something independently or without supervision  
**CN:** 离开去独自做某事，自行行动

**Original examples:**
- [02:07:51] How much should the model be a kind of 'skin suit' where it just directly follows the instructions given to it by whoever is giving those instructions, versus how much should the model have an inherent set of values and **go off** and do things on its own?  
  模型应该在多大程度上成为一种"皮囊"，直接遵循给它指令的人的指示，相对于模型应该在多大程度上拥有固有的价值观并自行行动？
- [02:08:24] We're not trying to build something that **goes off** and runs the world on its own.  
  我们不是要构建一个自行运作并统治世界的东西。

**Extra example:**
- Don't just **go off** and make decisions without consulting the team.  
  不要不咨询团队就自行做决定。

### set in stone
**Type:** idiom

**EN:** fixed and unchangeable  
**CN:** 固定不变的，板上钉钉的

**Literal:** 刻在石头上  
**Figurative EN:** permanently established and unable to be changed  
**Figurative CN:** 永久确立且无法改变

**Original examples:**
- [02:09:25] Normally, a constitution is written down, **set in stone**, and there's a process of updating it and changing it and so forth.  
  通常情况下，宪法被写下来，固定不变，然后有一个更新和修改的过程等等。

**Extra example:**
- The plan isn't **set in stone** yet - we can still make changes.  
  这个计划还没有板上钉钉——我们仍然可以做改变。

### feel out
**Type:** phrasal_verb

**EN:** to try to discover someone's opinions or intentions by careful or indirect questioning  
**CN:** 试探，摸清（某人的想法或意图）

**Original examples:**
- [02:12:42] There's not this vague sense in which the Supreme Court will **feel out** how people are feeling—what are the vibes—and update the constitution accordingly.  
  不存在这样一种模糊的感觉，即最高法院会试探人们的感受——氛围如何——然后相应地更新宪法。

**Extra example:**
- Let me **feel out** the boss before we make the formal proposal.  
  在我们正式提案之前，让我先试探一下老板的意思。

### get ahead
**Type:** phrasal_verb

**EN:** to become successful or advance in position, often at others' expense  
**CN:** 成功，出人头地，向前发展

**Original examples:**
- [02:18:23] That we're a team, that people aren't trying to **get ahead** at each other's expense or backstab each other, which again, I think happens a lot at some of the other places.  
  我们是一个团队，人们不会试图以牺牲彼此为代价来出人头地或背后捅刀子，而这在其他一些地方经常发生。

**Extra example:**
- She's willing to work hard to **get ahead** in her career.  
  她愿意努力工作以在职业生涯中取得成功。

### call things what they are
**Type:** collocation

**EN:** to describe situations honestly and directly without euphemism  
**CN:** 实话实说，直言不讳地描述事物

**Original examples:**
- [02:20:56] The point is to get a reputation of telling the company the truth about what's happening, to **call things what they are**, to acknowledge problems, to avoid the sort of corpo speak, the kind of defensive communication that often is necessary in public because the world is very large and full of people who are interpreting things in bad faith.  
  重点是要在公司中建立说真话的声誉，实话实说，承认问题，避免那种公司式的说辞，那种防御性的沟通方式，这在公开场合往往是必要的，因为世界很大，充满了恶意解读的人。

**Extra example:**
- We need to **call things what they are** - this project is failing and we need to pivot.  
  我们需要实话实说——这个项目正在失败，我们需要转向。

### on the same page
**Type:** idiom

**EN:** in agreement or having the same understanding  
**CN:** 意见一致，看法相同

**Literal:** 在同一页上  
**Figurative EN:** to share the same understanding or agreement about something  
**Figurative CN:** 对某事有相同的理解或共识

**Original examples:**
- [02:21:31] It makes it a better place to work, it makes people more than the sum of their parts, and increases the likelihood that we accomplish the mission because everyone is **on the same page** about the mission, and everyone is debating and discussing how best to accomplish the mission.  
  这让它成为一个更好的工作场所，让人们的能力超过各部分的总和，并增加了我们完成使命的可能性，因为每个人对使命的理解都是一致的，每个人都在辩论和讨论如何最好地完成使命。

**Extra example:**
- Before we start, let's make sure we're all **on the same page** about the project goals.  
  在我们开始之前，让我们确保大家对项目目标的理解是一致的。

### in lieu of
**Type:** collocation

**EN:** instead of, in place of  
**CN:** 代替，取代

**Original examples:**
- [2:21:46] Well, **in lieu of** an external Dario Vision Quest, we have this interview.  
  好吧，**代替**外部的 Dario 愿景探索，我们有这次采访。

**Extra example:**
- The company offered stock options **in lieu of** a salary increase.  
  公司提供股票期权来**代替**加薪。

---

## Complex Sentences

### [00:23]
**Original:** I don't know that I would have predicted the specific direction of code. But when I look at the exponential, it is roughly what I expected in terms of the march of the models from smart high school student to smart college student to beginning to do PhD and professional stuff, and in the case of code reaching beyond that.

**Translation:** 我不知道我是否能预测代码的具体方向。但当我看指数增长时，就模型从聪明的高中生到聪明的大学生再到开始做博士和专业工作，以及在代码方面超越这一点的进展而言，它大致符合我的预期。

**Core structure:**
- It is roughly what I expected in terms of the march of the models.  
  就模型的进展而言，它大致符合我的预期。

**Structure tree:**
```
main clause: it is roughly what I expected
prepositional phrase: in terms of the march...
parallel progression: from X to Y to Z
final extension: and in the case of code reaching beyond that
```

**Grammar points:**
- **in terms of** - 表示"就...而言"，引出评价角度
- **平行结构 from...to...to...** - 描述渐进式发展过程
- **现在分词短语** - reaching beyond that 补充说明 code 的情况

### [01:02]
**Original:** To me, it is absolutely wild that you have people — within the bubble and outside the bubble — talking about the same tired, old hot-button political issues, when we are near the end of the exponential.

**Translation:** 对我来说，当我们接近指数增长的尾声时，无论是圈内还是圈外的人，仍在谈论同样陈旧的热点政治议题，这简直令人难以置信。

**Core structure:**
- It is wild that people are talking about political issues when we are near the end.  
  当我们接近尾声时，人们还在谈论政治议题，这很疯狂。

**Structure tree:**
```
主句: it is absolutely wild that...
that从句: you have people talking about issues
插入语: within the bubble and outside the bubble
时间状语从句: when we are near the end...
```

**Grammar points:**
- **形式主语 it** - 真正主语是 that 从句
- **破折号插入语** - within the bubble and outside the bubble 补充说明 people 的范围
- **have sb doing sth** - 表示"有人正在做某事"的状态

### [01:31]
**Original:** At least from the public's point of view, three years ago there were well-known public trends across many orders of magnitude of compute where you could see how the loss improves.

**Translation:** 至少从公众的角度来看，三年前有一些众所周知的公开趋势，跨越多个数量级的计算量，你可以看到损失是如何改善的。

**Core structure:**
- There were public trends where you could see how the loss improves.  
  有一些公开趋势，你可以看到损失如何改善。

**Structure tree:**
```
介词短语状语: from the public's point of view
时间状语: three years ago
主句: there were trends
定语: across many orders of magnitude
定语从句: where you could see...
宾语从句: how the loss improves
```

**Grammar points:**
- **where 引导定语从句** - 修饰 trends，表示抽象地点
- **how 引导宾语从句** - see 的宾语，表示"如何、怎样"

### [04:55]
**Original:** Even other companies have published things in some of their releases that say, 'We train the model on math contests — AIME or other things — and how well the model does is log-linear in how long we've trained it.'

**Translation:** 甚至其他公司也在他们的一些发布中公布了一些内容，说'我们在数学竞赛（AIME 或其他）上训练模型，模型表现的好坏与我们训练时长呈对数线性关系。'

**Core structure:**
- Companies have published things that say how well the model does is log-linear in how long we've trained it.  
  公司发布了内容，说明模型表现与训练时长呈对数线性关系。

**Structure tree:**
```
主句: companies have published things
定语从句: that say...
引语主句: how well the model does is log-linear
介词宾语从句: in how long we've trained it
插入说明: AIME or other things
```

**Grammar points:**
- **how well/how long 引导名词性从句** - how well 作主语，how long 作介词 in 的宾语
- **be log-linear in** - 专业表达，表示"与...呈对数线性关系"

### [05:31]
**Original:** Something which possesses the true core of human learning would not require all these billions of dollars of data and compute and these bespoke environments, to learn how to use Excel, how to use PowerPoint, how to navigate a web browser.

**Translation:** 真正拥有人类学习核心的东西，不会需要所有这些数十亿美元的数据、计算和这些定制环境，来学习如何使用 Excel、如何使用 PowerPoint、如何操作网页浏览器。

**Core structure:**
- Something would not require billions of dollars to learn how to use Excel.  
  某物不需要数十亿美元来学习如何使用 Excel。

**Structure tree:**
```
主语: Something (定语从句修饰)
定语从句: which possesses the true core
谓语: would not require
宾语列举: billions of dollars of data and compute and environments
目的: to learn...
并列宾语从句: how to use Excel, how to use PowerPoint, how to navigate...
```

**Grammar points:**
- **which 引导定语从句** - 修饰 Something，限定其特征
- **多重 how to 不定式** - 并列结构作 learn 的宾语
- **would not 虚拟语气** - 表示与事实相反的假设

### [08:01]
**Original:** It was only when you trained over all the tasks on the internet — when you did a general internet scrape from something like Common Crawl or scraping links in Reddit, which is what we did for GPT-2 — that you started to get generalization.

**Translation:** 只有当你在互联网上的所有任务上进行训练时——当你从类似Common Crawl这样的网站进行通用互联网抓取，或者抓取Reddit中的链接（这就是我们为GPT-2所做的）时——你才开始获得泛化能力。

**Core structure:**
- It was only when you trained over all tasks that you started to get generalization.  
  只有当你在所有任务上训练时，你才开始获得泛化能力。

**Structure tree:**
```
main: It was only when... that... (强调句)
temporal clause: when you trained over all tasks
parenthetical insertion: when you did a general scrape...
relative clause: which is what we did for GPT-2
result clause: that you started to get generalization
```

**Grammar points:**
- **It was only when... that 强调句** - 强调时间状语从句，表示'只有在...时候才...'
- **破折号插入语** - 中间插入的when从句补充说明训练方式，增加理解难度
- **非限制性定语从句** - which指代前面整个短语，补充背景信息

### [09:06]
**Original:** But we also see that once they're trained, if we give them a long context length of a million — the only thing blocking long context is inference — they're very good at learning and adapting within that context.

**Translation:** 但我们也看到，一旦它们被训练好，如果我们给它们一百万的长上下文长度——唯一阻碍长上下文的是推理——它们非常擅长在该上下文中学习和适应。

**Core structure:**
- We see that they're very good at learning within that context.  
  我们看到它们非常擅长在该上下文中学习。

**Structure tree:**
```
main: we see that...
object clause: that they're very good at...
condition 1: once they're trained
condition 2: if we give them a long context
parenthetical: the only thing blocking long context is inference
```

**Grammar points:**
- **多重条件从句嵌套** - once和if两个条件从句叠加，增加句子复杂度
- **破折号插入独立句子** - 中间插入完整句子作为补充说明，打断主句流畅性

### [09:24]
**Original:** I think there's something going on where pre-training is not like the process of humans learning, but it's somewhere between the process of humans learning and the process of human evolution.

**Translation:** 我认为正在发生的事情是，预训练不像人类学习的过程，而是介于人类学习过程和人类进化过程之间的某个地方。

**Core structure:**
- Pre-training is somewhere between human learning and human evolution.  
  预训练介于人类学习和人类进化之间。

**Structure tree:**
```
main: I think there's something going on
relative clause: where pre-training is not like...
contrast structure: not like... but... between... and...
```

**Grammar points:**
- **where引导定语从句修饰something** - where在此表示抽象地点，相当于'in which situation'
- **not... but... between A and B** - 否定+转折+比较结构的三重组合，逻辑层次复杂

### [09:45]
**Original:** They literally start as random weights, whereas the human brain starts with all these regions connected to all these inputs and outputs.

**Translation:** 它们字面上是从随机权重开始的，而人类大脑则是从所有这些区域连接到所有这些输入和输出开始的。

**Core structure:**
- They start as random weights, whereas the human brain starts with connected regions.  
  它们从随机权重开始，而人脑从连接的区域开始。

**Structure tree:**
```
clause 1: They start as random weights
contrast marker: whereas
clause 2: the human brain starts with...
modifier: connected to all these inputs and outputs
```

**Grammar points:**
- **whereas对比连词** - whereas连接两个对比句，强调AI和人脑的起点差异
- **过去分词作后置定语** - connected修饰regions，表示'被连接的区域'

### [13:51]
**Original:** On the basic hypothesis of, as you put it, within ten years we'll get to what I call a 'country of geniuses in a data center', I'm at 90% on that.

**Translation:** 关于这个基本假设，正如你所说的，十年内我们将达到我所谓的'数据中心里的天才之国'，我对此有90%的把握。

**Core structure:**
- On the hypothesis, I'm at 90% on that.  
  关于这个假设，我有90%的把握。

**Structure tree:**
```
prepositional phrase: On the basic hypothesis of...
parenthetical: as you put it
noun clause: what I call...
main clause: I'm at 90% on that
```

**Grammar points:**
- **介词短语前置 + 插入语** - as you put it插入打断了介词短语，增加理解难度
- **what引导名词性从句** - what作call的宾语，相当于'the thing that I call'

### [14:58]
**Original:** My one little bit of fundamental uncertainty, even on long timescales, is about tasks that aren't verifiable: planning a mission to Mars; doing some fundamental scientific discovery like CRISPR; writing a novel.

**Translation:** 我唯一的一点根本性不确定,即使在长时间尺度上,是关于那些无法验证的任务:规划火星任务;做一些像CRISPR那样的基础科学发现;写小说。

**Core structure:**
- My uncertainty is about tasks that aren't verifiable.  
  我的不确定是关于无法验证的任务。

**Structure tree:**
```
main clause: My uncertainty is about tasks
subject: My one little bit of fundamental uncertainty
parenthetical: even on long timescales
relative clause: that aren't verifiable
appositive examples: planning... doing... writing...
```

**Grammar points:**
- **插入语** - even on long timescales 插入主语和谓语之间,打断句子自然流
- **冒号引出同位语** - 冒号后列举具体例子,解释抽象概念 tasks

### [16:21]
**Original:** That doesn't seem like how humans get better. The world in which we don't get there is the world in which we do all the verifiable things.

**Translation:** 这似乎不像人类变得更好的方式。我们无法达到目标的世界,是我们完成所有可验证任务的世界。

**Core structure:**
- The world is the world in which we do all verifiable things.  
  这个世界就是我们完成所有可验证任务的世界。

**Structure tree:**
```
main clause: The world is the world
relative clause 1: in which we don't get there
relative clause 2: in which we do all verifiable things
parallel structure: world... is the world...
```

**Grammar points:**
- **in which 定语从句嵌套** - 两个 in which 从句修饰不同的 world,造成理解层次复杂
- **抽象概念重复** - world 重复使用指代不同假设场景,需要理解上下文

### [18:32]
**Original:** The spectrum is: 90% of code is written by the model, 100% of code is written by the model.

**Translation:** 这个范围是:90%的代码由模型编写,100%的代码由模型编写。

**Core structure:**
- The spectrum is: 90% versus 100%.  
  范围是:90%对比100%。

**Structure tree:**
```
main clause: The spectrum is
colon introduces: two parallel clauses
clause 1: 90% of code is written by the model
clause 2: 100% of code is written by the model
```

**Grammar points:**
- **冒号引出并列结构** - 冒号后两个完整句子并列,对比细微差异
- **被动语态重复** - is written by 重复使用,听力中容易混淆两个百分比

### [18:41]
**Original:** 90% of the end-to-end SWE tasks — including things like compiling, setting up clusters and environments, testing features, writing memos — are done by the models.

**Translation:** 90%的端到端软件工程任务——包括编译、设置集群和环境、测试功能、写备忘录等事情——由模型完成。

**Core structure:**
- 90% of SWE tasks are done by the models.  
  90%的软件工程任务由模型完成。

**Structure tree:**
```
main clause: 90% of tasks are done by models
subject: 90% of the end-to-end SWE tasks
parenthetical list: including things like...
verb phrase: are done by the models
```

**Grammar points:**
- **破折号插入长列表** - 破折号内插入多个动名词短语,打断主干,听力中难以追踪主谓
- **主谓分离** - 主语(tasks)和谓语(are done)被长插入语隔开约15个词

### [19:45]
**Original:** But what I notice is that even in greenfield projects people start with Claude Code or something, people report starting a lot of projects… Do we see in the world out there a renaissance of software, all these new features that wouldn't exist otherwise?

**Translation:** 但我注意到的是,即使在全新项目中,人们用Claude Code之类的工具开始,人们报告启动很多项目……我们在外面的世界中看到软件的复兴吗,所有这些原本不会存在的新功能?

**Core structure:**
- What I notice is... Do we see a renaissance of software?  
  我注意到的是……我们看到软件的复兴吗?

**Structure tree:**
```
statement: What I notice is that...
clause 1: even in greenfield projects...
clause 2: people report starting...
question: Do we see... a renaissance
relative clause: that wouldn't exist otherwise
```

**Grammar points:**
- **陈述句转疑问句** - 从陈述(What I notice)突然转为疑问句,且用省略号连接,听力中难以捕捉转折
- **虚拟语气定语从句** - wouldn't exist otherwise 表示反事实假设,需理解条件关系

### [20:52]
**Original:** Economic diffusion has become one of these buzzwords that's a reason why we're not going to make AI progress, or why AI progress doesn't matter.

**Translation:** 经济扩散已经成为这些流行词之一，它被用作我们不会取得AI进步的理由，或者AI进步无关紧要的理由。

**Core structure:**
- Economic diffusion has become a buzzword that's a reason.  
  经济扩散已经成为一个流行词，它是一个理由。

**Structure tree:**
```
main clause: Economic diffusion has become buzzwords
relative clause: that's a reason
adverbial clause 1: why we're not going to make progress
adverbial clause 2: or why progress doesn't matter
```

**Grammar points:**
- **定语从句嵌套原因状语从句** - that 引导定语从句，内部包含两个并列的 why 原因状语从句
- **or 连接并列从句** - 两个 why 从句表达不同但相关的理由

### [22:39]
**Original:** Because it's fiddly: "I have to do change management within my enterprise... I set this up, but I have to change the security permissions on this in order to make it actually work... I had this old piece of software that checks the model before it's compiled and released and I have to rewrite it."

**Translation:** 因为它很繁琐："我必须在企业内部进行变更管理...我设置了这个，但我必须改变安全权限才能让它真正运行...我有这个旧软件，它在模型编译和发布之前检查模型，我必须重写它。"

**Core structure:**
- It's fiddly because you have to do change management, change permissions, and rewrite software.  
  它很繁琐，因为你必须进行变更管理、更改权限和重写软件。

**Structure tree:**
```
causal structure: Because it's fiddly
example 1: I have to do change management
example 2: I set this up, but I have to change permissions
  purpose clause: in order to make it work
example 3: I had software that checks... and I have to rewrite it
```

**Grammar points:**
- **冒号引出具体例证** - 用一系列实际场景说明 fiddly 的含义
- **in order to 表目的** - 说明改变权限的目的
- **定语从句修饰 software** - that checks... 描述软件的功能

### [23:10]
**Original:** So I think everything we've seen so far is compatible with the idea that there's one fast exponential that's the capability of the model. Then there's another fast exponential that's downstream of that, which is the diffusion of the model into the economy.

**Translation:** 所以我认为，到目前为止我们看到的一切都与这样一个想法相符：有一个快速指数增长，那是模型的能力。然后还有另一个快速指数增长，它是前者的下游，也就是模型在经济中的扩散。

**Core structure:**
- Everything is compatible with the idea that there's one exponential and another exponential.  
  一切都与这个想法相符：有一个指数增长和另一个指数增长。

**Structure tree:**
```
main clause: everything is compatible with the idea
that-clause 1: there's one fast exponential
  relative clause: that's the capability
that-clause 2: there's another exponential
  relative clause: that's downstream of that
  non-restrictive clause: which is the diffusion
```

**Grammar points:**
- **同位语从句** - that 从句解释 the idea 的具体内容
- **多层定语从句嵌套** - 多个 that/which 从句层层修饰，描述两个指数增长的关系

### [24:20]
**Original:** People hire humans all the time. We pay humans upwards of $50 trillion in wages because they're useful, even though in principle it would be much easier to integrate AIs into the economy than it is to hire humans.

**Translation:** 人们一直在雇佣人类。我们支付给人类超过50万亿美元的工资，因为他们有用，尽管原则上将AI整合到经济中会比雇佣人类容易得多。

**Core structure:**
- We pay humans wages because they're useful, even though it would be easier to integrate AIs.  
  我们支付人类工资因为他们有用，尽管整合AI会更容易。

**Structure tree:**
```
main clause: We pay humans wages
reason clause: because they're useful
concession clause: even though it would be easier...
  comparison structure: easier to integrate AIs than to hire humans
```

**Grammar points:**
- **even though 让步状语从句** - 表达与主句形成对比的让步关系
- **比较结构 easier...than** - it 作形式主语，真正主语是 to integrate AIs；than 后省略了相同的形式主语结构

### [25:46]
**Original:** Any given feature or any given product, like Claude Code or Cowork, will get adopted by the individual developers who are on Twitter all the time, by the Series A startups, many months faster than they will get adopted by a large enterprise that does food sales.

**Translation:** 任何特定功能或产品，比如Claude Code或Cowork，都会被那些一直在Twitter上的独立开发者、A轮创业公司采用，比被从事食品销售的大企业采用要快好几个月。

**Core structure:**
- Any feature will get adopted by developers and startups faster than by large enterprises.  
  任何功能都会被开发者和创业公司采用得比大企业快。

**Structure tree:**
```
subject: Any given feature or product
passive verb: will get adopted
agent 1: by developers (who are on Twitter)
agent 2: by Series A startups
comparison: many months faster than...
agent 3: by large enterprise (that does food sales)
```

**Grammar points:**
- **被动语态 get adopted** - 强调功能被采用的过程
- **比较级 + than 结构** - many months 作副词修饰 faster，比较不同类型用户的采用速度
- **定语从句修饰并列主体** - who/that 从句分别限定 developers 和 enterprise

### [27:22]
**Original:** It will be a compelling product enough maybe to get 3-5x, or 10x, a year of growth, even when you're in the hundreds of billions of dollars, which is extremely hard to do and has never been done in history before, but not infinitely fast.

**Translation:** 它将是一个足够有吸引力的产品,也许能实现每年3到5倍,或10倍的增长,即使当你已经达到数千亿美元规模时也是如此——这是极其难做到的,而且历史上从未有过——但不会是无限快速的增长。

**Core structure:**
- It will be a compelling product to get growth, but not infinitely fast.  
  它将是一个有吸引力的产品来实现增长,但不会无限快。

**Structure tree:**
```
main clause: It will be a compelling product
modifier: enough to get 3-5x growth
concessive clause: even when you're in hundreds of billions
relative clause: which is extremely hard...
contrast clause: but not infinitely fast
```

**Grammar points:**
- **enough to do 结构** - 表示'足够...以至于能够做某事'
- **even when 让步状语从句** - 强调即使在极端条件下也成立
- **非限制性定语从句** - which 引导,补充说明前面整个情况

### [31:43]
**Original:** It'll be able to also use the computer screen to go on the web, look at all your previous interviews, look at what people are saying on Twitter in response to your interviews, talk to you, ask you questions, talk to your staff, look at the history of edits.

**Translation:** 它还能够使用电脑屏幕上网,查看你所有以前的采访,看看人们在推特上对你采访的回应,与你交谈,问你问题,与你的员工交谈,查看编辑历史记录。

**Core structure:**
- It'll be able to use the computer screen to do multiple tasks.  
  它将能够使用电脑屏幕来完成多项任务。

**Structure tree:**
```
main clause: It'll be able to use the screen
purpose: to go on the web
parallel infinitives: look at..., look at..., talk to..., ask..., talk to..., look at...
nested clause: what people are saying (宾语从句)
```

**Grammar points:**
- **be able to 结构** - 表示能力
- **并列不定式省略 to** - 多个并列动作共用一个 to,后续省略
- **what 引导宾语从句** - what people are saying 作 look at 的宾语

### [32:06]
**Original:** I think this is one of the things that's actually blocking deployment: getting to the point on computer use where the models are really masters at using the computer.

**Translation:** 我认为这是实际阻碍部署的事情之一:在计算机使用方面达到模型真正精通使用计算机的程度。

**Core structure:**
- This is one of the things blocking deployment: getting to the point where models are masters.  
  这是阻碍部署的事情之一:达到模型精通的程度。

**Structure tree:**
```
main clause: This is one of the things
relative clause: that's blocking deployment
appositive: getting to the point...
relative clause: where models are masters
```

**Grammar points:**
- **定语从句 that's blocking** - 修饰 things
- **冒号后的同位语** - 动名词短语解释前面的 things
- **where 引导定语从句** - 修饰抽象名词 point,表示'在这个程度上'

### [33:03]
**Original:** If it's something like 'identify what the best clips would be in this transcript', maybe the LLMs do a seven-out-of-ten job on them. But there's not this ongoing way I can engage with them to help them get better at the job the way I could with a human employee.

**Translation:** 如果是像'识别这个文字稿中最好的片段会是什么'这样的事情,也许大语言模型能做到七成的水平。但我无法像对待人类员工那样,以持续的方式与它们互动来帮助它们在工作上变得更好。

**Core structure:**
- LLMs do a 7/10 job. But there's not a way I can engage with them the way I could with a human.  
  大语言模型能做到7成。但我无法像对待人类那样与它们互动。

**Structure tree:**
```
conditional: If it's something like...
main clause: LLMs do a 7/10 job
contrast: But there's not this way
relative clause: I can engage with them
purpose: to help them get better
comparison: the way I could with a human
```

**Grammar points:**
- **what 引导宾语从句** - identify 的宾语,what 在从句中作主语
- **the way 引导方式状语从句** - 表示'以...的方式',way 后省略了 that/in which
- **不定式作目的状语** - to help them get better 说明互动的目的

### [34:37]
**Original:** But when you say that, what you're implying is that by reading the codebase into the context, I have everything that the human needed to learn on the job.

**Translation:** 但当你这么说的时候,你暗示的是,通过将代码库读入上下文,我就拥有了人类需要在工作中学习的一切。

**Core structure:**
- What you're implying is that I have everything.  
  你暗示的是我拥有一切。

**Structure tree:**
```
main clause: what you're implying is that...
subject clause: what you're implying
predicative clause: that I have everything...
modifier: by reading the codebase (方式状语)
modifier: that the human needed to learn (定语从句修饰everything)
```

**Grammar points:**
- **What 引导主语从句** - What you're implying 作整句主语
- **嵌套 that 从句** - 表语从句内包含定语从句,层次复杂
- **介词短语作方式状语** - by reading... 说明获取方式

### [35:16]
**Original:** I'm sure you saw last year, there was a major study where they had experienced developers try to close pull requests in repositories that they were familiar with.

**Translation:** 我相信你去年看到过,有一项重要研究,他们让有经验的开发者尝试关闭他们熟悉的代码库中的拉取请求。

**Core structure:**
- There was a study where they had developers try to close pull requests.  
  有一项研究,他们让开发者尝试关闭拉取请求。

**Structure tree:**
```
main clause: there was a major study
relative clause: where they had developers try...
causative structure: had developers try to close...
relative clause: that they were familiar with (修饰repositories)
```

**Grammar points:**
- **插入语** - I'm sure you saw last year 插入打断主句
- **使役动词 have** - have sb do sth 结构
- **多层定语从句嵌套** - where 从句修饰 study, that 从句修饰 repositories

### [35:37]
**Original:** So I'm trying to square the qualitative feeling that people feel with these models versus, one, on a macro level, where is this renaissance of software?

**Translation:** 所以我试图让人们对这些模型的主观感受与以下两点相符:第一,在宏观层面上,软件的复兴在哪里?

**Core structure:**
- I'm trying to square the feeling versus where is the renaissance.  
  我试图让感受与复兴在哪里这个问题相符。

**Structure tree:**
```
main clause: I'm trying to square A versus B
A: the qualitative feeling (that从句修饰)
B: where is this renaissance (疑问句嵌入)
modifier: on a macro level (插入成分)
```

**Grammar points:**
- **square A with/versus B** - 使A与B一致、吻合
- **嵌入式疑问句** - where is... 作为比较对象保持疑问语序
- **插入成分** - one, on a macro level 打断句子流畅性

### [37:04]
**Original:** But the idea was supposed to be that with recursive self-improvement, you make a better AI, the AI helps you build a better next AI, et cetera, et cetera.

**Translation:** 但这个想法本应是,通过递归自我改进,你制造出更好的AI,这个AI帮助你构建更好的下一代AI,如此循环往复。

**Core structure:**
- The idea was that you make a better AI, the AI helps you build a better next AI.  
  这个想法是你制造更好的AI,这个AI帮助你构建更好的下一代AI。

**Structure tree:**
```
main clause: the idea was supposed to be that...
that从句: you make AI, the AI helps you...
modifier: with recursive self-improvement
parallel structure: make→helps→(implied continues)
```

**Grammar points:**
- **be supposed to** - 表示理应如此但实际未必
- **逗号连接并列句** - 无连词用逗号连接完整句子,口语化
- **et cetera 重复** - 强调循环持续的概念

### [39:29]
**Original:** It seems like the point you were making on the coding thing is that we actually don't need on-the-job learning.

**Translation:** 你在编程这件事上想表达的观点似乎是,我们实际上不需要在职学习。

**Core structure:**
- The point is that we don't need on-the-job learning.  
  这个观点是我们不需要在职学习。

**Structure tree:**
```
main clause: the point is that...
subject: the point (that从句修饰)
that从句: we don't need on-the-job learning
modifier: you were making (定语从句)
modifier: on the coding thing (介词短语)
```

**Grammar points:**
- **it seems like** - 表示推测,弱化断言语气
- **定语从句修饰 point** - you were making 描述观点来源
- **表语从句** - that 引导表语从句说明观点内容

### [40:45]
**Original:** Just like with pre-training, just how the models know more, if I look at a pre-trained model, it knows more about the history of samurai in Japan than I do.

**Translation:** 就像预训练一样,就像模型知道得更多一样,如果我看一个预训练模型,它对日本武士历史的了解比我多。

**Core structure:**
- If I look at a pre-trained model, it knows more than I do.  
  如果我看一个预训练模型,它知道得比我多。

**Structure tree:**
```
parallel introductory phrases: Just like... just how...
conditional clause: if I look at a pre-trained model
main clause: it knows more... than I do
comparison structure: more... than
```

**Grammar points:**
- **并列的引导短语** - Just like和just how两个相似结构并列,增加理解难度
- **条件从句插入** - if从句打断了引导短语和主句的连接

### [41:42]
**Original:** If you think about the model reading a million words, how long would it take me to read a million?

**Translation:** 如果你想想模型读一百万个单词,我读一百万个单词需要多长时间?

**Core structure:**
- How long would it take me to read a million?  
  我读一百万个需要多长时间?

**Structure tree:**
```
conditional clause: If you think about...
gerund phrase: the model reading a million words
main question: how long would it take me...
ellipsis: a million (words)
```

**Grammar points:**
- **省略结构** - a million后省略了words,听者需要从上下文补充
- **虚拟语气疑问句** - would it take表假设性问题

### [42:36]
**Original:** The trillions of dollars a year market, maybe all of the national security implications and the safety implications that I wrote about in 'Adolescence of Technology' can happen without it.

**Translation:** 数万亿美元的年度市场,也许我在《技术的青春期》中写到的所有国家安全影响和安全影响,都可以在没有它的情况下发生。

**Core structure:**
- The market and implications can happen without it.  
  市场和影响可以在没有它的情况下发生。

**Structure tree:**
```
compound subject: The market, implications and implications
modifier: trillions of dollars a year
relative clause: that I wrote about in...
modal verb phrase: can happen without it
```

**Grammar points:**
- **复杂并列主语** - 三个名词短语并列,中间穿插多个修饰成分
- **定语从句修饰多个先行词** - that从句修饰前面的两个implications

### [43:37]
**Original:** When context lengths get much longer than that, people report qualitative degradation in the ability of the model to consider that full context.

**Translation:** 当上下文长度变得比那个长得多时,人们报告说模型考虑完整上下文的能力出现了质量下降。

**Core structure:**
- When context lengths get longer, people report degradation.  
  当上下文长度变长时,人们报告说出现下降。

**Structure tree:**
```
temporal clause: When context lengths get much longer
main clause: people report degradation
noun phrase: degradation in the ability
infinitive phrase: to consider that full context
```

**Grammar points:**
- **多层修饰的名词短语** - degradation in the ability of the model to consider... 三层嵌套修饰
- **不定式作定语** - to consider修饰ability,表示能力的具体内容

### [45:01]
**Original:** Wouldn't you expect that if you had to train on longer context length, that would mean that you're able to get less samples in for the same amount of compute?

**Translation:** 难道你不会认为,如果你必须在更长的上下文长度上训练,那意味着在相同的计算量下你能获得更少的样本吗?

**Core structure:**
- Wouldn't you expect that that would mean you get less samples?  
  难道你不会认为那意味着你获得更少样本吗?

**Structure tree:**
```
negative question: Wouldn't you expect that...
conditional clause: if you had to train on...
predicative clause: that would mean that...
nested clause: that you're able to get less samples
```

**Grammar points:**
- **三层从句嵌套** - expect that... if..., that... that... 多层嵌套增加理解难度
- **否定疑问句** - Wouldn't you expect表示反问,期待肯定回答
- **双重that从句** - 两个that从句连用,第一个作expect宾语,第二个作mean宾语

### [46:23]
**Original:** And then, generally, Anthropic has predicted that by late '26 or early '27 we will have AI systems that "have the ability to navigate interfaces available to humans doing digital work today, intellectual capabilities matching or exceeding that of Nobel Prize winners, and the ability to interface with the physical world."

**Translation:** 然后，总体而言，Anthropic 预测到 26 年末或 27 年初，我们将拥有这样的 AI 系统：「能够操作当今从事数字工作的人类可用的界面，拥有与诺贝尔奖得主相当或超越的智力能力，以及与物理世界交互的能力。」

**Core structure:**
- Anthropic has predicted that we will have AI systems.  
  Anthropic 预测我们将拥有 AI 系统。

**Structure tree:**
```
main: Anthropic has predicted that...
that-clause: we will have AI systems
relative clause: that have the ability...
parallel structure: ability to navigate / intellectual capabilities / ability to interface
```

**Grammar points:**
- **宾语从句 + 定语从句嵌套** - that 从句作宾语，内部又包含 that 引导的定语从句修饰 AI systems
- **三重并列结构** - 三个名词短语用 and 连接，描述 AI 系统的能力

### [47:00]
**Original:** The TAM of a Nobel Prize winner, that can actually do everything a Nobel Prize winner can do, is trillions of dollars.

**Translation:** 一个诺贝尔奖得主的总可用市场规模——能够真正做到诺贝尔奖得主能做的一切——是数万亿美元。

**Core structure:**
- The TAM is trillions of dollars.  
  总可用市场规模是数万亿美元。

**Structure tree:**
```
main: The TAM is trillions of dollars
subject: The TAM of a Nobel Prize winner
appositive clause: that can actually do everything...
nested relative clause: (everything) a Nobel Prize winner can do
```

**Grammar points:**
- **同位语从句** - that 从句解释说明 Nobel Prize winner 的含义
- **省略关系代词的定语从句** - everything 后省略了 that，完整为 everything that a Nobel Prize winner can do

### [49:08]
**Original:** There's a question of how much of that goes to the pharmaceutical company or the AI company, but there's an enormous consumer surplus because—assuming we can get access for everyone, which I care about greatly—we cure all of these diseases.

**Translation:** 有一个问题是，其中有多少会流向制药公司或 AI 公司，但会有巨大的消费者剩余，因为——假设我们能让每个人都获得治疗，这是我非常关心的——我们治愈了所有这些疾病。

**Core structure:**
- There's a question, but there's an enormous consumer surplus because we cure diseases.  
  有一个问题，但会有巨大的消费者剩余，因为我们治愈了疾病。

**Structure tree:**
```
compound: question exists + consumer surplus exists
wh-clause: how much goes to...
causal clause: because we cure...
parenthetical: assuming we can get access / which I care about
```

**Grammar points:**
- **破折号插入语** - 破折号内的内容是补充说明，可独立去掉而不影响主句完整性
- **非限制性定语从句修饰整个假设** - which 指代前面整个 assuming 从句的内容

### [51:39]
**Original:** If my revenue is not $1 trillion dollars, if it's even $800 billion, there's no force on earth, there's no hedge on earth that could stop me from going bankrupt if I buy that much compute.

**Translation:** 如果我的收入不是 1 万亿美元，即使是 8000 亿美元，世界上也没有任何力量，没有任何对冲手段能阻止我破产，如果我购买那么多算力的话。

**Core structure:**
- If revenue is not $1T, there's no force that could stop me from going bankrupt.  
  如果收入不是 1 万亿，没有力量能阻止我破产。

**Structure tree:**
```
conditional: If my revenue is not... if it's even...
main: there's no force / no hedge
relative clause: that could stop me from...
nested conditional: if I buy that much
```

**Grammar points:**
- **多重条件句嵌套** - 两个 if 从句层层递进，最后一个 if 从句嵌套在主句内部
- **双重否定强调** - no force... no hedge 并列强调没有任何可能性

### [52:38]
**Original:** I get the impression that some of the other companies have not written down the spreadsheet, that they don't really understand the risks they're taking.

**Translation:** 我的印象是，其他一些公司没有写下电子表格，他们并不真正理解自己正在承担的风险。

**Core structure:**
- I get the impression that companies have not written down the spreadsheet.  
  我的印象是公司没有写下电子表格。

**Structure tree:**
```
main: I get the impression
that-clause 1: companies have not written down...
that-clause 2 (appositive): they don't understand the risks
relative clause: (risks) they're taking
```

**Grammar points:**
- **同位语从句** - 第二个 that 从句与第一个并列，共同解释 impression 的内容
- **省略关系代词** - risks 后省略 that/which，完整为 the risks that they're taking

### [54:02]
**Original:** I would happily buy $5 trillion worth of compute to run an actual country of human geniuses in a data center.

**Translation:** 我会很乐意购买价值5万亿美元的算力来在数据中心运行一个真正由人类天才组成的国家。

**Core structure:**
- I would buy compute to run a country of geniuses.  
  我会购买算力来运行一个天才国家。

**Structure tree:**
```
main clause: I would buy $5 trillion worth of compute
purpose clause: to run an actual country
modifier: of human geniuses
location: in a data center
```

**Grammar points:**
- **worth of + 名词** - $5 trillion worth of compute = 价值5万亿美元的算力
- **不定式表目的** - to run 表示购买算力的目的

### [54:18]
**Original:** It is worth stating that with clinical trials, most clinical trials fail because the drug doesn't work.

**Translation:** 值得说明的是，在临床试验中，大多数临床试验失败是因为药物不起作用。

**Core structure:**
- It is worth stating that most trials fail because the drug doesn't work.  
  值得说明的是，大多数试验失败是因为药物不起作用。

**Structure tree:**
```
formal subject: It
real subject: that most clinical trials fail
cause clause: because the drug doesn't work
prepositional phrase: with clinical trials (context)
```

**Grammar points:**
- **It is worth doing** - 形式主语结构，真正主语是 that 从句
- **because 引导原因状语从句**

### [55:50]
**Original:** But second, what if the country of geniuses comes, but it comes in mid-2028 instead of mid-2027?

**Translation:** 但其次，如果天才国家到来了，但它是在2028年中而不是2027年中到来的呢？

**Core structure:**
- What if it comes in 2028 instead of 2027?  
  如果它在2028年而不是2027年到来呢？

**Structure tree:**
```
hypothetical question: what if...
coordinate clauses: the country comes, but it comes...
time contrast: in mid-2028 instead of mid-2027
```

**Grammar points:**
- **What if 虚拟语气** - 表示假设情况，引导虚拟条件句
- **instead of** - 表示替代或对比

### [56:11]
**Original:** Even in the longest version of the timelines you state, the compute you are ramping up to build doesn't seem in accordance.

**Translation:** 即使在你陈述的最长时间线版本中，你正在逐步建设的算力似乎也不相符。

**Core structure:**
- The compute doesn't seem in accordance.  
  算力似乎不相符。

**Structure tree:**
```
concessive clause: Even in the longest version
subject: the compute you are ramping up to build
predicate: doesn't seem in accordance
modifier: you state (relative clause)
```

**Grammar points:**
- **ramp up to do** - 逐步增加以达到某个目标
- **in accordance (with)** - 与...一致，此处省略了 with 的宾语

### [57:03]
**Original:** I'm doing the math in my head, but each gigawatt costs maybe $10 billion, on the order of $10-15 billion a year.

**Translation:** 我在心里算着，但每千兆瓦的成本大约是100亿美元，大约每年100-150亿美元。

**Core structure:**
- Each gigawatt costs $10 billion.  
  每千兆瓦成本100亿美元。

**Structure tree:**
```
contrast: I'm doing math, but each gigawatt costs...
approximation: maybe $10 billion
refinement: on the order of $10-15 billion a year
```

**Grammar points:**
- **on the order of** - 大约、接近于（表示数量级）
- **同位语修饰** - on the order of 进一步解释 $10 billion

### [59:36]
**Original:** I actually think profitability happens when you underestimated the amount of demand you were going to get, and loss happens when you overestimated the amount of demand you were going to get, because you're buying the data centers ahead of time.

**Translation:** 我认为盈利发生在你低估了你将获得的需求量时,而亏损发生在你高估了你将获得的需求量时,因为你是提前购买数据中心的。

**Core structure:**
- Profitability happens when you underestimated demand, and loss happens when you overestimated demand.  
  盈利发生在你低估需求时,亏损发生在你高估需求时。

**Structure tree:**
```
main clause 1: profitability happens when...
  time clause: when you underestimated...
    object clause: the amount of demand (that) you were going to get
main clause 2: loss happens when...
  time clause: when you overestimated...
causal clause: because you're buying...
```

**Grammar points:**
- **并列复合句** - 两个平行的when时间状语从句结构,用and连接
- **省略关系代词的定语从句** - the amount of demand (that) you were going to get,口语中常省略that
- **过去将来时** - were going to get表示从过去某时间点看将要发生的事

### [01:00:07]
**Original:** So what that means is that if you were in steady state, you build a data center and if you knew exactly the demand you were getting, you would get a certain amount of revenue.

**Translation:** 所以这意味着,如果你处于稳定状态,你建造一个数据中心,并且如果你确切知道你将获得的需求,你就会获得一定数额的收入。

**Core structure:**
- What that means is that you would get revenue.  
  这意味着你会获得收入。

**Structure tree:**
```
main clause: what that means is that...
subject clause: what that means
predicative clause: that if you were... you would get...
  condition 1: if you were in steady state
  condition 2: if you knew exactly the demand
    object clause: the demand (that) you were getting
```

**Grammar points:**
- **虚拟条件句** - if...were/knew..., you would get...表示假设情况
- **多重从句嵌套** - 主语从句+表语从句+条件从句+定语从句的四层结构

### [01:02:04]
**Original:** What I'm trying to get at is that you have a model in your head of a business that invests, invests, invests, gets scale and then becomes profitable.

**Translation:** 我想说的是,你脑海中有一个商业模式:不断投资、投资、投资,获得规模,然后变得盈利。

**Core structure:**
- What I'm trying to get at is that you have a model of a business.  
  我想说的是你有一个商业模式。

**Structure tree:**
```
main clause: what I'm trying to get at is that...
subject clause: what I'm trying to get at
predicative clause: that you have a model...
  定语从句: of a business that invests...
    并列谓语: invests, invests, invests, gets scale and becomes profitable
```

**Grammar points:**
- **What主语从句** - what I'm trying to get at是习语,表示'我想表达的重点'
- **重复强调** - invests重复三次是口语强调手法,表示持续不断投资

### [01:02:44]
**Original:** If every year we predict exactly what the demand is going to be, we'll be profitable every year.

**Translation:** 如果我们每年都能准确预测需求将会是多少,我们每年都会盈利。

**Core structure:**
- If we predict the demand, we'll be profitable.  
  如果我们预测需求,我们就会盈利。

**Structure tree:**
```
condition clause: if every year we predict...
  object clause: what the demand is going to be
main clause: we'll be profitable every year
```

**Grammar points:**
- **真实条件句** - if...一般现在时, will...表示对未来真实可能情况的假设
- **what引导宾语从句** - what the demand is going to be作predict的宾语,what在从句中作表语

### [01:03:34]
**Original:** If 70% would get you a very little bit of a smaller model through a factor of 1.4x... That extra $20 billion, each dollar there is worth much less to you because of the log-linear setup.

**Translation:** 如果70%会通过1.4倍的系数让你得到一个小一点点的模型...那额外的200亿美元,其中的每一美元对你来说价值要少得多,因为是对数线性设置。

**Core structure:**
- Each dollar is worth much less to you.  
  每一美元对你来说价值要少得多。

**Structure tree:**
```
condition clause (incomplete): if 70% would get you...
main clause: each dollar is worth much less
  主语同位语: that extra $20 billion, each dollar there
  原因状语: because of the log-linear setup
```

**Grammar points:**
- **不完整条件句** - 口语中if从句未完成就转到主句,用省略号表示思维跳跃
- **同位语结构** - 'that extra $20 billion'和'each dollar there'是同位关系,后者具体化前者

### [01:05:10]
**Original:** Why doesn't everyone spend 100% of their compute on training and not serve any customers? It's because if they didn't get any revenue, they couldn't raise money, they couldn't do compute deals, they couldn't buy more compute the next year.

**Translation:** 为什么不是每个人都把100%的算力用于训练而不服务任何客户呢？因为如果他们得不到任何收入，他们就无法筹集资金，无法达成算力交易，无法在第二年购买更多算力。

**Core structure:**
- It's because if they didn't get revenue, they couldn't raise money, couldn't do deals, couldn't buy compute.  
  因为如果他们得不到收入，他们就无法筹集资金、达成交易、购买算力。

**Structure tree:**
```
main clause: It's because...
conditional clause: if they didn't get any revenue
parallel result clauses: they couldn't raise money / couldn't do deals / couldn't buy compute
time modifier: the next year
```

**Grammar points:**
- **虚拟条件句（与现在事实相反）** - if + 过去时，主句 + couldn't，表示与现实相反的假设
- **并列省略结构** - 三个 couldn't 后省略主语 they，形成紧凑的排比

### [01:06:08]
**Original:** The problem is you have this hellish demand prediction problem when you're buying the next year of compute and you might guess under and be very profitable but have no compute for research.

**Translation:** 问题是，当你购买下一年的算力时，你会遇到这个地狱般的需求预测问题，你可能会预估不足，结果非常盈利但没有算力用于研究。

**Core structure:**
- The problem is you have this demand prediction problem and you might guess under.  
  问题是你有这个需求预测问题，你可能会预估不足。

**Structure tree:**
```
main clause: The problem is...
predicative clause: you have this problem
temporal clause: when you're buying...
result clause: you might guess under and be profitable but have no compute
```

**Grammar points:**
- **连续并列转折** - guess under and be profitable but have no compute 三个动词短语用 and/but 连接，表达复杂的因果转折关系
- **guess under/over 作不及物动词** - under/over 作副词，表示预估不足/过度

### [01:06:21]
**Original:** Or you might guess over and you are not profitable and you have all the compute for research in the world.

**Translation:** 或者你可能会预估过度，然后你不盈利，但你拥有世界上所有用于研究的算力。

**Core structure:**
- You might guess over and you are not profitable and you have all the compute.  
  你可能预估过度，你不盈利，但你拥有所有算力。

**Structure tree:**
```
parallel structure to previous sentence
main clause: you might guess over
result clauses: you are not profitable / you have all the compute
hyperbole: all the compute in the world
```

**Grammar points:**
- **三个并列分句** - 三个 and 连接的独立分句，形成递进关系
- **夸张修辞** - in the world 强调算力充裕到极致

### [01:07:07]
**Original:** It is hard for me to see that there won't be trillions of dollars in revenue before 2030.

**Translation:** 我很难看到在2030年之前收入不会达到数万亿美元。

**Core structure:**
- It is hard for me to see that there won't be trillions.  
  我很难看到不会有数万亿。

**Structure tree:**
```
formal subject: It
real subject: to see that...
that-clause: there won't be trillions
double negative effect: hard to see + won't = will likely happen
```

**Grammar points:**
- **双重否定表肯定** - hard to see... won't 实际表达强烈肯定：很可能会有
- **形式主语 it** - 真正主语是不定式 to see that 从句

### [01:09:15]
**Original:** If there's three firms in the economy and all are kind of independently behaving rationally, it doesn't equilibrate to zero.

**Translation:** 如果经济中有三家公司，并且所有公司都以某种独立理性的方式行事，它不会均衡到零。

**Core structure:**
- If there's three firms and all behave rationally, it doesn't equilibrate to zero.  
  如果有三家公司且都理性行事，它不会均衡到零。

**Structure tree:**
```
conditional clause: If there's three firms...
parallel condition: and all are behaving rationally
main clause: it doesn't equilibrate to zero
hedge: kind of (softener)
```

**Grammar points:**
- **条件句（真实条件）** - if + 现在时，主句现在时，陈述客观规律
- **kind of 作程度副词** - 口语化表达，弱化语气，表示'某种程度上'

### [01:10:02]
**Original:** Then this year it produced $4 billion of revenue and cost $1 billion to inference from.

**Translation:** 然后今年它产生了40亿美元的收入，推理成本为10亿美元。

**Core structure:**
- It produced revenue and cost money to inference from.  
  它产生了收入，推理花费了资金。

**Structure tree:**
```
main clause: it produced $4 billion... and cost $1 billion...
parallel structure: produced... and cost...
infinitive phrase: to inference from
```

**Grammar points:**
- **并列谓语** - produced 和 cost 为并列动词，共用主语 it
- **不定式短语作状语** - to inference from 表示目的或用途，from 的使用较为非标准

### [01:10:23]
**Original:** But at the same time, we're spending $10 billion to train the next model because there's an exponential scale-up.

**Translation:** 但与此同时，我们要花费100亿美元来训练下一个模型，因为存在指数级扩张。

**Core structure:**
- We're spending money to train the model because there's a scale-up.  
  我们花钱训练模型，因为有扩张。

**Structure tree:**
```
main clause: we're spending $10 billion...
purpose: to train the next model
reason: because there's an exponential scale-up
```

**Grammar points:**
- **不定式表目的** - to train 说明花钱的目的
- **because 引导原因状语从句**

### [01:10:31]
**Original:** The equilibrium I'm talking about is an equilibrium where we have the "country of geniuses in a data center", but that model training scale-up has equilibrated more.

**Translation:** 我所说的平衡状态是这样一种平衡：我们拥有"数据中心里的天才之国"，但模型训练的扩张已经更加平衡了。

**Core structure:**
- The equilibrium is an equilibrium where the scale-up has equilibrated.  
  这个平衡状态是扩张已经平衡的状态。

**Structure tree:**
```
main clause: The equilibrium is an equilibrium...
modifier: I'm talking about (定语从句修饰 equilibrium)
where clause: where we have... but scale-up has equilibrated
contrast: but 连接转折关系
```

**Grammar points:**
- **省略关系代词的定语从句** - I'm talking about 修饰 equilibrium，省略了 that/which
- **where 引导定语从句** - 修饰先行词 equilibrium，描述这种平衡的特征

### [01:14:11]
**Original:** So if you go to someone and you're like, 'I want to disrupt this industry, here's $100 billion.' You're like, 'Okay, I'm putting in $100 billion and also betting that you can do all these other things that these people have been doing.'

**Translation:** 所以如果你去找某人说，'我想颠覆这个行业，这里有1000亿美元。'你会说，'好的，我投入1000亿美元，同时也在赌你能做到这些人一直在做的所有其他事情。'

**Core structure:**
- If you go to someone, you're putting in money and betting that you can do things.  
  如果你去找某人，你在投钱并赌你能做到这些事。

**Structure tree:**
```
condition: if you go to someone...
quoted speech: 'I want to disrupt...'
main response: You're like, 'Okay, I'm putting...'
parallel structure: putting... and betting...
nested that-clause: that you can do...
nested relative clause: that these people have been doing
```

**Grammar points:**
- **条件状语从句** - if 引导假设情境
- **并列结构** - putting 和 betting 为并列动名词，作表语
- **嵌套定语从句** - that these people have been doing 修饰 things，形成双层嵌套

### [01:14:26]
**Original:** Only to decrease the profit.

**Translation:** 只会降低利润。

**Core structure:**
- Only to decrease the profit.  
  只会降低利润。

**Structure tree:**
```
infinitive phrase: Only to decrease the profit
result/purpose marker: Only to (表示结果)
```

**Grammar points:**
- **only to 结构** - 表示意外或讽刺的结果，相当于'结果却是'，前面省略了完整句子，依赖上下文理解
- **省略句** - 这是个不完整的句子片段，完整形式应为'You do all this only to decrease the profit'

### [01:16:09]
**Original:** But that's kind of far post-'country of geniuses in the data center.'

**Translation:** 但那是在"数据中心里的天才之国"之后很远的事了。

**Core structure:**
- That's far post X.  
  那是在X之后很远的事。

**Structure tree:**
```
main clause: that's far post X
post-: 前缀表示"在...之后"
X = 'country of geniuses in the data center' (引用前文概念)
```

**Grammar points:**
- **post- 作前缀** - post-表示时间上的"在...之后"，这里post后直接跟名词短语而非动词，形成"post-X"结构表示"X之后的阶段"
- **引用式表达** - 用引号标记前文提到的概念，作为时间参照点

### [01:16:17]
**Original:** Maybe a finer way to put that potential point is: 1) it seems like AI research is especially loaded on raw intellectual power, which will be especially abundant in the world of AGI.

**Translation:** 也许更精确地表述这个潜在观点的方式是：1) 看起来AI研究特别依赖原始智力，而这在AGI的世界里会特别充足。

**Core structure:**
- A way to put the point is: AI research is loaded on intellectual power.  
  表述这个观点的方式是：AI研究依赖智力。

**Structure tree:**
```
main clause: A way to put the point is...
predicative clause: it seems like AI research is loaded on power
relative clause: which will be abundant in the world of AGI
modifiers: finer (比较级), especially (副词强调)
```

**Grammar points:**
- **be loaded on** - 固定搭配，表示"严重依赖于、集中在"
- **非限制性定语从句前置先行词** - which指代intellectual power，补充说明其在AGI世界中的状态
- **it seems like 从句** - 表示推测或主观判断

### [01:17:34]
**Original:** So when I said the 10-20% growth rate, a worry I have is that the growth rate could be like 50% in Silicon Valley and parts of the world that are socially connected to Silicon Valley, and not that much faster than its current pace elsewhere.

**Translation:** 所以当我说10-20%的增长率时，我担心的是增长率可能在硅谷和与硅谷有社会联系的世界其他地区达到50%，而在其他地方不会比目前的速度快多少。

**Core structure:**
- A worry is that the growth rate could be 50% in some places and not faster elsewhere.  
  担心是增长率在某些地方可能是50%，而在其他地方不会更快。

**Structure tree:**
```
时间状语从句: when I said...
main clause: a worry I have is that...
that从句: the growth rate could be X in Y, and not Z elsewhere
Y的定语从句: that are socially connected to Silicon Valley
```

**Grammar points:**
- **同位语结构** - a worry I have 中省略了关系代词that/which，I have修饰worry
- **并列否定结构** - and not... elsewhere 与前面的could be形成对比，表示不同地区的差异
- **be like + 数字** - 口语化表达，表示"大约是、接近"

### [01:20:02]
**Original:** But just as people weren't talking about continual learning a couple of years ago, and then we realized, 'Oh, why aren't these models as useful as they could be right now, even though they are clearly passing the Turing test and are experts in so many different domains? Maybe it's this thing.'

**Translation:** 但就像几年前人们还没在讨论持续学习，然后我们意识到，'哦，为什么这些模型现在还没有达到它们本可以达到的有用程度，尽管它们明显通过了图灵测试并且在很多不同领域都是专家？也许就是因为这个。'

**Core structure:**
- Just as people weren't talking about X, we realized why models aren't as useful as they could be.  
  就像人们没在讨论X，我们意识到为什么模型没有那么有用。

**Structure tree:**
```
just as从句: people weren't talking about X
main clause: we realized...
realized后接引语: 包含疑问句(why aren't models as useful...)
concession clause: even though they are passing... and are experts...
suggestion: Maybe it's this thing
```

**Grammar points:**
- **just as 引导方式状语从句** - 表示"正如、就像"，类比前后两种情况
- **as...as sb could be 结构** - 表示"达到某人本可以达到的程度"，could暗示潜力未实现
- **even though 让步状语从句** - 引出转折，强调尽管有某些成就但仍存在问题

### [01:20:51]
**Original:** In ML, of people coming up with things that are barriers that end up kind of dissolving within the big blob of compute.

**Translation:** 在机器学习中，人们提出的那些被认为是障碍的东西，最终都在大规模计算的洪流中逐渐消解了。

**Core structure:**
- People come up with barriers that dissolve within compute.  
  人们提出的障碍在计算中消解。

**Structure tree:**
```
介词短语: In ML
of短语: of people coming up with things (修饰history，省略自前文)
things的定语从句1: that are barriers
barriers的定语从句2: that end up dissolving...
```

**Grammar points:**
- **省略主语结构** - 这是前一句的延续，省略了主语'the history'，of短语作定语
- **嵌套定语从句** - 两个that从句层层修饰，第二个that指代barriers
- **end up doing** - 固定搭配，表示"最终...、结果是..."

### [01:23:04]
**Original:** These days that probably has more to do with seeing a bunch of stuff within Anthropic and having to make a bunch of decisions than I have any great research insight that others don't.

**Translation:** 如今，这可能更多地与在 Anthropic 内部看到许多东西以及必须做出许多决策有关，而不是我拥有其他人所没有的伟大研究洞察力。

**Core structure:**
- That has more to do with X than Y.  
  这更多地与 X 有关，而不是 Y。

**Structure tree:**
```
main clause: that has more to do with X than Y
X: seeing stuff and having to make decisions
Y: I have any great research insight
relative clause: that others don't (have)
```

**Grammar points:**
- **more...than 比较结构** - 表示「更多是 X 而非 Y」的对比关系
- **have to do with** - 表示「与...有关」
- **定语从句省略** - that others don't (have) 修饰 insight

### [01:23:59]
**Original:** One way I think about it is if the technology is advancing quickly, if it's advancing exponentially, what that means is there's always a surface area of new use cases that have been developed in the last three months.

**Translation:** 我思考这个问题的一种方式是，如果技术发展迅速，如果它呈指数级发展，那意味着总是有在过去三个月中开发出来的新用例的表面区域。

**Core structure:**
- One way I think about it is (that) what that means is there's always new use cases.  
  我思考它的一种方式是，那意味着总是有新的用例。

**Structure tree:**
```
main: One way is...
predicative clause 1: if technology is advancing...
predicative clause 2: what that means is...
subject clause: what that means
there be structure: there's always a surface area
relative clause: that have been developed
```

**Grammar points:**
- **多层嵌套从句** - 主句包含两个条件从句和一个 what 主语从句
- **what 引导主语从句** - what that means 作主语，表示「那意味着的东西」
- **there be 存在句** - 表示「存在」某物

### [01:24:20]
**Original:** Any kind of product surface you put in place is always at risk of sort of becoming irrelevant.

**Translation:** 你建立的任何一种产品界面总是面临着某种程度上变得无关紧要的风险。

**Core structure:**
- Any product surface is at risk of becoming irrelevant.  
  任何产品界面都面临变得无关紧要的风险。

**Structure tree:**
```
main: Any product surface is at risk
relative clause: (that) you put in place
prepositional phrase: at risk of...
gerund phrase: becoming irrelevant
```

**Grammar points:**
- **省略关系代词的定语从句** - (that) you put in place 修饰 product surface
- **be at risk of doing** - 表示「面临做某事的风险」
- **sort of** - 口语化表达，表示「某种程度上」，弱化语气

### [01:26:26]
**Original:** Whereas if the model goes to one of the pharmaceutical companies and it says, 'Oh, you know, this molecule you're developing, you should take the aromatic ring from that end of the molecule and put it on that end of the molecule. If you do that, wonderful things will happen.'

**Translation:** 而如果模型去到其中一家制药公司并说，「哦，你知道，你正在开发的这个分子，你应该把芳香环从分子的那一端取下来，放到分子的另一端。如果你这样做，美妙的事情就会发生。」

**Core structure:**
- If the model goes to companies and says X, those tokens are worth millions.  
  如果模型去公司并说 X，这些 token 就值数百万。

**Structure tree:**
```
conditional: if the model goes and says...
direct speech: 'Oh, you know...'
inner clause: this molecule (that) you're developing
command: you should take... and put...
inner conditional: If you do that, things will happen
```

**Grammar points:**
- **条件句 + 直接引语** - if 从句中包含直接引语，引语内又有条件句
- **省略 that 的定语从句** - this molecule (that) you're developing
- **祈使句结构** - you should take... and put... 表示建议

### [01:28:09]
**Original:** Around the beginning of 2025, I said, 'I think the time has come where you can have nontrivial acceleration of your own research if you're an AI company by using these models.'

**Translation:** 在 2025 年初左右，我说，「我认为这样一个时刻已经到来：如果你是一家 AI 公司，通过使用这些模型，你可以对自己的研究产生不小的加速作用。」

**Core structure:**
- I said the time has come where you can have acceleration by using models.  
  我说时机已到，你可以通过使用模型获得加速。

**Structure tree:**
```
main: I said...
direct speech: I think the time has come
relative clause: where you can have acceleration
condition: if you're an AI company
means: by using these models
```

**Grammar points:**
- **where 引导定语从句** - 修饰抽象名词 time，表示「在这个时刻」
- **the time has come** - 现在完成时表示「时机已到」
- **by doing** - 表示方式、手段

### [01:31:24]
**Original:** It seems like whatever vision we have about how AI goes well has to be compatible with two things: 1) the ability to build and run AIs is diffusing extremely rapidly and 2) the population of AIs, the amount we have and their intelligence, will also increase very rapidly.

**Translation:** 看起来无论我们对AI如何良好发展有什么愿景,都必须与两件事相兼容:1)构建和运行AI的能力正在极其迅速地扩散,2)AI的数量、我们拥有的AI数量以及它们的智能也将非常迅速地增长。

**Core structure:**
- Whatever vision we have has to be compatible with two things.  
  无论我们有什么愿景,都必须与两件事相兼容。

**Structure tree:**
```
主句: whatever vision has to be compatible
让步状语从句: whatever vision we have
介词短语: about how AI goes well
并列成分: two things (1) and (2)
```

**Grammar points:**
- **Whatever引导让步状语从句** - whatever = no matter what,表示'无论什么'
- **嵌套从句结构** - 主语从句中包含介词短语'about how AI goes well',层层嵌套

### [01:31:44]
**Original:** That means that lots of people will be able to build huge populations of misaligned AIs, or AIs which are just companies which are trying to increase their footprint or have weird psyches like Sydney Bing, but now they're superhuman.

**Translation:** 这意味着很多人将能够构建大量未对齐的AI,或者那些只是试图扩大影响力或具有像Sydney Bing那样奇怪心理的公司型AI,但现在它们是超人类的。

**Core structure:**
- Lots of people will be able to build populations of AIs.  
  很多人将能够构建大量AI。

**Structure tree:**
```
主句: That means that...
宾语从句: lots of people will be able to build...
定语从句1: which are just companies
定语从句2: which are trying to increase...
转折: but now they're superhuman
```

**Grammar points:**
- **多层定语从句嵌套** - which引导的定语从句内部再嵌套另一个which从句
- **并列结构与对比** - 'or'连接两种AI类型,'but'引出转折对比

### [01:33:26]
**Original:** I think in the long run we need some architecture of governance that preserves human freedom, but also allows us to govern a very large number of human systems, AI systems, hybrid human-AI companies or economic units.

**Translation:** 我认为从长远来看,我们需要某种治理架构,既能保护人类自由,又能让我们治理大量的人类系统、AI系统、人类-AI混合公司或经济单位。

**Core structure:**
- We need some architecture that preserves freedom but also allows us to govern systems.  
  我们需要某种架构,既保护自由又允许我们治理系统。

**Structure tree:**
```
主句: we need some architecture
定语从句: that preserves...but also allows...
并列宾语: human systems, AI systems, hybrid companies...
```

**Grammar points:**
- **Not only...but also结构变体** - 用'but also'表达双重要求,强调两者兼顾
- **复杂宾语列举** - govern后跟多个并列宾语,表达治理对象的多样性

### [01:34:34]
**Original:** My worry is, if we had 100 years for this to happen all very slowly, we'd get used to it.

**Translation:** 我的担忧是,如果我们有100年时间让这一切非常缓慢地发生,我们会习惯它。

**Core structure:**
- My worry is we'd get used to it if we had time.  
  我的担忧是如果有时间我们会习惯它。

**Structure tree:**
```
主句: My worry is...
条件状语从句: if we had 100 years...
主句(虚拟语气): we'd get used to it
```

**Grammar points:**
- **虚拟语气(与现在事实相反)** - if从句用过去时(had),主句用would,表示与现实相反的假设
- **Get used to结构** - '习惯于'的固定搭配,to是介词

### [01:35:07]
**Original:** It seems like in an offense-dominant world, over the course of the next century—the idea is that AI is making the progress that would happen over the next century happen in some period of five to ten years—we would still need the same mechanisms, or balance of power would be similarly intractable, even if humans were the only game in town.

**Translation:** 似乎在一个进攻占主导的世界中,在下个世纪的进程中——这个想法是AI正在让原本需要下个世纪才能发生的进步在五到十年内发生——我们仍然需要相同的机制,或者权力平衡将同样棘手,即使人类是唯一的参与者。

**Core structure:**
- We would still need the same mechanisms or balance would be intractable.  
  我们仍需要相同的机制,或者平衡将是棘手的。

**Structure tree:**
```
主句: we would still need... or balance would be...
插入语: the idea is that AI is making...
定语从句: that would happen over the next century
让步状语从句: even if humans were the only game
```

**Grammar points:**
- **破折号插入语** - 中间插入长句解释'下个世纪'的含义,打断主句结构
- **Make sth happen结构** - 使役动词make后接宾语+动词原形,表示'使...发生'
- **Even if虚拟条件** - 即使在假设情况下,问题依然存在

### [01:35:58]
**Original:** I don't want to say this is so far ahead in time, but it's so far ahead in technological ability that may happen over a short period of time, that it's hard for us to anticipate it in advance.

**Translation:** 我不想说这在时间上遥不可及,但它在技术能力上如此超前,以至于可能在短时间内发生,这让我们很难提前预测。

**Core structure:**
- It's so far ahead that it's hard to anticipate.  
  它如此超前,以至于很难预测。

**Structure tree:**
```
main: I don't want to say X, but Y, that Z
├─ contrast clause: but it's so far ahead...
├─ result clause: that may happen...
└─ consequence clause: that it's hard to anticipate
```

**Grammar points:**
- **so...that 结果状语从句嵌套** - 两个连续的 that 从句表示递进结果,第一个修饰程度,第二个表达最终结果
- **but 连接转折** - 连接两个对比观点,结构复杂时容易失去重心

### [01:36:48]
**Original:** A lot of the benefits that normal people could experience as a result of AI are going to be curtailed, especially when we get into the kinds of things you discuss in 'Machines of Loving Grace': biological freedom, mental health improvements, et cetera.

**Translation:** 普通人能从AI中获得的许多好处将会被削减,尤其是当我们涉及你在《慈爱的机器》中讨论的那些事情时:生物自由、心理健康改善等等。

**Core structure:**
- Benefits are going to be curtailed when we get into those things.  
  当我们涉及那些事情时,好处将被削减。

**Structure tree:**
```
main: Benefits are going to be curtailed
├─ subject modifier: that normal people could experience
├─ as a result of AI (状语)
└─ time clause: when we get into... (嵌套定语从句)
```

**Grammar points:**
- **定语从句 that + 状语 as a result of** - 主语被长定语从句和状语短语分隔,增加理解难度
- **when 时间状语从句内嵌套定语** - you discuss 修饰 things,从句内再嵌套修饰成分

### [01:38:47]
**Original:** Now, I think the thing that we should do, the thing that I would support, is the federal government should step in, not saying 'states you can't regulate', but 'Here's what we're going to do, and states you can't differ from this.'

**Translation:** 现在,我认为我们应该做的,也是我会支持的,是联邦政府应该介入,不是说'州政府你们不能监管',而是说'这是我们要做的,州政府你们不能与此有所不同'。

**Core structure:**
- The thing we should do is the federal government should step in.  
  我们应该做的是联邦政府应该介入。

**Structure tree:**
```
main: The thing is (that) the government should step in
├─ subject apposition: the thing that..., the thing that...
├─ predicative: government should step in
└─ manner: not saying X, but Y (对比结构)
```

**Grammar points:**
- **同位语重复强调** - the thing that 重复出现,强调主语,但增加句子长度
- **not...but 对比 + 直接引语** - 对比两种表述方式,引语内容长且复杂

### [01:40:07]
**Original:** We need to pursue this in an intellectually honest way where we say that ahead of time, the risk has not emerged yet.

**Translation:** 我们需要以一种理智诚实的方式来推进这件事,在这种方式下,我们提前说明风险尚未出现。

**Core structure:**
- We need to pursue this in a way where we say the risk has not emerged.  
  我们需要以一种方式推进,在这种方式下我们说风险尚未出现。

**Structure tree:**
```
main: We need to pursue this in a way
├─ manner modifier: in an intellectually honest way
├─ where clause: where we say that...
└─ embedded clause: that...the risk has not emerged
```

**Grammar points:**
- **where 引导定语从句修饰抽象名词 way** - where = in which,修饰方式,较为抽象
- **ahead of time 插入语位置** - 插在 say that 和主句之间,打断正常语序

### [01:41:03]
**Original:** Whereas at the same time, it seems like you think the dangers are already on the horizon and I just don't see that much... It seems like it would be especially injurious to the benefits of AI as compared to the dangers of AI.

**Translation:** 然而与此同时,看起来你认为危险已经迫在眉睫,而我就是看不到那么多...这似乎会对AI的好处造成特别大的伤害,相比于AI的危险而言。

**Core structure:**
- It would be injurious to the benefits as compared to the dangers.  
  相比于危险,这会对好处造成伤害。

**Structure tree:**
```
compound: whereas..., it seems X and I don't see Y... It seems Z
├─ contrast: whereas at the same time
├─ embedded: like you think (that) dangers are...
└─ comparison: as compared to the dangers
```

**Grammar points:**
- **whereas 表示对比转折** - 引导对比分句,与 at the same time 共同强化对比关系
- **as compared to 比较结构后置** - 比较成分放在句末,需要回溯理解完整对比关系

### [01:42:50]
**Original:** I think reform of the regulatory process should bias more towards the fact that we have a lot of things coming where the safety and efficacy is actually going to be really crisp and clear, a beautiful thing, and really effective.

**Translation:** 我认为监管流程的改革应该更多地倾向于这样一个事实：我们有很多即将到来的东西，它们的安全性和有效性实际上将会非常明确清晰、非常美好，而且真正有效。

**Core structure:**
- Reform should bias towards the fact that we have things coming.  
  改革应该倾向于我们有东西即将到来这一事实。

**Structure tree:**
```
main: I think reform should bias towards the fact
subordinate: that we have things coming
relative: where the safety is going to be crisp
coordination: clear, a beautiful thing, and effective
```

**Grammar points:**
- **bias towards + 名词性从句** - towards 后接 the fact，再用 that 从句解释具体事实
- **where 引导定语从句** - 修饰 things，描述这些东西的特点
- **并列结构** - crisp and clear, a beautiful thing, and effective 三个并列成分

### [01:43:12]
**Original:** Maybe we don't need all this superstructure around it that was designed around an era of drugs that barely work and often have serious side effects.

**Translation:** 也许我们不需要围绕它的所有这些上层结构，这些结构是围绕一个药物勉强有效且经常有严重副作用的时代而设计的。

**Core structure:**
- Maybe we don't need this superstructure that was designed around an era.  
  也许我们不需要这个围绕某个时代设计的上层结构。

**Structure tree:**
```
main: we don't need superstructure
relative 1: that was designed around an era
relative 2: of drugs that barely work
coordination: and often have side effects
```

**Grammar points:**
- **嵌套定语从句** - that 从句修饰 superstructure，其中又嵌套修饰 drugs 的定语从句
- **barely 副词用法** - 表示'几乎不'，修饰 work

### [01:43:58]
**Original:** Then, if these risks emerge when we're more certain of them—which I think we might be as soon as later this year—then I think we need to act very fast in the areas where we've actually seen the risk.

**Translation:** 然后，如果这些风险在我们更确定它们时出现——我认为我们最快可能在今年晚些时候就会确定——那么我认为我们需要在我们实际看到风险的领域非常快速地采取行动。

**Core structure:**
- If risks emerge, then we need to act fast in the areas.  
  如果风险出现，那么我们需要在这些领域快速行动。

**Structure tree:**
```
condition: if risks emerge when we're certain
parenthetical: which I think we might be
main: then we need to act fast
relative: where we've seen the risk
```

**Grammar points:**
- **条件状语从句 + 时间状语从句** - if 从句中嵌套 when 从句，形成复合条件
- **破折号插入成分** - which 从句作为补充说明，打断主句结构
- **where 引导定语从句** - 修饰 areas，表示地点/领域

### [01:44:24]
**Original:** That's why I wrote Adolescence of Technology. I wanted policymakers, economists, national security professionals, and decision-makers to read it so that they have some hope of acting faster than they would have otherwise.

**Translation:** 这就是我写《技术的青春期》的原因。我希望政策制定者、经济学家、国家安全专业人士和决策者阅读它，这样他们就有希望比他们原本会采取的行动更快地行动。

**Core structure:**
- I wanted people to read it so that they have hope of acting faster.  
  我希望人们阅读它，这样他们就有希望更快地行动。

**Structure tree:**
```
main: I wanted [people] to read it
purpose: so that they have hope
of + gerund: of acting faster
comparison: than they would have otherwise
```

**Grammar points:**
- **want sb to do 结构** - want 后接复合宾语，多个并列宾语
- **so that 目的状语从句** - 表示目的或结果
- **省略结构 otherwise** - than they would have [acted] otherwise，省略重复动词

### [01:47:10]
**Original:** So the things we've been doing are working with philanthropists. We work with folks who deliver medicine and health interventions to the developing world, to sub-Saharan Africa, India, Latin America, and other developing parts of the world.

**Translation:** 所以我们一直在做的事情是与慈善家合作。我们与那些向发展中国家、撒哈拉以南非洲、印度、拉丁美洲和世界其他发展中地区提供医疗和健康干预的人合作。

**Core structure:**
- We work with folks who deliver medicine to the developing world.  
  我们与向发展中国家提供医疗的人合作。

**Structure tree:**
```
main: we work with folks
relative: who deliver medicine and interventions
prepositional phrase: to the developing world
enumeration: to Africa, India, Latin America...
```

**Grammar points:**
- **who 引导定语从句** - 修饰 folks，说明具体是哪些人
- **并列介词短语** - 多个 to 短语并列，列举具体地区

### [01:49:30]
**Original:** My worry is if the world gets carved up into two pieces, one of those two pieces could be authoritarian or totalitarian in a way that's very difficult to displace.

**Translation:** 我担心的是，如果世界被分割成两部分，其中一部分可能会以一种很难被取代的方式成为威权或极权国家。

**Core structure:**
- My worry is if the world gets carved up, one piece could be authoritarian.  
  我担心的是，如果世界被分割，一部分可能会变成威权国家。

**Structure tree:**
```
主句: My worry is...
表语从句: if the world gets carved up (条件)
  - 主句: one piece could be authoritarian
  - 方式状语: in a way that's very difficult to displace
    - 定语从句: that's very difficult to displace
```

**Grammar points:**
- **if 引导表语从句** - if 从句作表语，表达担忧内容。不同于条件状语从句。
- **in a way that 结构** - 方式状语 + 定语从句，描述程度或特征。

### [01:50:00]
**Original:** I'm not saying that one country, either the United States or a coalition of democracies—which I think would be a better setup, although it requires more international cooperation than we currently seem to want to make—should just say, 'These are the rules of the road.'

**Translation:** 我并不是说某一个国家，不管是美国还是民主国家联盟——我认为后者会是更好的安排，尽管它需要比我们目前似乎愿意做的更多的国际合作——应该直接说'这些就是游戏规则'。

**Core structure:**
- I'm not saying that one country should say 'These are the rules.'  
  我不是说一个国家应该说'这些是规则'。

**Structure tree:**
```
主句: I'm not saying that...
宾语从句: one country should say...
  - 主语: one country (同位语: US or coalition)
    - 插入语: which I think would be better
      - 让步从句: although it requires more cooperation
```

**Grammar points:**
- **破折号插入语** - 中断主句结构，插入评论或补充信息。听力难点。
- **比较级 + than 从句** - more...than we seem to want to make，比较结构嵌套从句。
- **either...or 同位语** - 对 one country 进行具体说明。

### [01:50:22]
**Original:** What I would like is for the democratic nations of the world—those whose governments represent closer to pro-human values—are holding the stronger hand and have more leverage when the rules of the road are set.

**Translation:** 我希望的是，世界上的民主国家——那些政府更能代表亲人类价值观的国家——在制定游戏规则时能掌握更强的筹码和拥有更多的影响力。

**Core structure:**
- What I would like is for the democratic nations are holding the stronger hand when rules are set.  
  我希望民主国家在制定规则时掌握更强筹码。

**Structure tree:**
```
主句: What I would like is...
主语从句: What I would like
表语: for the democratic nations are holding...
  - 插入语: those whose governments represent...
  - 时间从句: when the rules are set
```

**Grammar points:**
- **What 引导主语从句** - What I would like 作主语，表达期望。
- **for + 名词 + to be doing** - for 引导不定式的逻辑主语，但此处用 are holding 形成混合结构。
- **定语从句作插入语** - whose 定语从句补充说明 democratic nations。

### [01:52:50]
**Original:** There are points where if you reach a certain level, maybe you have offensive cyber dominance, and every computer system is transparent to you after that unless the other side has an equivalent defense.

**Translation:** 存在一些临界点，如果你达到某个水平，也许你就拥有了进攻性网络优势，之后每个计算机系统对你来说都是透明的，除非对方拥有相应的防御能力。

**Core structure:**
- There are points where you have cyber dominance and every system is transparent unless the other side has defense.  
  存在临界点，你拥有网络优势，每个系统都透明，除非对方有防御。

**Structure tree:**
```
主句: There are points
定语从句: where if you reach a level...
  - 条件从句: if you reach a certain level
  - 结果1: you have dominance
  - 结果2: every system is transparent
    - 条件从句: unless the other side has defense
```

**Grammar points:**
- **where 引导定语从句** - 修饰抽象名词 points (时间点/情况)。
- **多层条件嵌套** - if 从句嵌套在定语从句中，unless 又修饰后续结果。

### [01:53:04]
**Original:** But I think there will be either a critical moment, a small number of critical moments, or some critical window where AI confers some large advantage from the perspective of national security, and one country or coalition has reached it before others.

**Translation:** 但我认为将会有一个关键时刻、少数几个关键时刻，或者某个关键窗口期，在此期间从国家安全角度来看AI会带来巨大优势，而某个国家或联盟会比其他国家先达到这一点。

**Core structure:**
- I think there will be a critical moment where AI confers advantage and one country has reached it before others.  
  我认为会有关键时刻，AI带来优势，某国先达到。

**Structure tree:**
```
主句: I think...
宾语从句: there will be moment/window
  - either...or 并列: moment / moments / window
  - 定语从句: where AI confers advantage
    - 并列句: and one country has reached it
```

**Grammar points:**
- **either...or 多项并列** - 三个并列选项，表达不确定性。
- **where 定语从句修饰时间** - where 修饰 moment/window，相当于 when。
- **confer 用法** - confer sth (on sb): 授予、赋予。正式用语。

### [01:54:33]
**Original:** That sounds like you're saying the CCP as an institution cannot exist after we get AGI. That seems like a very strong demand, and it seems to imply a world where the leading lab or the leading country will be able to—and by that language, should get to—determine how the world is governed or what kinds of governments are, and are not, allowed.

**Translation:** 这听起来像是你在说,中共作为一个机构在我们获得AGI之后就无法存在了。这似乎是一个非常强烈的要求,而且它似乎暗示着一个世界,在这个世界里,领先的实验室或领先的国家将能够——用这种语言来说,应该有权——决定世界如何被治理,或者什么样的政府是被允许的,什么样的是不被允许的。

**Core structure:**
- That implies a world where the leading lab will be able to determine how the world is governed or what kinds of governments are allowed.  
  这暗示着一个世界,领先的实验室将能够决定世界如何被治理或什么样的政府被允许。

**Structure tree:**
```
main: it seems to imply a world
where clause: where the leading lab will be able to...
interruption: —and by that language, should get to—
parallel objects: determine [how...] or [what kinds...]
```

**Grammar points:**
- **破折号插入语** - —and by that language, should get to— 打断主句,增加说话者的评论
- **并列宾语从句** - determine后接how从句和what从句,用or连接
- **插入式表达 are, and are not** - 强调对比,governments后省略了allowed

### [01:55:13]
**Original:** I was saying, 'Here's a weaker thing that I believe. We have to worry a lot about authoritarians and we should try to check them and limit their power. You could take this much further and have a more interventionist view that says authoritarian countries with AI are these self-fulfilling cycles that are very hard to displace, so you just need to get rid of them from the beginning.'

**Translation:** 我是在说,'这是我相信的一个较弱的观点。我们必须非常担心独裁者,我们应该尝试遏制他们并限制他们的权力。你可以把这个观点推得更远,持有一种更加干预主义的观点,认为拥有AI的独裁国家是这些自我实现的循环,很难被取代,所以你只需要从一开始就摆脱它们。'

**Core structure:**
- You could have a view that says authoritarian countries are cycles that are hard to displace, so you need to get rid of them.  
  你可以持有一种观点,认为独裁国家是难以取代的循环,所以你需要摆脱它们。

**Structure tree:**
```
main: You could have a view
that clause: that says...
indirect speech: authoritarian countries are cycles
relative: that are hard to displace
result: so you need to get rid of them
```

**Grammar points:**
- **嵌套定语从句** - view that says... countries that are hard to displace,两层定语修饰
- **so引导结果状语** - 表示前面观点的逻辑结论

### [01:57:04]
**Original:** I mentioned this in 'Adolescence of Technology', where I said feudalism was basically a form of government, and when we invented industrialization, feudalism was no longer sustainable.

**Translation:** 我在《技术的青春期》中提到过这一点,我在那里说封建主义基本上是一种政府形式,而当我们发明了工业化时,封建主义就不再可持续了。

**Core structure:**
- I mentioned this where I said feudalism was no longer sustainable when we invented industrialization.  
  我提到过这一点,我说当我们发明工业化时,封建主义就不再可持续了。

**Structure tree:**
```
main: I mentioned this
where clause: where I said...
indirect speech 1: feudalism was a form of government
indirect speech 2: when we invented..., feudalism was no longer sustainable
```

**Grammar points:**
- **where引导定语从句** - 指代前面整个文献,相当于in which
- **间接引语中的时间状语从句** - said后接两个并列的that从句(省略),第二个含when从句

### [02:00:49]
**Original:** Could there be developments we can make—either that naturally happen as a result of AI, or that we could make happen by building technology on AI—that create an equilibrium where it becomes infeasible for authoritarian countries to deny their people private use of the benefits of the technology?

**Translation:** 是否可能存在我们可以实现的发展——要么是作为AI的结果自然发生的,要么是我们可以通过在AI基础上构建技术来实现的——这些发展创造了一种平衡,在这种平衡下,独裁国家无法阻止其人民私下使用技术的好处?

**Core structure:**
- Could there be developments that create an equilibrium where it becomes infeasible for countries to deny their people use of the technology?  
  是否可能存在创造一种平衡的发展,在这种平衡下国家无法阻止人民使用技术?

**Structure tree:**
```
main: Could there be developments
relative 1: we can make
insertion: —either that happen or that we make happen—
relative 2: that create an equilibrium
where clause: where it becomes infeasible
for...to structure: for countries to deny
```

**Grammar points:**
- **倒装疑问句 + 存在句** - Could there be置于句首的倒装结构
- **破折号插入的either...or结构** - 打断主干,说明developments的两种可能来源
- **形式主语it + for sb to do** - it作形式主语,真正主语是for countries to deny...

### [02:01:12]
**Original:** Are there equilibria where we can give everyone in an authoritarian country their own AI model that defends them from surveillance and there isn't a way for the authoritarian country to crack down on this while retaining power?

**Translation:** 是否存在这样的平衡:我们可以给独裁国家的每个人提供他们自己的AI模型来保护他们免受监控,而独裁国家没有办法在保持权力的同时镇压这一点?

**Core structure:**
- Are there equilibria where we can give everyone their own AI model and there isn't a way for the country to crack down on this?  
  是否存在这样的平衡:我们可以给每个人AI模型,而国家没有办法镇压这一点?

**Structure tree:**
```
main: Are there equilibria
where clause contains two parallel parts:
  1. we can give everyone... model that defends...
  2. there isn't a way for... to crack down
while clause: while retaining power
```

**Grammar points:**
- **where从句中的并列结构** - where从句包含两个并列分句,用and连接
- **while doing时间状语** - 表示同时发生的动作,省略主语

### [02:01:35]
**Original:** But maybe there's a middle world where there's an equilibrium where, if they want to hold on to power, the authoritarians can't deny individualized access to the technology.

**Translation:** 但也许存在一个中间世界，在那里有一种平衡状态，即如果独裁者想要维持权力，他们就无法拒绝给予个性化的技术访问权限。

**Core structure:**
- Maybe there's a middle world where there's an equilibrium.  
  也许存在一个中间世界，在那里有一种平衡状态。

**Structure tree:**
```
main clause: there's a middle world
where clause 1: where there's an equilibrium
where clause 2: where authoritarians can't deny access
conditional clause: if they want to hold on to power
```

**Grammar points:**
- **双重 where 定语从句** - 两个 where 从句嵌套，第二个进一步限定 equilibrium
- **条件状语从句插入** - if 从句插在主句中间，增加理解难度

### [02:01:50]
**Original:** Is it possible that the technology might inherently have properties—or that by building on it in certain ways we could create properties—that have this dissolving effect on authoritarian structures?

**Translation:** 技术本身是否可能固有某些特性——或者说通过以某些方式在其基础上构建，我们能否创造出某些特性——这些特性对独裁结构具有瓦解作用？

**Core structure:**
- Is it possible that the technology might have properties that have this effect?  
  技术是否可能拥有具有这种作用的特性？

**Structure tree:**
```
main question: Is it possible that...
that clause 1: technology might have properties
that clause 2 (parallel): we could create properties
parenthetical: or that by building...
relative clause: that have this dissolving effect
```

**Grammar points:**
- **破折号插入并列从句** - 破折号内插入平行的 that 从句，打断主句流畅性
- **多层嵌套从句** - 主句包含 that 从句，内含定语从句，层次深
- **might 表推测** - 表示不确定的可能性

### [02:02:01]
**Original:** Now, we hoped originally—think back to the beginning of the Obama administration—that social media and the internet would have that property, and it turns out not to.

**Translation:** 现在，我们最初曾希望——回想一下奥巴马政府初期——社交媒体和互联网会具有那种特性，但结果并非如此。

**Core structure:**
- We hoped that social media would have that property, and it turns out not to.  
  我们曾希望社交媒体会具有那种特性，但结果并非如此。

**Structure tree:**
```
main clause 1: we hoped that...
parenthetical insertion: think back to...
that clause: social media would have that property
main clause 2: it turns out not to
```

**Grammar points:**
- **破折号插入祈使句** - 破折号内插入独立祈使句，中断主句
- **省略结构 (ellipsis)** - turns out not to 后省略了 have that property

### [02:03:43]
**Original:** So when I think about policy, I think that the technology and the market will deliver all the fundamental benefits—this is my fundamental belief—almost faster than we can take them.

**Translation:** 所以当我思考政策时，我认为技术和市场会提供所有基本利益——这是我的基本信念——其速度几乎比我们能够接受它们的速度还要快。

**Core structure:**
- I think that technology will deliver benefits faster than we can take them.  
  我认为技术提供利益的速度比我们接受的速度还快。

**Structure tree:**
```
temporal clause: when I think about policy
main clause: I think that...
that clause: technology will deliver benefits
parenthetical: this is my fundamental belief
comparison: faster than we can take them
```

**Grammar points:**
- **破折号插入评论** - 插入作者个人评论，打断陈述
- **比较级 + than 从句** - faster than we can... 表达速度对比

### [02:05:31]
**Original:** Certainly, during the transition—we can talk about the point where humans have no role—humans will still have some role in starting up these companies and supervising the AI models.

**Translation:** 当然，在过渡期间——我们可以讨论人类完全没有作用的那个时点——人类仍然会在创办这些公司和监督AI模型方面发挥一定作用。

**Core structure:**
- During the transition, humans will still have some role.  
  在过渡期间，人类仍将发挥一定作用。

**Structure tree:**
```
temporal phrase: during the transition
parenthetical: we can talk about the point...
where clause: where humans have no role
main clause: humans will still have some role
prepositional phrases: in starting up... and supervising...
```

**Grammar points:**
- **破折号插入完整子句** - 插入关于未来某个时点的讨论，形成对比
- **where 引导定语从句** - 修饰 the point，表示抽象地点

### [02:06:58]
**Original:** In other words, if you give it a list of rules—"don't tell people how to hot-wire a car, don't speak in Korean"—it doesn't really understand the rules, and it's hard to generalize from them.

**Translation:** 换句话说，如果你给它一系列规则——"不要告诉人们如何偷车，不要说韩语"——它并不真正理解这些规则，而且很难从中归纳推广。

**Core structure:**
- If you give it a list of rules, it doesn't understand them, and it's hard to generalize.  
  如果你给它一系列规则，它不理解它们，而且很难归纳。

**Structure tree:**
```
conditional clause: if you give it a list of rules
parenthetical insertion: examples in dashes
main clause 1: it doesn't understand the rules
coordinating conjunction: and
main clause 2: it's hard to generalize from them
```

**Grammar points:**
- **破折号插入语** - 破折号中间插入具体例子，打断句子主干，增加理解难度
- **并列复合句** - 两个独立主句用and连接，需同时处理条件从句和两个结果

### [02:07:51]
**Original:** How much should the model be a kind of "skin suit" where it just directly follows the instructions given to it by whoever is giving those instructions, versus how much should the model have an inherent set of values and go off and do things on its own?

**Translation:** 模型应该在多大程度上成为一种"皮囊"，直接遵循任何指令给予者的指示，相对于模型应该在多大程度上拥有一套内在价值观并自主行动？

**Core structure:**
- How much should the model follow instructions versus how much should it have its own values?  
  模型应该在多大程度上遵循指令，相对于在多大程度上拥有自己的价值观？

**Structure tree:**
```
parallel structure: How much... versus how much...
clause 1: model should be a "skin suit" where...
relative clause: where it follows instructions given by whoever...
clause 2: model should have values and go off and do things
```

**Grammar points:**
- **versus 平行结构** - 两个How much问句通过versus对比，结构对称但内容复杂
- **where引导定语从句** - 修饰抽象概念"skin suit"，增加一层嵌套
- **whoever引导名词性从句** - whoever = anyone who，表示任何给出指令的人

### [02:09:25]
**Original:** Normally, a constitution is written down, set in stone, and there's a process of updating it and changing it and so forth. In this case, it seems like a document that people at Anthropic write, that can be changed at any time, that guides the behavior of systems that are going to be the basis of a lot of economic activity.

**Translation:** 通常，一部宪法被写下来、定型，并有一个更新和修改的流程等等。在这种情况下，它似乎是一份由Anthropic员工撰写的文件，可以随时更改，指导那些将成为大量经济活动基础的系统的行为。

**Core structure:**
- It seems like a document that people write, that can be changed, that guides systems.  
  它似乎是一份人们撰写的、可以更改的、指导系统的文件。

**Structure tree:**
```
main clause: it seems like a document
relative clause 1: that people at Anthropic write
relative clause 2: that can be changed at any time
relative clause 3: that guides the behavior of systems
nested relative: that are going to be the basis...
```

**Grammar points:**
- **连续定语从句** - 三个that从句连续修饰document，层层限定，听觉上难以分清边界
- **嵌套定语从句** - 最后一个that从句内部还有that修饰systems，形成双层嵌套

### [02:12:00]
**Original:** But there's no reason you couldn't, in principle, say, "All AI models have to have a constitution that starts with these things, and then you can append other things after it, but there has to be this special section that takes precedence."

**Translation:** 但原则上，没有理由你不能说："所有AI模型必须有一部宪法，以这些内容开头，然后你可以在后面附加其他内容，但必须有这个优先的特殊部分。"

**Core structure:**
- There's no reason you couldn't say that AI models have to have a constitution.  
  没有理由你不能说AI模型必须有宪法。

**Structure tree:**
```
main clause: there's no reason you couldn't say...
parenthetical: in principle
object clause: "All AI models have to have..."
nested: constitution that starts with...
coordination: and then... but there has to be...
final relative: section that takes precedence
```

**Grammar points:**
- **双重否定** - no reason you couldn't = you could，双重否定表肯定但增加理解负担
- **引语内的复杂结构** - say后面的引语本身是个包含多个从句和转折的长句
- **插入语in principle** - 打断主干，需暂时搁置再继续理解

### [02:12:13]
**Original:** That's too rigid and sounds overly prescriptive in a way that I think overly aggressive legislation is.

**Translation:** 那太僵化了，听起来过于规定性，我认为过于激进的立法就是这样的。

**Core structure:**
- That's too rigid and sounds overly prescriptive in a way that legislation is.  
  那太僵化，听起来过于规定性，就像立法那样。

**Structure tree:**
```
parallel predicates: That's rigid and sounds prescriptive
manner phrase: in a way that...
relative clause: that overly aggressive legislation is
ellipsis: is [overly prescriptive in that way]
```

**Grammar points:**
- **方式状语从句** - in a way that引导方式状语，描述prescriptive的具体表现方式
- **省略结构** - legislation is后省略了前面提到的overly prescriptive in that way，需回溯理解
- **插入I think** - 在定语从句中插入主观判断，增加处理层次

### [02:12:55]
**Original:** But you have a vision of competition between constitutions, which is actually very reminiscent of how some libertarian charter cities people used to talk about what an archipelago of different kinds of governments would look like.

**Translation:** 但你有一个关于宪法之间竞争的愿景,这实际上非常让人想起一些自由主义特许城市的倡导者过去常常谈论的,关于由不同类型政府组成的群岛会是什么样子。

**Core structure:**
- You have a vision which is reminiscent of how people talked about governments.  
  你有一个愿景,让人想起人们如何谈论政府。

**Structure tree:**
```
main: you have a vision
modifier: of competition between constitutions
relative clause: which is reminiscent of...
nested clause: how people used to talk about...
nested question: what an archipelago would look like
```

**Grammar points:**
- **非限制性定语从句** - which 引导,补充说明 vision
- **how 引导宾语从句** - 表示方式,作 of 的宾语
- **what 引导宾语从句** - 嵌套在 about 后,表示内容

### [02:14:24]
**Original:** When people look back, it will—It'll be hard for them to put themselves in the place of people who were actually making a bet on this thing to happen that wasn't inevitable, that we had these arguments like the arguments I make for scaling or that continual learning will be solved.

**Translation:** 当人们回顾时,他们将很难把自己放在那些实际上在为这件事发生下注的人的位置上,而这件事并非必然发生,我们有这些争论,比如我为规模化提出的论点,或者持续学习将被解决的论点。

**Core structure:**
- It'll be hard for them to put themselves in the place of people who were making a bet.  
  他们将很难把自己放在那些下注的人的位置上。

**Structure tree:**
```
main: It'll be hard for them to put themselves...
place modifier: in the place of people
relative clause: who were making a bet
modifier: on this thing to happen
relative clause: that wasn't inevitable
appositive clause: that we had these arguments...
```

**Grammar points:**
- **形式主语 it** - 真正主语是 to put themselves...
- **多重 that 从句** - 第一个修饰 thing,第二个作同位语说明 arguments

### [02:15:33]
**Original:** Decisions that you might think were carefully calculated, well actually you have to make that decision, and then you have to make 30 other decisions on the same day because it's all happening so fast.

**Translation:** 你可能认为经过仔细计算的决策,实际上你必须做出那个决定,然后你还必须在同一天做出其他30个决定,因为一切都发生得太快了。

**Core structure:**
- You have to make that decision and then make 30 other decisions because it's happening fast.  
  你必须做出那个决定,然后做出30个其他决定,因为一切发生得很快。

**Structure tree:**
```
fragment: Decisions that you might think were calculated
main: you have to make that decision
coordinate: and then you have to make 30 other decisions
reason: because it's all happening so fast
```

**Grammar points:**
- **省略句** - 开头是独立片段,与主句形成对比
- **think + 宾语从句省略 that** - you might think (that) they were calculated

### [02:16:35]
**Original:** It seems like you have managed to build a role for yourself and a company around you which is compatible with this more intellectual-type role of CEO.

**Translation:** 看起来你成功地为自己构建了一个角色,并在你周围建立了一家公司,这与这种更偏知识分子型的CEO角色是兼容的。

**Core structure:**
- You have managed to build a role and a company which is compatible with this role.  
  你成功地构建了一个角色和一家公司,这与这种角色兼容。

**Structure tree:**
```
main: It seems like...
that clause: you have managed to build...
parallel objects: a role / a company
modifier: for yourself / around you
relative clause: which is compatible with this role
```

**Grammar points:**
- **seem like + 从句** - 表推测,从句省略 that
- **并列结构** - a role for yourself and a company around you

### [02:17:41]
**Original:** I try as much as possible, but one thing that's very leveraged is making sure Anthropic is a good place to work, people like working there, everyone thinks of themselves as team members, and everyone works together instead of against each other.

**Translation:** 我尽可能多地尝试,但有一件杠杆作用很大的事情是,确保Anthropic是一个好的工作场所,人们喜欢在那里工作,每个人都把自己当作团队成员,每个人一起合作而不是互相对抗。

**Core structure:**
- One thing is making sure Anthropic is good, people like working there, everyone thinks of themselves as members, and everyone works together.  
  一件事是确保Anthropic很好,人们喜欢在那里工作,每个人都把自己当作成员,每个人一起合作。

**Structure tree:**
```
main: one thing is making sure...
gerund phrase: making sure (that)...
parallel clauses: Anthropic is good / people like working / everyone thinks / everyone works
```

**Grammar points:**
- **动名词短语作表语** - making sure... 说明 one thing 的内容
- **多重并列从句** - make sure 后接4个并列的宾语从句

### [2:18:44]
**Original:** But I think an important thing in the culture is that the other leaders as well, but especially me, have to articulate what the company is about, why it's doing what it's doing, what its strategy is, what its values are, what its mission is, and what it stands for.

**Translation:** 但我认为文化中一个重要的事情是，其他领导者以及特别是我，必须阐明公司是关于什么的，为什么做它正在做的事情，它的策略是什么，它的价值观是什么，它的使命是什么，以及它代表什么。

**Core structure:**
- An important thing is that leaders have to articulate what the company is about.  
  一个重要的事情是领导者必须阐明公司是关于什么的。

**Structure tree:**
```
main: I think [that]...
predicative clause: that... leaders have to articulate...
parenthetical: as well, but especially me
object: 6 parallel 'what' clauses (what the company is about / why it's doing... / what its strategy is...)
```

**Grammar points:**
- **多重宾语从句并列** - articulate 后接6个并列的 what/why 引导的宾语从句，用逗号和 and 连接
- **插入语** - as well, but especially me 插在主语和谓语之间，增加理解难度

### [2:19:38]
**Original:** So I get up in front of the company every two weeks. I have a three or four-page document, and I just talk through three or four different topics about what's going on internally, the models we're producing, the products, the outside industry, the world as a whole as it relates to AI and geopolitically in general.

**Translation:** 所以我每两周在全公司面前站起来发言。我有一份三到四页的文件，我只是讲解三到四个不同的主题，关于内部正在发生的事情、我们正在生产的模型、产品、外部行业、整个世界（因为它与人工智能和一般地缘政治相关）。

**Core structure:**
- I talk through topics about what's going on.  
  我讲解关于正在发生的事情的主题。

**Structure tree:**
```
main: I talk through topics about...
object 1: what's going on internally
object 2-3: the models [we're producing], the products
object 4: the outside industry
object 5: the world [as a whole] [as it relates to AI and geopolitically]
```

**Grammar points:**
- **同位语并列扩展** - about 后接多个并列同位语短语，逐层扩展话题范围
- **嵌套修饰** - the world 后有两个 as 短语修饰，as a whole（整体上）和 as it relates to...（因为它与...相关）

### [2:20:56]
**Original:** The point is to get a reputation of telling the company the truth about what's happening, to call things what they are, to acknowledge problems, to avoid the sort of corpo speak, the kind of defensive communication that often is necessary in public because the world is very large and full of people who are interpreting things in bad faith.

**Translation:** 重点是要获得向公司讲述正在发生的事情的真相的声誉，直呼其名，承认问题，避免那种公司式语言，那种在公共场合经常必要的防御性沟通，因为世界非常大，充满了恶意解读事情的人。

**Core structure:**
- The point is to get a reputation and to avoid defensive communication.  
  重点是获得声誉并避免防御性沟通。

**Structure tree:**
```
main: The point is to...
4 parallel infinitives: to get / to call / to acknowledge / to avoid
appositive: the kind of communication [that is necessary]
cause clause: because the world is large and full of people [who...]
```

**Grammar points:**
- **不定式短语并列** - 4个 to do 结构并列作表语，表达多个目标
- **同位语 + 定语从句** - corpo speak 和 defensive communication 同位，后接 that 从句修饰，再用 because 从句说明原因

### [2:21:33]
**Original:** It makes it a better place to work, it makes people more than the sum of their parts, and increases the likelihood that we accomplish the mission because everyone is on the same page about the mission, and everyone is debating and discussing how best to accomplish the mission.

**Translation:** 它使这里成为一个更好的工作场所，它使人们的价值大于各部分的总和，并增加我们完成使命的可能性，因为每个人对使命都有共识，每个人都在辩论和讨论如何最好地完成使命。

**Core structure:**
- It makes it a better place, makes people more, and increases the likelihood.  
  它使这里成为更好的地方，使人们更强，并增加可能性。

**Structure tree:**
```
3 parallel main clauses: it makes... / it makes... / [it] increases...
appositive clause: that we accomplish the mission
cause: because everyone is on the same page...
parallel cause details: everyone is debating and discussing...
```

**Grammar points:**
- **并列谓语省略** - 第三个分句省略了主语 it，三个动词 makes/makes/increases 并列
- **同位语从句** - that we accomplish the mission 解释说明 likelihood 的具体内容
- **习语** - on the same page 表示'意见一致、有共识'；more than the sum of their parts 表示'整体大于部分之和'