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第 29 期 · 核心追问 Ⅰ·05「整体能大于部分之和吗？」
“AI and Our Economic Future” with Professor Chad Jones
节目发布 2026-05-21章节 · 点击跳转视频
0:05
开场：AI 与历代变革性技术
1:58
情景一：硅谷式增长爆炸
6:43
情景二：150 年不变的 2% 增长
11:05
薄弱环节：链条只强于最弱一环
14:16
电脑份额下降：稀缺者拿走蛋糕
17:31
建模：无限软件只让 GDP 富 2%
21:17
模拟结果：会爆炸，但慢得惊人
31:07
就业：放射科医生 vs. Uber 司机
33:36
不平等、意义感与富足世界
36:30
灾难性风险：下行来得更快
42:06
问答：GDP 测量与短期分配冲击
50:36
问答：资本集中、职业建议与全球视角
57:34
问答：DeepMind 员工质疑渐进假设
本期小问 · 档案清单
31:07
人类还是特殊的吗？
21:17
AI 会引爆经济增长，还是只延续 2% 的常态？
11:05
为什么单点的技术突破无法撬动整体？
36:30
我们要为未来负责吗？
本期讲者
查尔斯·琼斯斯坦福大学商学院经济学教授，增长理论领域权威，与 Bloom 等合著《Are Ideas Getting Harder to Find?》，著有经典教材《Introduction to Economic Growth》。本场为斯坦福 GSB 校友返校日讲座。
罗伯托·桑塔纳斯坦福 GSB 2011 届校友，就职于 Google DeepMind，在问答环节提出对模型「渐进假设」的核心质疑。
01开场：AI 与历代变革性技术
0:05
[DARON ACEMOGLU] Really, really happy to have a chance to share this talk with you. This is something that's been on all of our minds. My research for the last 15 years and longer has been on economic growth, and I feel like AI is this incredible new technology and how it shapes the future and what kind of consequences it may have for us and our children is something I think all of us are thinking about every day. So this is based on I think four or five research papers that I've been working on over the last couple of years.
[达伦·阿西莫格鲁] 非常非常高兴有机会跟大家分享这个演讲。这是我们所有人都在思考的一件事。我过去15年甚至更长时间的研究一直是关于经济增长的，而我觉得AI是这样一项令人惊叹的新技术，它如何塑造未来、可能给我们和我们的孩子带来什么样的后果，我想这是我们每个人每天都在思考的问题。所以这个演讲是基于我过去几年在做的大概四五篇研究论文。
0:37
So... I think it's an easy statement that AI is likely to be the most transformative technology of our lifetime. Importantly, it's the latest in a line of transformative technologies, right? So, electricity, the transistor, semiconductors, information technology, the internet are all transformative technologies. And a question I've been pondering, and I'll get to this in a little bit, is, you know, to what extent is AI different and to what extent does it share features with these earlier transformative technologies?
那么……我认为有一点很容易达成共识：AI很可能是我们这辈子最具变革性的技术。重要的是，它是一系列变革性技术中最新的一项，对吧？比如电力、晶体管、半导体、信息技术、互联网，这些都是变革性技术。而我一直在思考的一个问题——待会儿我会讲到——就是，AI在多大程度上是不同的，又在多大程度上跟这些早期的变革性技术有共同之处？
1:13
And I think in terms of the difference, the next bullet point, which Sarah echoed a little bit, I think really gets to the heart of it. What if machines- AI for cognitive work, and then AI running robots for physical work. What if they can perform every task a human can do? What does it look like to live in that world? And to start out, I want to lay out for you two scenarios that I think of as two extremes. Neither one is Probably what's going to happen. They're kind of caricatures in a way, but I think we learn something by thinking about these two scenarios, and then I'll sort of show you some research I've been doing to help me think about where in between these two extremes we might end up.
我觉得说到差别，下一个要点——莎拉刚才也稍微提到了——我认为真正切中了要害。如果机器——用AI做认知性的工作，再用AI来驱动机器人做体力工作。如果它们能完成人类能做的每一项任务呢？生活在那样一个世界里会是什么样子？作为开场，我想给大家勾勒两种情景，我把它们看作两个极端。这两种情况可能都不是真正会发生的。某种意义上它们有点像漫画式的夸张，但我认为思考这两种情景能让我们学到一些东西，然后我会给大家看一些我做的研究，帮助我思考我们最终可能落在这两个极端之间的什么位置。
02情景一：硅谷式增长爆炸
1:58
Okay, so the first scenario is going to be AI dramatically accelerates economic growth. The FOOM scenario of Silicon Valley, which you know we read about practically every day. The second scenario is where AI is just a normal technology. AI is business as usual. And so, you know, maybe it's normal the way electricity and semiconductors and the internet were normal. They were transformative technologies, but yeah, so we'll see. So let me dive into these two scenarios. So in the first scenario... And I should say, my plan is to talk until 5:45 and then take 15 minutes of questions.
好，第一种情景是AI极大地加速经济增长。也就是硅谷的那个“FOOM”情景，我们基本上每天都能读到。第二种情景是AI只是一项普通的技术。AI一切照旧。那么，你知道，也许它的“普通”就像电力、半导体和互联网当年那样普通。它们是变革性技术，但，嗯，我们走着瞧。那让我深入讲讲这两种情景。那么在第一种情景里……我应该说明一下，我的计划是讲到5点45，然后留15分钟提问。
2:36
So hopefully that'll work well, and I got lots of clocks here to keep me on time. So AI dramatically accelerating growth. I think this one we're in the middle of watching now. So, you know, Dario Amodei, Sam Altman, Demis Hassabis, Geoff Hinton, sort of the luminaries of AI, have been saying for the last 10 years. That these things are coming, and we're kind of marching along the schedule that they laid out for us 10 years ago. The first part of that schedule, I think, is AI automating software, right?
希望这个能顺利运行，我这儿摆了好多个钟表，好让自己不超时。所以说，AI 正在大幅加速经济增长。我觉得这一幕我们现在正处在其中，正在亲眼见证。你知道，Dario Amodei、Sam Altman、Demis Hassabis、Geoff Hinton，这些算是 AI 界的泰斗人物，过去十年一直在说这些话。说这些东西就要来了，而我们差不多正沿着他们十年前给我们描绘的时间表在往前走。我认为这个时间表的第一步，是 AI 实现软件的自动化，对吧？
3:08
AI automating software engineering. And we saw back in November, I think, when Claude Opus 4.5 was released, Anthropic took this model, and whenever they're trying to hire a software engineer, they give the software engineers a two-hour take-home exam, and they see how they do, and that's part of how they decide who to hire. They gave this same two-hour exam to Opus 4.5, and it scored higher than any human in history. Right. That was already seven months ago. And the models have only gotten better.
AI 把软件工程自动化。我记得去年十一月，Claude Opus 4.5 发布的时候我们看到，Anthropic 拿这个模型做了个测试——他们每次要招软件工程师的时候，会给应聘者一份两小时的带回家做的笔试题，看他们做得怎么样，这是他们决定录用谁的依据之一。他们把同一份两小时的题目给了 Opus 4.5，结果它的得分比历史上任何一个人类都高。对吧。那已经是七个月前的事了。而这些模型只会越来越强。
3:40
We're up to Claude Opus 4.7 now, right? Two generations later. In the next decade, is it plausible that we'll have AI agents that can automate most coding? Yeah, maybe that's next week. This seems like it's coming very soon, but maybe it's a decade. Okay, when you have AI agents that can do everything a software engineer can do— Well, you put them to work doing more things. In particular, you put them to work on AI research, build better algorithms to improve the AI itself and build agents that can use a computer the way a human can use a computer.
我们现在都到 Claude Opus 4.7 了，对吧？已经又过了两代。在未来十年里，我们有可能拥有能把大部分编程工作自动化的 AI 智能体吗？是啊，也许下周就有了。看起来这事很快就要发生了，但也可能还要十年。好，那么当你有了能做软件工程师所有工作的 AI 智能体之后——那你就会让它们去做更多的事。特别是，你会让它们去做 AI 研究，去开发更好的算法来改进 AI 本身，去打造那种能像人一样操作电脑的智能体。
4:16
Right? And so again, shortly after that, it seems plausible that we'll have agents that can function as virtual remote workers. Anything you could call up a colleague on a virtual Zoom call and ask them to do, the colleague could be an AI rather than a human. Right? Once you have that, and again shortly after maybe, well, you can scale these things up on millions of GPUs, right? And you can end up with billions of virtual research assistants, each running 100 times faster than we run. And you put these to work, this country of geniuses in a data center, as Dario Amodei called them, you put them to work discovering new ideas.
对吧？所以同样地，在那之后不久，看起来我们很有可能拥有能充当虚拟远程员工的智能体。任何你能打个视频电话、在 Zoom 上拜托同事去做的事情，这个同事可以是 AI，而不是人类。对吧？一旦你有了这个，同样地也许过不了多久，你就可以把它们扩展到几百万块 GPU 上，对吧？于是你最终会拥有几十亿个虚拟研究助理，每一个的运行速度都比我们快一百倍。然后你让它们去干活，就是 Dario Amodei 所说的那个「数据中心里的天才之国」，你让它们去发现新想法。
4:58
Right? You tell them, help me design better computer chips. Simulate the real world and help me design better robots. So we can finally get the grippers that are just sort of the bottleneck for robotics. Help us design better technologies, new pharmaceuticals. AlphaFold was nearly 10 years ago now, and it's transforming pharmaceuticals, but there's so much more that can be done, right? If you've got this country of geniuses in a data center What virtual cognitive tasks can they not do? Well, once they're designing better robots in virtual reality, we test them out in the real world.
对吧？你告诉它们，帮我设计更好的计算机芯片。模拟真实世界，帮我设计更好的机器人。这样我们终于能搞定机械手爪这个机器人技术的瓶颈了。帮我们设计更好的技术，新的药物。AlphaFold 到现在快十年了，它正在彻底改变制药行业，但还有太多可以做的事情，对吧？如果你有了这个数据中心里的天才之国，还有什么虚拟的认知任务是它们做不了的？那么，一旦它们开始在虚拟现实里设计更好的机器人，我们就在真实世界里测试这些机器人。
5:35
And again, eventually, maybe it takes a decade, but eventually we have robots run by these super geniuses in a data center, and then we've automated physical tasks as well. And once you automate cognitive tasks and physical tasks, yeah, in the growth models that I like, that I wrote down, that I taught many of you, growth explodes So this explosive growth is something that absolutely can happen if this story is right, and it's not obvious where this story breaks down. So this is a scenario that I think is one that's very popular in Silicon Valley now.
再一次，最终——也许要花十年——但最终我们会有由数据中心里这些超级天才操控的机器人，然后我们就把物理性的工作也自动化了。而一旦你把认知任务和物理任务都自动化了，那么，是的，在我喜欢的、我自己写下来的、也教给你们很多人的那些增长模型里，增长会爆炸式上升。所以如果这个故事是对的，这种爆炸式增长是绝对可能发生的，而且也看不出这个故事会在哪一步崩掉。所以我觉得，这是目前在硅谷非常流行的一种设想。
6:12
It's totally plausible. There's a question of the horizon. Does it happen in three years or five years, like the AI 2027 people, or Leopold Aschenbrenner's situational awareness? Or does it happen in 25 years? Well, in some sense, if growth is exploding and accelerating- You know, five years versus 25 years is still transforming the world. Okay, so that's a scenario that's very familiar in Silicon Valley. I do think it's plausible and has some merits, but it's certainly not guaranteed that this is the way things happen.
它完全是说得通的。问题在于时间跨度。它是三年内发生、五年内发生，像 AI 2027 那帮人或者 Leopold Aschenbrenner 的《态势感知》里说的那样？还是要 25 年才发生？不过某种意义上说，如果增长在爆炸、在加速——你知道，五年还是 25 年，都一样会改变整个世界。好，这就是硅谷非常熟悉的一种设想。我确实认为它有道理、也说得通，但事情一定会这样发展，这可绝对不是板上钉钉的。
03情景二：150 年不变的 2% 增长
6:43
Let me give you a scenario at the other extreme. AI is just sort of a business-as-usual technology. And here I think the story goes the following way. Let me show you a graph. And again, if you ever took my class, you saw this graph 37 times, so apologies, but now you get to see the updated version. It's changed so much relative to when you took the class, right? Okay, so what is this? This is average living standards in the United States. Real income per person, 150 years on a logarithmic or a ratio scale, right?
让我给你们讲一个处在另一个极端的设想。AI 只不过是一项照常发展的普通技术而已。在这种情况下，我觉得故事是这样讲的。我给你们看一张图。同样地，如果你上过我的课，这张图你已经看过 37 遍了，所以先说声抱歉，不过现在你能看到更新版了。相比你们上课那会儿，它变了好多啊，对吧？好，那这是什么呢？这是美国的平均生活水平。人均实际收入，150年的数据，用对数刻度或者说比例刻度来看，对吧？
7:19
And what you see is you never get too far away from this straight line with a slope of 2% per year. Living standards in the United States for 150 years have risen at 2% per year plus or minus a little bit. Okay? Now, what's especially interesting about that is when you reflect on the transformative technologies that entered and diffused throughout the US economy during this period, right? In 1870, the electrical transformation of the US economy was just a glimmer in Thomas Edison's eye. Over the next 50 years, this transformation happened.
你会发现，数据从来没有偏离这条斜率为每年2%的直线太远。150年来，美国的生活水平一直以每年2%左右的速度增长，上下浮动不大。好吧？现在，特别有意思的是，你回想一下在这段时期内，进入美国经济并在其中扩散开来的那些变革性技术，对吧？1870年的时候，美国经济的电气化革命还只是托马斯·爱迪生眼中的一丝火花。在接下来的50年里，这场变革发生了。
7:59
Internal combustion engines, jet airplanes, right? Antibiotics, vacuum tubes, transistors, semiconductors, Information technology, the internet, all of these amazing technologies that absolutely transformed living standards, and yet the growth rate was always 2% per year. So this raises a big puzzle, I think. How is it, how can it simultaneously be true that these technologies were? Wildly transformative, and yet growth rates still 2%. Well, the answer, if you think about it, is that what is the counterfactual? Right?
内燃机、喷气式飞机，对吧？抗生素、真空管、晶体管、半导体，信息技术、互联网，所有这些彻底改变了生活水平的惊人技术，然而增长率却始终是每年2%。所以我觉得，这就带来了一个大难题。怎么会这样呢？这两件事怎么可能同时成立：这些技术具有翻天覆地的变革性，但增长率却依然是2%。其实，如果你仔细想想，答案在于：反事实的情况是什么？对吧？
8:43
I think the way technology works is that within any technology class, ideas get harder to find. The steam engine runs out of steam. And so that if all you had was the steam engine and you didn't discover electricity, growth would have slowed. Or if all you had is electricity and you didn't discover internal combustion engines and semiconductors, growth would have slowed. And so the straight line would have bent, the arc would have bent here. And instead, what each of these technologies did is they allowed 2% growth to continue for another 50 years.
我认为技术的运作方式是，在任何一个技术门类内部，新想法会越来越难找到。蒸汽机会用尽它的蒸汽。所以如果你只有蒸汽机，而没有发现电，增长就会放缓。或者如果你只有电，而没有发明内燃机和半导体，增长也会放缓。那样的话，这条直线就会拐弯，这条曲线会在这里向下弯。而实际上，这些技术每一项所做的，是让 2% 的增长又延续了 50 年。
9:19
Right? And sort of the pessimistic, in quotes, scenario about AI is maybe it's the latest transformative technology that lets 2% growth continue for another 50 years. Okay, so you can see how these are two very different extremes. I think they both have a lot of merit. One other point I'll say about sort of a lesson from economic history, and you see this a little bit in the discussion I just gave. Economists who've studied how steam power got replaced by electric power or how information technology diffused throughout the economy have highlighted repeatedly that these transformations take decades.
对吧？关于 AI，那种所谓“悲观”的情景是：也许它只是最新一项变革性技术，让 2% 的增长再延续 50 年。好，所以你能看出这是两个非常不同的极端。我觉得这两种看法都很有道理。关于经济史给我们的启示，我还想说另外一点，其实我刚才的讨论里已经稍微提到了一些。那些研究蒸汽动力如何被电力取代、或者信息技术如何在整个经济中扩散的经济学家们，反复强调过一点，那就是：这些转型需要几十年的时间。
10:01
You have to reorganize the factory when you go from steam power to electric motors, right? Instead of one shaft through the middle of the factory, you can now put motors everywhere. Right? Or information technology, right? You have all these complementary innovations that need to be made, right? You invent the spreadsheet, you invent the word processor, you invent the database, you invent SQL, right? And so you have lots of complementary innovations that need to be integrated and production needs to be reorganized.
当你从蒸汽动力转向电动机时，你得重新改造整个工厂的布局，对吧？工厂里不再只有一根贯穿中间的传动轴，你现在可以到处安装电机。对吧？或者说信息技术，对吧？你需要有一整套配套的创新，对吧？你发明了电子表格，发明了文字处理软件，发明了数据库，发明了 SQL，对吧？所以有大量配套创新需要被整合进来，而且生产方式需要重新组织。
10:31
That takes a time that's measured in decades. So again, this sort of lesson from economic history is don't be so quick to assume transformative technologies bend the curve here. They're bending it relative to growth slowing down, and it may take a while. Those are the lessons. Okay. But still, we've got this question. Okay, both of these scenarios have plausible aspects to them. Where are we gonna be in between, and how should we think about that? And that's the question I've been thinking about for the last year and a half or so.
这个过程要以几十年为单位来衡量。所以，经济史给我们的这个教训是：别急着断定变革性技术会在这里让曲线拐弯。它们确实相对于放缓的增长在把曲线往上掰，但这可能需要一段时间。这就是那些教训。好。但我们还是有这个问题。好吧，这两种情形都有各自说得通的地方。那我们最后会落在中间的什么位置，我们又该怎么思考这件事？这就是我过去一年半左右一直在思考的问题。
04薄弱环节：链条只强于最弱一环
11:05
And the concept that I've come up with that I think is really helpful. Is weak links, right? A chain is only as strong as its weakest link. And let me give you the application of that to business, right? Business success requires completing many, many tasks successfully. Right? So if you want to release a new version of the iPhone, you have to design it. You have to source all the parts. You have to get it manufactured. You have to make sure the manufacturing is within very, very exact tolerances in every place.
我想出来的一个我觉得非常有帮助的概念，就是薄弱环节，对吧？一条链子的强度取决于它最薄弱的那一环。我来讲讲这个概念在商业上的应用，对吧？商业上的成功需要成功完成非常非常多的任务。对吧？所以如果你想发布新一代 iPhone，你得设计它。你得采购所有零部件。你得把它生产出来。你得确保制造过程在每一个环节都控制在极其精确的公差范围内。
11:44
You get one thing out of place, the whole thing falls apart, right? Once you've manufactured it, you have to figure out how to get it delivered in a timely fashion. Hundreds of millions of iPhones delivered every fall. You have to handle the retail, the advertising, et cetera, et cetera. If any of these tasks falls down— Then a lot of the value gets lost, at least in the short term. Okay, and that's what a weak link model is, right? And so the Space Shuttle Challenger explodes because a $25 rubber seal, the rubber O-ring, fails.
有一个地方出了偏差，整件事就崩了，对吧？造出来之后，你还得想办法按时把它交付出去。每年秋天要交付上亿部 iPhone。你还得处理零售、广告，等等等等。如果这些任务中的任何一环掉链子——那么很大一部分价值就损失掉了，至少在短期内是这样。好，这就是所谓的“薄弱环节模型”，对吧？所以“挑战者号”航天飞机爆炸，是因为一个25美元的橡胶密封件——那个橡胶O型圈——失效了。
12:19
One small part fails. Or you think about ASML and TSMC, the manufacturer of these computer chips, with machines that are just incredibly complicated, with precisions that are just incredibly tight Right? One little error there and your chips don't work, right? And so how does this help us think about these two scenarios? Well, again, back to the chain. If you've got a chain with 20 links and you make 17 of them really, really strong, that helps, but it doesn't in the end really change the overall strength of the chain because there are always three more weakest links
一个小零件失效了。或者你想想 ASML 和台积电，也就是这些电脑芯片的制造方，他们用的机器复杂得难以想象，精度要求也紧到不可思议，对吧？那里出一点点差错，你的芯片就没法用了，对吧？那这对我们思考这两种情景有什么帮助呢？嗯，还是回到那条链条上。如果你有一条20环的链条，你把其中17环做得非常非常结实，这当然有帮助，但最终并不会真正改变整条链条的强度，因为总还有另外三个最薄弱的环节。
13:03
And so let me give you an example of that that I find really, really helpful. In your pocket, in my pocket, we have a computer with 100 million times the transistors than the equivalent of us had in the 1970s. Okay, so in your pocket, you have 100 million times the transistors than the equivalent of you had in the 1970s, right? I'm not 100 million times more productive at research, right? Why not? Well, my computer can invert matrices like nobody's business, but I have to figure out what data to put in those matrices, or I have to figure out what questions to ask, what theory to test, right?
那我举一个我觉得特别特别有启发的例子。在你的口袋里、在我的口袋里，都装着一台电脑，它的晶体管数量是1970年代同等设备的一亿倍。好，也就是说，你口袋里的晶体管数量是1970年代同等设备的一亿倍，对吧？可我做研究的生产率并没有提高一亿倍，对吧？为什么没有呢？因为我的电脑求矩阵逆矩阵快得没话说，但我还得想清楚该往这些矩阵里放什么数据，或者我得想清楚该问什么问题、该检验什么理论，对吧？
13:41
It's a weak link problem, right? You could be really good at one thing. Instead of 100 million times more productive, maybe I'm two or three times more productive. That's great, but I'm limited by the other weak links that haven't changed, right? So this is, I think, a really important insight about how the world works, and it's gonna help us understand where we are in these two scenarios. Let me highlight one other thing that I'll come back to shortly. Weak links are the source of scarcity. One of the key lessons of economics is scarcity is what gives rise to high returns, right?
这就是一个薄弱环节问题，对吧？你可以在某一件事上做得非常好。但生产率提高的不是一亿倍，也许只是两三倍。这已经很棒了，但我仍然受制于那些没有改变的其他薄弱环节，对吧？所以我认为，这是关于世界如何运转的一个非常重要的洞见，它能帮我们理解在这两种情景中我们究竟处在什么位置。让我再强调另一点，稍后我会回过头来讲。薄弱环节是稀缺性的来源。经济学的一个关键教训是：稀缺性才会带来高回报，对吧？
05电脑份额下降：稀缺者拿走蛋糕
14:16
What is the scarce factor? What is the weak link? Is a question we should always be asking when we're studying some economic phenomenon. And so when we come back to what's going to happen to the income of our kids. Thinking about this, what is scarce, I think is a very helpful framing for that question. Okay. One more graph that I like a lot. So again, if you took my class, you may remember this. Economists are infatuated with the question, who gets GDP? Who gets paid? Right? What share of GDP is paid to labor versus is paid as a return to capital?
什么是稀缺要素？什么是薄弱环节？这是我们研究任何经济现象时都应该不断追问的问题。所以当我们回到“我们孩子的收入会怎样”这个问题时，我认为“什么是稀缺的”这个思路，是一个非常有帮助的框架。好。再讲一张我很喜欢的图。还是那句话，如果你上过我的课，可能还记得这个。经济学家特别着迷于一个问题：GDP 归谁？谁拿到了报酬？对吧？GDP 中有多少份额支付给劳动，多少作为资本回报？
14:56
Turns out for 75 years, it was remarkably stable. Two-thirds went to labor, one-third went to capital. Okay? In the last 25 years, interestingly, the share paid to labor has fallen by about 10%, share paid to capital has gone up, and economists are, obsessed with understanding this. We have two theories. One is automation. I'll come back to that in a second, and the other is market power, concentration. Maybe big firms are bigger and they're exercising more market power. They're able to capture more of the GDP as profits and pay less to labor.
结果发现，在长达75年的时间里，这个比例都非常稳定。三分之二归劳动，三分之一归资本。好吧？有意思的是，过去25年里，支付给劳动的份额下降了大约10%，支付给资本的份额上升了，而经济学家们非常执着于搞清楚这是为什么。我们有两种理论。一种是自动化。这个我待会儿再说，另一种是市场势力、也就是集中度。也许大企业变得更大了，行使着更强的市场势力。它们能够以利润形式攫取更多 GDP，而少付给劳动。
15:32
Okay? We don't know the answers to that. I'm not going to answer that today. Instead, I want to take that in a different direction, which is, okay. Two thirds is paid to labor, one third is paid to capital. But you can break labor and capital into their components. How much of the GDP that's paid to labor is paid to people with more than a college education versus less than a college education? Or look at the capital income. How much is a return to buildings and structures versus equipment? Or within equipment, how much is a return to computers?
好吧？这个问题的答案我们并不知道。我今天也不打算回答它。相反，我想把它引向另一个方向，也就是说，好。三分之二付给劳动，三分之一付给资本。但你可以把劳动和资本拆解成各自的组成部分。付给劳动的那部分 GDP 中，有多少给了受过大学以上教育的人，多少给了大学以下教育的人？或者看看资本收入。有多少是建筑和构筑物的回报，有多少是设备的回报？或者在设备内部，有多少是电脑的回报？
16:05
Okay? So what share of GDP is paid as a return to computing power, and how has that changed over time? Okay, turns out if you look in the right spreadsheet, you could just look this number up on the BEA, BLS website. But you have to dig. Okay, what does it look like? On the one hand, computers are everywhere. On the other hand, the price is falling like crazy. Right, it's a P times Q, where Q is going up and P is going down. So which effect dominates? Okay, here's the data. During the 1990s, during the dotcom boom, the share of GDP pays a return, a return to computers went up.
好吧？那么，GDP 中有多大份额是作为算力的回报被支付出去的，这个比例随时间又是怎么变化的？好，事实证明，只要你找对了那张表，就能在 BEA、BLS 的网站上查到这个数字。但你得花点功夫去挖。好，那结果是什么样呢？一方面，电脑无处不在。另一方面，价格在疯狂下跌。对吧，这是 P 乘以 Q，Q 在上升，P 在下降。那么哪个效应占主导？好，数据在这里。在1990年代，也就是互联网泡沫时期，作为电脑回报的 GDP 份额上升了。
16:49
It peaks in 2000 at just under four and a half percent, and since 2000 it's fallen by a third to three percent. Right? Computers are indeed everywhere, and yet they're paid less as a share of GDP rather than more. The price decline dominates the quantity increase. This is exactly what a weak-link model predicts. Right. Computers are plentiful. They're the most plentiful thing in the economy. Everything else, humans are scarce. Right. And so the computer share has gone down. So when we think AI might automate everything— And we worry, what are all of us going to do?
它在2000年见顶，接近但不到4.5%，而自2000年以来下降了三分之一，降到了3%。对吧？电脑确实无处不在，可它们拿到的 GDP 份额反而更少了，而不是更多。价格下跌的效应压过了数量增长的效应。这正是薄弱环节模型所预测的。对。电脑是充裕的。它们是经济中最充裕的东西。而其他一切，尤其是人，才是稀缺的。对。所以电脑的份额下降了。所以当我们觉得 AI 可能会把一切都自动化——并且担心，我们所有人还能干什么？
06建模：无限软件只让 GDP 富 2%
17:31
This graph is at least something to take into account. Right. Computers are getting less of GDP, not more of GDP, even though there are 100 million times more transistors in your pocket. Okay. So, what do we do next? We write down a model to try to study this, these two scenarios. Okay? And the model features ideas as the source of long-run growth, Paul Romer's Nobel Prize. It features a production function for goods and ideas that both involve weak links, right? The production of everything, in my sort of view, involves weak links.
这张图至少是一个值得纳入考量的事实。对。电脑拿到的 GDP 份额在减少，而不是增加，尽管你口袋里的晶体管多了一亿倍。好。那么，接下来我们做什么？我们写下一个模型，来试着研究这两种情景。好吧？这个模型把“想法”作为长期增长的源泉，这也是保罗·罗默拿诺贝尔奖的贡献。模型里商品和想法的生产函数都包含薄弱环节，对吧？在我看来，任何东西的生产都涉及薄弱环节。
18:10
So goods and idea production functions have weak links, but they get automated away. It used to be inverting matrices was done with a pencil and paper by hand. Now the computer does it, right? It used to be that driving your car was done by hand, now in San Francisco, the computer does it, right? Hopefully, hopefully, throughout the world, the computer will do it someday soon. Okay, and then that automation process occurs endogenously over time. You invent better machines, you invent faster computers, and that lets you automate more and more things.
所以商品和想法的生产函数都有薄弱环节，但它们会被自动化掉。以前求逆矩阵是靠纸笔手算的。现在电脑来做，对吧？以前开车是靠人手来做的，现在在旧金山，电脑来做，对吧？希望，希望在全世界，电脑不久之后都能来做这件事。好，然后这个自动化过程会随时间内生地发生。你发明更好的机器、发明更快的电脑，而这让你能把越来越多的事情自动化。
18:45
Okay? And then we calibrate the model to fit the historical US data back to the 1950s, okay? And then we run it forward to ask what's gonna happen Okay, and none of the numbers I'm going to show you should you take that seriously, but it's just helpful to see what happens and try to think about what the economic forces are at play and how important they might be. Okay. Before I show you the simulation going forward, let me give you another thought experiment that you can do in this model that's really interesting.
好吧？然后我们校准这个模型，让它拟合美国从1950年代以来的历史数据，好吧？然后我们把它向前推演，问问接下来会发生什么。好，我接下来要给你们看的这些数字，你们都不必太当真，但看看会发生什么还是很有帮助的，可以借此思考背后有哪些经济力量在起作用、它们可能有多重要。好。在给你们看向前推演的模拟结果之前，我先讲另一个可以在这个模型里做的思想实验，非常有意思。
19:13
So as I mentioned, Dario Amodei, Anthropic, OpenAI, everyone is expecting we're going to automate software engineers very soon. One can ask, how much richer would we be if we automated all software engineers? We can ask that in the model. And in fact, I'm going to change the question slightly. What if we had infinite amounts of software rather than finite amounts of software? So anything that uses software, push it to infinity. How much richer would we be? Okay? Remember the computer example. We have 100 million times the transistors, and we're not 100 million times richer, so it's going to be much less because of weak links.
就像我提到的，Dario Amodei、Anthropic、OpenAI，所有人都预期我们很快就会把软件工程师自动化掉。那么可以问：如果我们把所有软件工程师都自动化了，我们会富多少？我们可以在模型里问这个问题。实际上，我要把问题稍微改一下。如果我们拥有的不是有限的软件，而是无限量的软件呢？也就是说，任何用到软件的地方，都把它推到无穷大。我们会富多少？好吧？记得电脑那个例子。我们的晶体管多了一亿倍，可我们并没有富一亿倍，所以由于薄弱环节，答案会小得多。
19:54
How much less? Turns out there's a very simple, elegant formula. Because of weak links, infinite amounts of some task raises GDP by that task's share of GDP. Software is about 2% of GDP. If we had infinite software, we'd be 2% richer, right? Why only 2%? Because all the other weak links are bottlenecking us. Okay? And so this sort of makes you realize automating one thing really well- Is not enough. What you need to do is you need to keep automating the weak links. Right, we need a dynamic model, not just this static model, And that's what the model does.
小多少呢？结果发现有一个非常简单、优雅的公式。由于薄弱环节的存在，某项任务的投入变成无限，只会让 GDP 提高该任务在 GDP 中所占的份额。软件大约占 GDP 的2%。如果我们有无限的软件，我们就会富2%，对吧？为什么只有2%？因为所有其他的薄弱环节都在卡住我们。好吧？所以这会让你意识到，把某一件事自动化得非常好——是不够的。你需要做的，是不断地把一个又一个薄弱环节自动化掉。对，我们需要一个动态模型，而不只是这个静态模型，而这正是这个模型所做的。
20:38
Okay, yeah. So our model features the two key ingredients that I think one takes away from those scenarios that I outlined. In the first scenario, you've got automation gives you new ideas, gives you more automation, gives you more new ideas. There's a flywheel effect there with positive feedback. And positive feedback wants to explode, right? On the other hand, based on the business as usual scenario, we've got weak links, right? Weak links tell you, okay, automating some stuff but not the rest, the chain is still weak because of the weak links.
好，是的。所以我们的模型包含了两个关键要素，我认为这正是从我前面勾勒的那两种情景中该提炼出来的。在第一种情景里，自动化带来新想法，新想法带来更多自动化，更多自动化又带来更多新想法。这里存在一种带正反馈的飞轮效应。而正反馈会不断放大，对吧？另一方面，在照常发展的情景下，我们会遇到薄弱环节，对吧？薄弱环节告诉你，好，你自动化了一部分，但其余的没有，因为这些薄弱环节，整条链条依然是脆弱的。
07模拟结果：会爆炸，但慢得惊人
21:17
Okay, so what if you put both these ingredients in a model, calibrate it to what we've observed in the past, and run it forward? And I'm going to show you two more simulations, right? Two sets of simulations. In one, AI is just a continuation of the historical patterns of automation that we've seen. Okay, so it just continues. You know, we've been automating the economy. This is an important point. We've been automating production for 200 years. The Industrial Revolution, right? Textile looms, tractors and railroads, right?
那好，如果把这两个要素放进一个模型里，用我们过去观察到的数据来校准它，然后让它向前推演，会怎样？接下来我要再给你们展示两组模拟，对吧？两组模拟。第一组里，AI 只是我们所见的历史自动化模式的延续。好，也就是说它只是延续下去。你知道，我们一直在对经济进行自动化。这是很重要的一点。我们已经把生产自动化了 200 年。工业革命，对吧？纺织机、拖拉机、铁路，对吧？
21:52
Computers. We've been automating for a long time, and yet 2% growth Right? But if you continue doing that, something may change. And then the second scenario recognizes: look, AI may be different. AI might not just be a continuation of historical patterns. A weak link of our paper is we don't have a micro-founded model of AI that explains exactly how it's different. So instead, what we're going to do is we're going to take a very aggressive calibration. One of the sectors we look at in our history is the computing sector.
计算机。我们已经自动化了很长时间，可增长率还是 2%，对吧？但如果你继续这么走下去，有些东西可能会发生变化。然后第二个情景承认：看，AI 可能是不一样的。AI 可能并不只是历史模式的延续。我们这篇论文的一个薄弱环节是，我们没有一个有微观基础的 AI 模型，来准确解释它到底怎么不一样。所以我们要做的是，采用一个非常激进的校准。我们在历史数据中考察的一个部门是计算机产业。
22:32
Right? Moore's Law is incredibly fast. So what if, as our aggressive scenario where AI is a break with the past, we say the entire economy starts out today getting automated just like Moore's Law? Machines get better at 10% per year throughout the economy starting today, and then that automation gives you more ideas, gives you more automation. Okay, so that's going to be a very aggressive scenario. I think of the second scenario as too aggressive, and the first scenario as probably not aggressive enough.
对吧？摩尔定律快得惊人。那么，作为我们那个「AI 与过去决裂」的激进情景，如果我们说，整个经济从今天开始，就以摩尔定律的速度被自动化，会怎样？从今天起，整个经济中的机器每年改进 10%，然后这种自动化又带来更多的想法，带来更多的自动化。好，所以这会是一个非常激进的情景。我认为第二个情景过于激进，而第一个情景大概又不够激进。
23:03
So again, maybe the truth is somewhere in between. All right. So what do we see when we do this? So I'm gonna show you three sets of graphs for each scenario and a lot of lines on the graphs, but hopefully it'll make sense. This first graph is the capital share versus the labor share. Remember I said one-third is paid to capital, two-thirds is paid to labor? What share is paid to capital in our simulations? You could see going forward, it's 38.2% for the next 80 years. And then it actually splits off, you know, a hundred years from now.
所以还是那句话，真相也许在两者之间。好。那么我们这么做之后，看到了什么？我要为每个情景展示三组图，图上会有很多条线，但希望大家能看明白。第一张图是资本份额与劳动份额的对比。还记得我说过三分之一付给资本，三分之二付给劳动吗？在我们的模拟中，付给资本的份额是多少？你可以看到，未来 80 年里，它是 38.2%。然后到了差不多一百年之后，它才真正分岔开来。
23:36
What's going on? We have three scenarios. The purple scenario says there's nothing special about humans, and everything gets automated in finite time. If that happens, the share of GDP paid to capital goes to 100%, share paid to labor goes to zero. Okay? The green scenario says almost that, but there's 3% of tasks that are reserved for humans, that can't be automated away. Magnus Carlsen playing chess, Leo Messi playing soccer, right? There's some things we're going to reserve for humans, and in that case, you get infinitely good at the 97%, but the 3% done by humans bottlenecks you.
这是怎么回事？我们有三个情景。紫色的情景认为人类没有任何特殊之处，一切都会在有限时间内被自动化。如果那样，付给资本的 GDP 份额就趋于 100%，付给劳动的份额趋于零。好吧？绿色的情景几乎也是这样，但有 3% 的任务是留给人类的，无法被自动化掉。马格努斯·卡尔森下棋，莱奥·梅西踢球，对吧？总有些事情我们要留给人类，在那种情况下，你在那 97% 上变得无限出色，但由人类完成的那 3% 成了你的瓶颈。
24:24
And in fact, the weak link is the humans. The human share goes to 100%, the labor share goes to 100%, the capital share goes to zero. This is like that computer example I gave you where the share paid to computers is falling. Okay, and then interestingly, there's a scenario in between that I call the baseline, the blue scenario, that says actually the capital share remains stable, one-third, two-thirds, Right? And that one's interesting because you say, "Okay," what's gonna happen to growth in the intermediate scenario where the capital share, labor still gets two-thirds all the way through?
而且事实上，薄弱环节就是人类。人类的份额趋于 100%，劳动份额趋于 100%，资本份额趋于零。这就像我刚才举的计算机的例子，付给计算机的份额在下降。好，然后有意思的是，中间还有一个情景，我称之为基准情景，也就是蓝色那个，它说资本份额其实保持稳定，三分之一、三分之二，对吧？那个情景很有意思，因为你会问：「好，」在这个中间情景里，增长会怎样，也就是资本份额不变、劳动始终拿到三分之二的情况下？
24:56
What happens to automation and economic growth? Okay, here's the growth rates over time. The baseline is 2% growth, like in the graph that I showed you. And you see a couple of different things here. First, purple line, growth goes off to infinity. Full automation, if machines can do everything, including produce ideas, and machines keep getting better, growth explodes. The green scenario, Says, "No, no, no." We're bottlenecked by the 3% of things that can only be done by humans. So the growth rate in the end is limited to how fast humans get better, and I'm not that much better at inverting matrices by hand than my mom was when she was young, right?
自动化和经济增长会怎样？好，这是随时间变化的增长率。基准是 2% 的增长，就像我之前给你们看的那张图。这里你能看到几件不同的事。首先，紫色那条线，增长走向无穷。完全自动化：如果机器能做一切，包括产生想法，而机器又不断变好，增长就会爆炸。绿色的情景说：「不不不。」我们被那 3% 只能由人类完成的事情卡住了。所以最终增长率受限于人类进步的速度，而我用手算矩阵求逆的本事，并不比我妈年轻时强多少，对吧？
25:44
So yeah, that rate is pretty slow. And then interestingly, the baseline scenario, the blue scenario, if you look closely, growth is speeding up. And in fact, even in the baseline scenario, growth speeds up and it ends up settling down at 50% per year. But it takes centuries to get there. So even in the baseline scenario, growth accelerates. You can see the acceleration, 2 to 2.3 to 2.6 to 3, but look at the axis. The acceleration is stunningly slow. Right? By 2050, instead of 2%, we're growing at 2.3%.
所以是啊，那个速度相当慢。然后有趣的是，基准情景，也就是蓝色那个，如果你仔细看，增长在加速。事实上，即便在基准情景下，增长也会加速，最终稳定在每年 50%。但要花上好几个世纪才能走到那儿。所以即便在基准情景下，增长也会加速。你能看到这种加速：2 到 2.3 到 2.6 到 3，但看看坐标轴。这个加速慢得惊人。对吧？到 2050 年，我们不是以 2% 增长，而是以 2.3% 增长。
26:21
Right? This is a model that eventually explodes, but it takes a long time. Why? Because of weak links. Right, this is again like the computer example. We have 100 million times the transistors, but yeah, we're just limited by all the things that humans still have to do. Okay? I can also show you this in levels. So this is the growth rate graph. Here's the level, income per person. So remember the 2% line, that straight line is the dashed line in orange now, and then the numbers are how much richer are you relative to the orange line?
对吧？这是一个最终会爆炸的模型，但要花很长时间。为什么？因为薄弱环节。没错，这又和计算机那个例子一样。我们有了一亿倍的晶体管，可是啊，我们就是被人类仍然必须做的所有那些事情限制住了。好吧？我也可以用水平值的形式给你们看。刚才那是增长率的图。这是水平值，人均收入。记住那条 2% 的线，现在那条直线就是橙色的虚线，而那些数字表示，相对于橙色那条线，你富了多少。
26:56
If growth hadn't accelerated. And you see, yeah, by 2050, we're 4% richer. By 2075, we're 15% richer. And so growth is accelerating. Growth is even exploding, but the explosion is slow because of weak links. Okay, so now you can see why. Oh, and one other thing I find interesting about these graphs, notice the three different scenarios. Maybe look here, are very different for the future. 200 years from now, the world looks very different, whether in green or purple. And yet, for the next 75 years, it's really hard to tell which scenario you're in.
也就是如果增长没有加速的话。你会看到，是啊，到 2050 年，我们富了 4%。到 2075 年，我们富了 15%。所以增长是在加速的。增长甚至是在爆炸的，但因为薄弱环节，这种爆炸很慢。好，现在你能明白为什么了。哦，还有一件我觉得这些图很有意思的事，注意这三个不同的情景。看这里吧，它们对未来的预测差别巨大。200 年后，无论是绿色还是紫色，世界看起来都非常不同。然而，在接下来的 75 年里，你其实很难分辨自己身处哪个情景。
27:37
Okay? Now, you can see why I wanted a second set of scenarios, because this growth explodes, but it takes 100 years. You might say, "Okay, well, you're being too conservative with respect to AI." AI is not just a continuation of the automation that we've seen for the last 200 years. [DARON ACEMOGLU] Maybe it is, but seeing this, you wanna say, "What if it's not?" And so now we say for our next set of scenarios, no, suppose AI starts out today with Moore's Law everywhere. It's not just Moore's Law in the computing sector, machines get better throughout the economy at 10% per year instead of 3% per year, which was our baseline number for the aggregate economy.
好吧？现在你能明白我为什么想要第二组情景了，因为这里增长确实会爆炸，但要花 100 年。你可能会说：「好吧，你对 AI 的估计太保守了。」AI 不只是我们过去 200 年所见的那种自动化的延续。[达龙·阿西莫格鲁] 也许它就是，但看到这个，你会想说，「万一它不是呢？」所以现在，在下一组情景里我们说，不，假设从今天起 AI 就让摩尔定律遍布各处。不只是计算机部门有摩尔定律，而是整个经济中的机器每年改进 10%，而不是我们对整体经济设定的基准数字 3%。也就是整体经济。
28:19
Okay? Okay. Capital share, labor share looks the same. This graph, you might say, isn't that the same graph? Well, look at the axis. The years have changed. Okay. And so if I show you the growth rates now, Okay. Now this is different, right? You can see in the purple line and even the blue line, growth is exploding. Right? By 2050, we're well on the way. We're above 25% growth per year. And even today, The scenario starts in 2020. If you have Moore's Law everywhere today, instead of growing at 2%, the economy grows at 4.7%.
好吧？好。资本份额、劳动份额看起来是一样的。这张图，你可能会说，这不是同一张图吗？可是，看看坐标轴。年份变了。好。那么如果我现在给你们看增长率，好，这就不一样了，对吧？你可以看到，紫色那条线，甚至蓝色那条线，增长都在爆炸。对吧？到 2050 年，我们已经走得很远了。我们的年增长率超过 25%。甚至就在今天——这个情景从 2020 年开始。如果今天到处都有摩尔定律，经济就不是以 2% 增长，而是以 4.7% 增长。
29:03
So we certainly didn't do that for the last six years. And so we kind of, that's another version of this example. It's too aggressive. But what I take away from here, let me show you, and growth goes to 7% and 13% by 2040. This is an explosion that happens relatively quickly. And yet, relative to the AI 2027 folks or the people in Silicon Valley who think it's happening in four years, no, it's not until 2060 that the explosion, you know, is fully complete, right? Even in this case, in this incredibly aggressive case where we're 50% richer in 2030 than we would have been without the accelerating growth, even in this case, the explosion takes 30 years.
所以过去六年我们显然没有做到这一点。所以这算是，这是这个例子的另一个版本。它太激进了。但我从中得到的结论是，我给大家看一下，到2040年增长率会达到7%和13%。这是一场发生得相对较快的爆发。然而，相比于AI 2027那批人，或者硅谷那些认为四年内就会发生的人，不是的，要到2060年，这场爆发才算彻底完成，对吧？即便在这种情况下，在这种极其激进的情形里——2030年我们比没有加速增长时富裕50%——即便如此，这场爆发也要花30年。
29:51
Why? Again, weak links. And so I think this weak link phenomenon is really slowing things down, and that's one of the lessons I take away from the scenario. So, two lessons here. [CHAD JONES] I'm a person who's made my career off of that straight line graph that I showed you. The whole reason I'm at Stanford, the whole reason I got tenure is that one picture. Right? That picture says bet on growth not changing, right? 2% per year for 150 years, maybe for the next 30 years. And yet, I'm telling you, in a world of AI- All the scenarios I run say growth explodes over the next 50 or 100 years.
为什么？还是那句话，薄弱环节。所以我认为这种薄弱环节现象确实在拖慢进程，这也是我从这个情景推演中得到的一个教训。所以，这里有两个教训。[CHAD JONES] 我这个人的整个职业生涯，就是建立在我刚给大家看的那条直线图上的。我之所以能在斯坦福，之所以能拿到终身教职，全靠那一张图。对吧？那张图告诉你：押注增长不会变，对吧？150年来每年2%，也许未来30年还是这样。然而我要告诉你，在一个有AI的世界里——我跑的所有情景都表明，未来50年或100年增长会爆发。
30:33
Right, now what happens in the long run depends on these details, the Leo Messi example. Growth explodes, but still the explosion is not nearly as fast as you would have thought when I said the word explosion, right? And that's the weak link thing. So those are the things that I think are going on. Notice, why does it explode even with weak links? Well, eventually we automate away all the weak links. And then this flywheel effect really takes over, and that's what's giving you the explosion. Okay? All right.
对，那么长期会发生什么，取决于这些细节，也就是莱奥·梅西那个例子。增长会爆发，但这场爆发远没有你在听到「爆发」这个词时所想象的那么快，对吧？这就是薄弱环节的作用。所以这些就是我认为正在发生的事情。请注意，为什么即使存在薄弱环节，增长最终还是会爆发？因为最终我们会把所有薄弱环节都自动化掉。然后这个飞轮效应就真正接管一切，这才是带来爆发的原因。好吗？好的。
08就业：放射科医生 vs. Uber 司机
31:07
So what I wanna do in the last 10 minutes is talk about several other things. And these things, at least the job and inequality thing, like my kids ask me about this every day. Every time I give a talk, they ask me about this. I don't know nearly as much about this as I do about growth, but I can at least, having thought about it for a while and read other people's expert work, I can share some insights with you. So Jeff Hinton, Nobel Prize winner, inventor of deep neural networks, In 2016, I was actually at the conference in Toronto when he gave this remark.
所以最后10分钟我想谈几件其他的事情。这些事情，至少是就业和不平等这件事，我的孩子们每天都会问我。我每次做演讲，他们都会问我这个。我对这方面的了解远不如对增长的了解，但至少，在思考了一段时间并读了其他专家的研究之后，我可以跟大家分享一些见解。杰夫·辛顿（Jeff Hinton），诺贝尔奖得主，深度神经网络的发明者，2016年他说那番话时，我其实就在多伦多的那场会议现场。
31:41
He said, "We should stop training radiologists." He said, "In five years," from 2016's perspective, "there'll be no more radiologists with jobs, because the AI will be better than the radiologists." Okay? He wasn't wrong about AI being better than the radiologists. That's true on many dimensions today, but not on every dimension. What do I mean by that? Well, in fact, there's a nice article written in this magazine, Works in Progress, that documented we have more radiologists today than we did in 2016, and they're paid more than they were in 2016.
他说：「我们应该停止培养放射科医生。」他说：「五年之内」——从2016年的角度看——「就不会再有放射科医生有工作了，因为AI会比放射科医生更强。」对吧？关于AI会比放射科医生更强这一点，他并没有说错。在今天的很多维度上确实如此，但并非在每个维度上都如此。我这话是什么意思？事实上，有一本杂志上有篇很不错的文章，叫《Works in Progress》，文章记录了：今天的放射科医生比2016年更多，而且他们的薪酬比2016年更高。
32:14
Right? Why is that? I think, again, it's weak links. So the insight economists have come up with is jobs are bundles of tasks. There are a hundred different tasks that you do in your job. When the AI automates 75 of them Well, the weak links are the things that are now scarce and get the high return. And the automation, you know, the fact that I have a computer when I'm doing research makes me more productive as a researcher, so my wage goes up. The fact that the radiologist can consult with an AI model that helps them read scans and detect cancers and other problems makes them more valuable and more productive, and they're still needed to consult on surgeries or consult with other doctors or double-check the hardest scans, right?
对吧？为什么会这样？我认为，还是那句话，是薄弱环节。经济学家提出的洞见是：工作是一系列任务的集合。你的工作里有一百项不同的任务。当AI自动化了其中75项——那么薄弱环节就成了现在稀缺、并获得高回报的东西。而自动化，你知道，我做研究时有电脑这件事让我作为研究者更高产，所以我的工资就上去了。放射科医生能借助AI模型来帮他们读片、发现癌症和其他问题，这让他们更有价值、更高产，而且他们仍然被需要——去为手术提供会诊、与其他医生会诊，或者复核最难判读的片子，对吧？
33:01
So automating 75% of tasks can raise wages. On the other hand, if you're betting on Uber drivers 10 years from now, I think there's a good chance we won't have Uber drivers 10 years from now, right? Now I'm gonna sound like Jeff Hinton, you know, in 10 years when we still have Uber drivers, but, you know. The Waymo, the self-driving cars are really automating everything that an Uber driver does. And, you know, it takes time. It's actually fascinating how much time it takes. You know, the first self-driving car competition, DARPA had a competition in 2004.
所以自动化掉75%的任务，是有可能提高工资的。另一方面，如果你要押注10年后还有没有Uber司机，我认为很有可能10年后我们就没有Uber司机了，对吧？当然，10年后我们要是还有Uber司机，我这话听起来就跟杰夫·辛顿一样了，不过嘛，你懂的。Waymo，那些自动驾驶汽车，确实在把Uber司机做的每一件事都自动化。而且你知道，这需要时间。它需要多长时间，其实挺让人惊讶的。你知道，第一次自动驾驶汽车比赛，是DARPA在2004年办的。
09不平等、意义感与富足世界
33:36
Carnegie Mellon, Stanford, other teams entered. No one won. No one completed the course. 2005, Stanford wins, Sebastian Thrun. And, you know, that was 2005. We're more than 20 years later, and yeah, in San Francisco you could take a self-driving car, but they're very rare outside of the Bay Area, and even in the Bay Area, one wouldn't call them common. Okay? And so, again, why? Kind of the weak link view tells you things take a lot longer than you think. Inequality and meaningful work. So, you know, historically, labor's the main asset that people trade to get consumption.
卡内基梅隆、斯坦福，还有其他队伍都参加了。没有人赢。没有人跑完全程。2005年，斯坦福赢了，塞巴斯蒂安·特龙（Sebastian Thrun）。而那已经是2005年的事了。如今20多年过去了，是的，在旧金山你可以坐自动驾驶汽车，但在湾区以外它们非常罕见，而且即便在湾区，也谈不上常见。对吧？所以，还是那个问题，为什么？薄弱环节这个视角告诉你：事情花的时间比你想象的要长得多。不平等与有意义的工作。从历史上看，劳动是人们用来换取消费的主要资产。
34:19
What happens when the machines can do things better than you? You might worry about the value of your labor and will you be able to trade it for consumption. That's a valid worry. Let me give you the optimistic take. The world where AI changes everything, as we saw in the simulations, is a world where GDP is incredibly high. We're living in a world of abundance there, and there's plenty to go around. Rich countries already engage in lots of redistribution. An interesting question that I want to look at is, suppose we kept the US redistribution programs in place as they are now.
当机器能把事情做得比你更好时，会发生什么？你可能会担心自己劳动的价值，担心还能不能用它换来消费。这是一个合理的担忧。让我给出乐观的那一面。正如我们在模拟中看到的，AI改变一切的那个世界，是一个GDP极高的世界。在那里我们生活在富足之中，可分配的东西非常充裕。富裕国家本来就已经在进行大量的再分配。我想研究的一个有意思的问题是：假设我们把美国现有的再分配项目原封不动地保留下来。
34:57
And we run the model, what happens to the wages of the bottom 10%, or consumption of the bottom 10%, not the wages? I think it goes up, right? And so there's a chance to make everyone better off. Economists love making everyone better off, and we say things like that about trade, and yet then you get the China shock and people in North Carolina seeing their jobs disappear and communities being decimated. So it doesn't happen automatically. But at least it's a world of abundance where there are possibilities.
然后我们跑这个模型，最底层10%的人的工资会怎样，或者说最底层10%的人的消费会怎样——不是工资，是消费？我认为它会上升，对吧？所以有机会让每个人都过得更好。经济学家最喜欢让每个人都变得更好，我们谈到贸易时也是这么说的，然而后来就出现了「中国冲击」，北卡罗来纳州的人眼看着自己的工作消失，社区被摧毁。所以这不会自动发生。但至少那是一个富足的世界，其中存在各种可能性。
35:30
I'll say one other thing here. I worry, you know, I've been using the AI models to help me with research all the time now, and already GPT 5.2 Pro was as good as me at math, and 5.5 Pro is way better than me at math. And I wonder, how long is it gonna be before the AI writes better growth papers than I do? And what am I gonna do? I get all my meaning, half my meaning. Family's great. The other half of my meaning comes from, you know, developing growth models, and the AI's gonna be better than me.
这里我再说一件事。我有点担心，你知道，我现在一直在用AI模型帮我做研究，而且GPT 5.2 Pro在数学上就已经和我不相上下了，5.5 Pro在数学上比我强太多了。我就在想，还要多久AI写的增长论文就会比我写的更好？那我该做什么呢？我的意义感全部来自——好吧，一半来自——家庭很棒。另一半意义感来自于，你知道，构建增长模型，而AI将会比我做得更好。
36:04
What will we do? Well, an analogy I go back to is retirement, right? When we look at retirees, we don't say, "Oh, they no longer have this great meaningful work." They seem pretty happy. They live in a world of abundance, go on cruises, go see their friends, go to have dancing. Summer camp was an analogy I liked. They go make pottery, and maybe my version of that would be making pottery and singing songs, and getting together with my growth friends, and having the AI teach us the latest growth model.
我们该做什么呢？我常回到的一个类比是退休，对吧？我们看待退休的人时，并不会说：「哦，他们再也没有那种伟大而有意义的工作了。」他们看起来挺开心的。他们生活在富足之中，去坐邮轮，去看朋友，去跳舞。夏令营是我喜欢的一个类比。他们去做陶艺，也许我的版本会是做陶艺加唱歌，然后和我那些搞增长研究的朋友聚在一起，让AI教我们最新的增长模型。
10灾难性风险：下行来得更快
36:30
I think that'll be my version of summer camp. [DARON ACEMOGLU] Okay, let me say one other thing though, because contrary to what you might have taken from the talk so far, I'm not, a total optimist on this. I would even say I'm very nervous about our future. Why? Because of catastrophic risks. And I'll say something about it, and I think it's really important to Discuss this honestly and openly and without the sort of pejorative criticism that sometimes gets associated with it. I think this is something we absolutely should take seriously.
我想那就是我版本的夏令营。[DARON ACEMOGLU] 好，不过我还想说一件事，因为跟你们从目前这场演讲中可能得到的印象相反，我并不是在这件事上完全乐观的人。我甚至可以说，我对我们的未来非常紧张。为什么？因为灾难性风险。我会谈谈这个，我认为非常重要的是要诚实、公开地讨论这件事，而不要带上有时会伴随它出现的那种带贬义的批评。我认为这是我们绝对应该认真对待的事情。
37:03
So there are two versions that people talk about. There's the bad actor version and the alien intelligence version. The bad actor version, I think everyone can understand very easily and believes is likely to be a problem. So what if there's some bad actor, some hacker in North Korea or wherever? That decides they want to do harm, and they have access to a jailbroken version of GPT-8 or Opus 7. All these models are jailbroken the day they come out. And they ask the model, you know, this oracle by GPT-8, by Opus 7, these are going to be able to do anything the smartest humans can do.
人们谈论的有两个版本。一个是坏行为者版本，一个是异类智能版本。坏行为者版本，我想每个人都很容易理解，而且都相信这很可能会是个问题。假如有某个坏行为者，某个在朝鲜或别的什么地方的黑客？他们决定要作恶，而且能拿到越狱版的GPT-8或者Opus 7。这些模型在发布当天就全被越狱了。然后他们去问这个模型，你知道，问GPT-8、Opus 7这个「神谕」，这些模型将能做到最聪明的人类能做的任何事。
37:42
If it's possible to design a virus that's more lethal than Ebola and takes three months to display symptoms, the AI will figure it out, right? We got through nuclear weapons so far because they were so rare. Only a handful of people had red buttons that they could push and do serious damage. If eight billion people have access to the red button, can we make sure no one pushes it? So, this is a problem that I take very seriously. The other version is more speculative, but there's a quote from this computer science professor from Berkeley that I found helpful.
如果有可能设计出一种比埃博拉更致命、且潜伏三个月才显现症状的病毒，那么这个AI会想明白的，对吧？到目前为止我们能安然度过核武器这一关，是因为核武器太稀少了。只有极少数人手里有那个红色按钮，一按下去就能造成严重破坏。如果八十亿人都能碰到这个红色按钮，我们还能保证没人去按吗？所以这个问题我看得非常重。另一种说法就更带有推测性了，不过伯克利有位计算机科学教授说过一句话，我觉得很有启发。
38:16
So, alien intelligence. Suppose we found out this evening that there's a spaceship on its way from Pluto toward the Earth. Right? Some alien spacecraft heading toward Earth. How do we feel? We'd be pretty excited at first, and then we'd say, "But, you know, wait a minute. When advanced societies or species encounter less advanced societies and species in our history, it hasn't gone well for the less advanced." Right? And Stuart Russell's quote, "How do we retain power over entities more powerful than us forever?"
也就是——外星智能。假设今晚我们得知，有一艘飞船正从冥王星朝地球飞来。对吧？某种外星飞行器正朝地球而来。我们会怎么想？一开始我们会挺兴奋，然后就会说：“可是，等一下。”在人类历史上，当先进的社会或物种遇到不那么先进的社会和物种时，对不那么先进的那一方来说，结果都不太妙。”对吧？还有斯图尔特·罗素（Stuart Russell）的那句话：“我们要如何永远保持对比我们更强大的存在的控制权？”
38:51
That's worth thinking about. Let me say one other thing. I'm going to skip the how much should we spend to avoid risk. Let me say one other thing, because it's related to weak links and it's the thing I've been worried about just in the last month as I developed the research paper. Chain is only strong as its weakest links. I told you one consequence of that is the benefits come slowly, right? Because you have to automate all the weak links. You need to strengthen all of them to get the full benefit, to get growth to explode.
这值得好好想一想。我再说一件事。关于我们该花多少代价去规避风险，这部分我就跳过了。我再说一件事，因为它和“薄弱环节”有关，而且这正是我最近一个月写这篇研究论文时一直担心的事。一条链的强度取决于它最薄弱的环节。我跟你们说过，这带来的一个后果是：收益来得很慢，对吧？因为你必须把所有薄弱环节都自动化。你得把它们全部强化，才能拿到全部收益，才能让增长爆发。
39:21
[CHAD JONES] Okay, but remember the Space Shuttle Challenger, the O-ring problem, or a chain. If you break one link in the chain, all the value gets lost. Right? And so a weak link model is very slow to improve, but it's very fragile on the downside. So already, think about Mythos, the model that Anthropic, didn't release publicly, but said, "Look, this model's discovering bugs in 25-year-old software that's been battle-tested by humans for 25 years that we didn't find," right? It's discovered thousands of bugs that people didn't find.
[查德·琼斯] 好，但请记得挑战者号航天飞机，那个O型密封圈的问题，或者说一条链子。只要链条上有一环断了，所有价值就全没了。对吧？所以薄弱环节模型在向上改善时非常慢，但在向下崩坏时又极其脆弱。所以现在就想想 Mythos 这个模型吧——Anthropic 并没有公开发布它，但说过：“看，这个模型能在有 25 年历史的软件里发现漏洞，那些软件被人类实打实用了 25 年，我们都没发现这些漏洞”，对吧？它发现了成千上万个人类没找出来的漏洞。
39:57
In six months, in a year, we'll have an open source version of Mythos that anyone can use, right? How sure are we that a bad actor doesn't take it and hack the electric grid, hack the financial system, hack the banking system? Or it can communicate, it contacts a bio lab somewhere across the earth and says, "Help me design a virus. "I'm a scientist at Stanford trying to do something." Or, you know, like- If you hack the electric grid, you hack the banking system, we zero out everyone's bank balance in your favorite financial institution, that would be a huge problem.
半年后、一年后，我们就会有一个人人都能用的 Mythos 开源版本，对吧？我们有多大把握，不会有坏人拿它去攻击电网、攻击金融体系，攻击银行系统？或者它能对外沟通，联系上地球某处的一个生物实验室，说：“帮我设计一种病毒。我是斯坦福的科学家，正在做某项研究。”或者，比如说——如果你黑掉电网，黑掉银行系统，把你最喜欢的那家金融机构里所有人的账户余额清零，那就是个天大的麻烦。
40:35
Not an existential problem, but I think that's a problem we have a good chance of facing in the next three years, right? The AI can already do that. Mythos can already come close to doing that. Wait a year or two or three at the rate these models are getting better, this is a problem that's around the corner, and I think it's something we should be concerned with. Okay, let me just end with this last thought. So a question I find helpful: how much did the internet change the world? Between 1990 and 2020.
这算不上生存性威胁，但我认为这是我们在未来三年里很有可能面对的问题，对吧？AI 现在就已经能做到了。Mythos 现在已经很接近能做到了。按照这些模型进步的速度，再等一两年、三年，这个问题就近在眼前了，我觉得这是我们应该重视的事情。好，我就用最后这个想法来收尾。有个问题我觉得很有帮助：互联网把世界改变了多少？从 1990 年到 2020 年。
41:08
A lot, we think. Despite the 2% growth, right? Again, you don't know the counterfactual. How much is AI gonna change the world between, say, 2015 and 2045? How many internets is it worth? I think multiple internets, many internets. I think it's more transformative than anything we've seen probably so far, but it's probably gonna take a longer time than we thought. But just because it takes 30 years instead of five years doesn't mean the effects won't ultimately be huge and transformative. And then I add this last bullet point, I think the downside risks can come sooner.
我们觉得，改变了很多。尽管增长率只有 2%，对吧？再说一次，你并不知道反事实的情形会是怎样。那么 AI 会在，比如说，2015 到 2045 年间把世界改变多少？它相当于多少个互联网？我认为是好几个互联网，很多个互联网。我觉得它可能比我们迄今见过的任何东西都更具变革性，但它花的时间可能会比我们原以为的更久。不过，只因为它要花 30 年而不是 5 年，并不意味着它最终的影响不会是巨大而颠覆性的。然后我加上了最后这一条：我认为下行风险可能来得更早。
41:40
That's what the weak link view of the world delivers. And so we should be using the intervening years that we have to prepare for these risks, both the inequality risks, the labor market risks, the political economy risks, and the catastrophic type risks that might be there. So sorry to end on a down note. I should say, class of 2006, hooray! (audience cheers and applauds)
这正是“薄弱环节”式的世界观所推导出的结论。所以我们应该利用中间这些年，为这些风险做好准备——既包括不平等风险、劳动力市场风险、政治经济风险，也包括可能出现的灾难性风险。抱歉，最后讲得有点丧气。我该说一句：2006 届的同学们，万岁！（观众欢呼鼓掌）
11问答：GDP 测量与短期分配冲击
42:06
[CHAD JONES] Okay, so I think we have 15 minutes or so for questions. There are people with microphones, so how about wait until someone comes to you with a microphone so everyone can hear? Happy to talk about any of the things on these slides, or macroeconomics, or how Stanford's changed. I guess up here.
[查德·琼斯] 好，我想我们大概还有 15 分钟可以提问。有人在传话筒，要不大家等话筒递到手上再讲，这样所有人都能听见？这些幻灯片上的任何内容，或者宏观经济学，或者斯坦福这些年的变化，我都很乐意聊。那就这边这位吧。
42:31
[AUDIENCE MEMBER] Thank you for that. A question around the slow growth rate that seems counterintuitively slow. And I'm sure you've heard this objection a million times, but I'm curious how you respond is, what about the infinite free chess lessons that I get? That aren't monetized. Um, and there's value there, but I don't think that it's reflected in GDP, and that's just gonna multiply going forward. So even if the GDP growth rate looks slow, there's so much value. Um, how are we capturing that?
[观众] 谢谢您的演讲。我想问一个关于增长率的问题，那个增长率慢得有点反直觉。我相信这个质疑您已经听过无数遍了，但我很好奇您会怎么回应：那我免费拿到的、无穷无尽的国际象棋课怎么算呢？那些都没有被货币化。嗯，那里面是有价值的，但我觉得它没有体现在 GDP 里，而且这种情况以后只会成倍增加。所以就算 GDP 增长率看着慢，其实创造了非常多的价值。嗯，我们要怎么把这部分算进来？
43:08
[CHAD JONES] Yeah. This is a question you can ask historically as well. GDP is mismeasured for sure, but it's always been mismeasured, right? You invent antibiotics. How are those captured? How are the gains in life expectancy over the 20th century? Massive gains. Bill Nordhaus, the Nobel Prize winner from a couple of years ago, asked this question. He said, "Suppose you could only have one of two things. You could have the GDP growth of the 20th century, or you could have the gains in life expectancy, but not both."
[查德·琼斯] 是的。这个问题放到历史上同样可以问。GDP 肯定是被测量错了，但它一直都被测量错了，对吧？比如你发明了抗生素。这些怎么被计入呢？20 世纪里预期寿命的提升又怎么算？那是巨大的提升。几年前的诺贝尔奖得主比尔·诺德豪斯（Bill Nordhaus）就问过这个问题。他说：“假设两样东西你只能选一样。你可以要 20 世纪的 GDP 增长，或者要预期寿命的提升，但不能两样都要。”
43:38
Life expectancy went from 50 years to, you know, 77 years. Right, and he surveyed a bunch of people and half chose one, half chose the other. It says, "The gains in life expectancy, which are not nearly adequately captured in GDP, are as important as the gains in GDP." So I totally agree with you, things are mismeasured. An interesting question is whether things will be increasingly mismeasured or not. And I'm open to the possibility that the answer is yes. So the simulations I'm showing you are kind of holding constant the measurement, and that would be one reason why things could be a little faster.
预期寿命从 50 岁提高到了大约 77 岁。对吧，他调查了一大批人，结果一半人选这个，一半人选那个。这说明：“预期寿命的提升——它在 GDP 里根本没有被充分体现——和 GDP 的增长同样重要。”所以我完全同意你，很多东西确实被测量错了。一个有意思的问题是：这种测量偏差以后会不会越来越大。我不排除答案是“会”的可能性。所以我给你们看的这些模拟，某种程度上是把测量方式当作不变的，而这也正是实际情况可能会更快一些的一个原因。
44:11
I think that's totally a fair point. I appreciate it. Yeah, I think there's a question up here. Oh, over here. Sorry.
我觉得这完全是个中肯的观点。谢谢你提出来。好，我看这边好像还有个问题。哦，是这边。抱歉。
44:21
[AUDIENCE MEMBER] So one question I have, and I don't know, it could be not the topic of your talk, is about distribution and the short-term effects, short being next 10, 20 years, is that if AI comes and automates certain tasks and completely eliminates the need for those tasks, a big chunk of the economy dies almost immediately. And until we reach that 50 to 100 year trajectory of whatever growth we have, this is going to be in a shock in and by itself, which could be a disrupting factor to AI growth or any other growth.
[观众] 我有一个问题，不知道，可能不太在您演讲的主题范围内，是关于分配，以及短期影响的——所谓短期是指未来 10 年、20 年——就是说，如果 AI 到来，把某些工作任务自动化，彻底让这些任务不再有需求，那么经济中很大一块几乎会立刻死掉。而在我们走到那个 50 到 100 年的增长轨迹之前，这本身就会是一次冲击，它可能反过来成为 AI增长或其他任何增长的阻碍因素。
44:53
Do you have any scenarios simulating that? [CHAD JONES] I don't. I think this is an incredibly important question. I had those two slides on the labor market effects, and I kind of mentioned, Yeah, I'm not the right person for that. [DARON ACEMOGLU] I am. The follow-up paper we're working on is exactly on this question. So, you know, come back in a year or two and hopefully I'll have a better answer. I will say, as I think about it, I think the Waymo example or the radiologist example I find very helpful.
您有没有做过模拟这种情形的情景分析？[查德·琼斯] 没有。我认为这是一个极其重要的问题。我有两张讲劳动力市场影响的幻灯片，我也稍微提到了，是啊，这方面我不是最合适的人选。[达龙·阿西莫格鲁] 我是。我们正在做的后续论文研究的正好就是这个问题。所以，一两年后再来问我，希望到时候我能给出更好的答案。我想说的是，就我自己的思考而言，Waymo 的例子或者放射科医生的例子我觉得很有启发。
45:23
Because again, Geoff Hinton, world's expert on deep learning, you know, deep neural networks, is telling you we're not going to need radiologists, yet we have more and they're better paid. Or, you know, Waymo, when it was with self-driving cars, 2012. If you go back and read the Wall Street Journal, they're saying self-driving cars are here. In five years, no one will need to drive ever again. And we're a long, long way from that. Why? Because weak links and the lesson of history that all these changes take a lot longer than you think.
因为，还是那句话，杰夫·辛顿（Geoff Hinton），深度学习、深度神经网络领域的世界级专家，告诉你我们将不再需要放射科医生，可结果放射科医生反而更多了，收入也更高了。再比如 Waymo，那还是自动驾驶汽车刚起步的 2012 年。你回过头去翻《华尔街日报》，他们说自动驾驶汽车已经来了。五年之内，再也没人需要开车了。而我们离那一天还远得很。为什么？因为薄弱环节，还有历史的教训——所有这些变化花的时间都比你想的长得多。
45:53
And so, the thing that's going to be automated first, I believe, is software engineering. Ask yourself, do you think we'll have as many software engineers 10 years from now as we do today? And it's easy to say no. But let me give you the scenario where we say yes. AI is gonna transform the entire world. Integrating AI into every business in the world is a long, drawn-out process that requires lots of software engineers. You might say someday we'll have AI that can do it, but ask yourself, you're the CEO of your company, you're the CIO, are you just letting Anthropic press the button, or do you want some people in there that you can talk to and do it slowly and make sure it works right with your precious data?
所以，我相信最先被自动化的领域会是软件工程。问问你自己：你觉得十年后我们还会有和今天一样多的软件工程师吗？说“不会”很容易。但让我给你讲一个我们会说“会”的情景。AI 将会改变整个世界。把 AI 整合进世界上的每一家企业，是一个漫长而拖沓的过程，需要大量的软件工程师。你可能会说，总有一天我们会有能干这活儿的 AI，但你想想，假如你是公司的 CEO，是 CIO，你会就这么让Anthropic 去按下那个按钮吗？还是说你希望有一些人在里面，你能跟他们沟通，慢慢来，确保它跟你宝贵的数据能正确地跑通？
46:38
I think we're going to have software engineers around, and that's probably the first thing to be automated. [CHAD JONES] So I think one of the things I took away from that, the scenarios being relatively slow, is that we might have more time than we think to avoid some of these scenarios that we're very concerned about. But that's just a very preliminary thought, and I think this is a really important question.
我觉得软件工程师还是会一直存在的，而这大概是最先被自动化的工作。[查德·琼斯] 我从中得到的一个体会是，这些情景的推进相对缓慢，这意味着我们也许比自己以为的有更多时间，来避免那些我们非常担心的情景。但这只是一个非常初步的想法，我认为这是个真正重要的问题。
47:02
[AUDIENCE MEMBER] I'd just like to double-click on the radiologist versus Uber driver. If you look at the number of people that are getting scanned, and have more active preventionary healthcare, it's exploded over the last 50 years. And so, possibly a reason for the rise of radiologists and also incomes for radiologists is because there's more variability, and due to the fact that there are more medical technicians that increase the variability by placing patients into a scan in various different ways, and there are more things that are being seen.
[观众] 我想就放射科医生和 Uber 司机这一点再深入问一下。如果你看一下接受扫描检查的人数，以及更积极的预防性医疗保健，过去 50 年里这些是爆炸式增长的。所以放射科医生数量增加、收入也增加的一个可能原因，是变异性变多了，而且由于医技人员变多，他们用各种不同方式把病人送进扫描仪，也增加了这种变异性，同时能看到的东西也变多了。
47:53
Code is very different. And I would argue that if there is automation in terms of placement and also machine learning in terms of imaging, that the weak link could become much stronger. So this all feels like von Neumann's elephant to a certain degree. And You know, at what point are we saying, like, you know, with five points, five variables, we can, like, you know, make the elephants' tails wag? [CHAD JONES] Yeah, I know. These are all excellent points. I don't disagree with anything you said. I do think that, yeah, things taking longer.
代码则非常不一样。我想说的是，如果在摆位方面实现了自动化，在成像方面又用上了机器学习，那么那个薄弱环节可能会变得强大得多。所以这一切在某种程度上让我想到冯·诺依曼的大象。那么，到什么程度我们才会说，你知道，用五个点、五个变量，我们就能让大象的尾巴摆起来？[查德·琼斯] 是啊，我明白。这些都是非常好的观点。你说的我都不反对。我确实认为，是的，事情会花更长时间。
48:48
Than we might have expected based on Waymo. Here's another way to think about it. So someday, I think we'll have robots teaching our kindergartners, right? Why do I say that? You might say, "No, I'd never want a robot teaching my kindergartner." Well, remember, the average kindergartner teacher is not nearly as good as the world's best in history kindergarten teacher. Once we invent the robot that can be trained to be the world's best kindergarten teacher in history, or even better. We can then replicate them a million times and give every kindergarten classroom that robot.
比我们基于 Waymo 所预期的要久。还有另一种思考方式。总有一天，我认为我们会让机器人来教我们的幼儿园小朋友，对吧？我为什么这么说？你可能会说：“不，我绝不会想让机器人来教我家的幼儿园小孩。”但请记住，普通的幼儿园老师远远比不上历史上最优秀的幼儿园老师。一旦我们发明出可以被训练成史上最好的幼儿园老师、甚至更好的机器人，我们就可以把它复制一百万份，让每一间幼儿园教室都拥有这样一个机器人。
49:21
Okay? That's gonna happen, but is it gonna happen in 10 years? No way, right? It's Waymo, you know, triple clicked, right? Are we gonna let the robot take care of little, you know, whoever? Mine, Audrey, little Audrey. Uh, no, because, you know, we wanna make sure it's 100,000% safe, right? So I do think these things are gonna take... [AUDIENCE MEMBER] Just one follow-up. Sure. Also, if you look at... You know, the growth of the Taiwanese economy or the South Korean economy, IT output for those economies are substantial.
对吧？这是会发生的，但会在 10 年内发生吗？绝不可能，对吧？这就是 Waymo 那件事，再深入三层，对吧？我们会让机器人去照看小朋友，你知道，谁家的孩子呢？我家的，奥黛丽，小奥黛丽。呃，不会的，因为我们想确保它有 100,000% 的安全，对吧？所以我确实认为这些事情会需要……[观众] 就再追问一个问题。当然可以。另外，如果你看一下……台湾经济或者韩国经济的增长，IT 产出在这些经济体中占了相当大的比重。
49:57
So we've outsourced manufacturing of semiconductor chips to those economies, and we've replaced it with services, namely healthcare and financial services over the years. Those are the areas that are Easily at threat from AI. So how does your model, like kind of, account for the fact that you're replacing manufacturing with services and weak links? [CHAD JONES] Yeah, I think historically manufacturing is kind of the easy thing to automate, and kindergarten teachers and the nurse holding the hand of my father when he has Alzheimer's at night, you know, those things.
我们把半导体芯片的制造外包给了这些经济体，而我们这些年用服务业取而代之，主要是医疗保健和金融服务。而这些恰恰是很容易受到 AI 冲击的领域。那么你的模型怎么解释这一点——你是在用服务业和薄弱环节来替代制造业？[查德·琼斯] 是的，我认为从历史上看，制造业算是比较容易自动化的东西，而幼儿园老师，还有我父亲夜里犯阿尔茨海默病时握着他手的护士，你知道，这些事情。
12问答：资本集中、职业建议与全球视角
50:36
Again, we'll get a robot someday to do it, but, you know, maybe not in the next 20 years. I think the services, to me, they seem like the weak link. I think over here. Sorry, I need to look for the people in the red shirts. That's my, my rule. [AUDIENCE MEMBER] Okay, so people have talked about the K-shaped economy. You know, we've all heard, of course, of Meta doling out $100 million to top software engineers or AI engineers. It feels very Gilded Age right now, at least in the United States. So my question is, in capitalism, what stops the hyper concentration of the capital share of income, i.e., basically an oligopoly, big tech on steroids capturing more and more of the economy?
再说一次，总有一天我们会有机器人来做这些，但是，你知道，也许不会在未来 20 年内。我觉得服务业，在我看来，它们像是那个薄弱环节。我看这边。抱歉，我得找穿红衬衫的人。这是我的规矩。[观众] 好的，大家一直在谈论 K 型经济。当然，我们都听说过 Meta 给顶尖软件工程师或 AI 工程师开出 1 亿美元的天价。至少在美国，现在感觉非常像镀金时代。所以我的问题是，在资本主义制度下，是什么阻止了资本收入份额的高度集中，也就是说，基本上形成寡头垄断，让打了鸡血的科技巨头攫取越来越多的经济份额？
51:19
[DARON ACEMOGLU] Yeah, no, this is totally a valid question to worry about. I don't have the answers, but I'll tell you some things that I've been thinking about along these lines. So first, I think what we're seeing with AI. So, contrary to what I said, historically, we thought manufacturing would be the first thing to be automated. Low-skilled workers could be replaced, and us creative types, how could you ever replace creativity? No, it turns out ChatGPT does creativity like this, right? I'm gonna be replaced long before the electricians and the plumbers, right?
[达龙·阿西莫格鲁] 是的，这完全是个值得担忧的合理问题。我没有答案，但我可以跟你说说我在这方面的一些思考。首先，我觉得我们在 AI 身上看到的情况是——跟我刚才说的相反，历史上我们以为制造业会是最先被自动化的。低技能工人可能被替代，而我们这些搞创意的，创造力怎么可能被替代呢？结果发现，ChatGPT 搞创意就是这么轻松，对吧？我会比电工和水管工早得多被替代，对吧？
51:49
Think about what that does for inequality. That's actually good. The electrician's wage is going up like crazy, and my wage is— I think it's probably not going down actually, but, you know... Fingers crossed. Fingers crossed. But it could be good for inequality in the short term. Second thing I'll say: the high-skilled cognitive labor that's getting replaced by AI: the doctors, the lawyers, the economists, the professors. We all own shares of the S&P 500. If you own shares in the stock market, you're getting some of this capital income.
想想这对不平等意味着什么。这其实是件好事。电工的工资在疯涨，而我的工资——我想实际上可能不会下降，但是，你知道……但愿如此。但愿如此。但短期内这对不平等可能是好事。第二点我要说的是：那些被 AI 替代的高技能认知劳动者——医生、律师、经济学家、教授。我们都持有标普 500 的股份。如果你持有股市里的股票，你就能分到这部分资本收入。
52:22
And so actually, I think the people who own shares are fine. It's the people who don't own shares you have to worry about. But then I would say the government owns a lot of shares in GDP. Why? Through its tax system, right? So the government taxes and transfers, and that allows them To help out the less fortunate. We do that already, maybe not enough. But that at least puts a floor that hopefully can be improved. But this is a political economy question, and if you wanted to say, "Chad, you know nothing about political economy,"
所以实际上，我认为持有股票的人是没问题的。你需要担心的是那些没有股票的人。但我还想说，政府在 GDP 中也占有很大一份“股份”。为什么？通过它的税收体系，对吧？所以政府征税、再转移支付，这让他们能够帮助那些境况较差的人。我们已经在这么做了，也许做得还不够。但这至少设了一个下限，希望以后还能改进。不过这是个政治经济学问题，如果你想说：“查德，你对政治经济学一无所知，”
52:57
"look at the last 20 years," that's a fair point. So I think it's a complicated problem for sure. Okay, over here, please. [AUDIENCE MEMBER] So I really liked your framework of weak links being the source of scarcity and therefore potential for high returns. So given that framework, what advice would you give for younger generation as well as us? To earn the high returns. [CHAD JONES] I do think that, I think management, the things we teach at a business school are actually going to be some of the things that are still valued in the world 10, 15, 20 years from now.
“看看过去 20 年吧，”那也是有道理的。所以我认为这肯定是个复杂的问题。好，请这边。[观众] 我非常喜欢你那个框架——薄弱环节是稀缺性的来源，因此也是高回报的潜在所在。那么在这个框架下，你会给年轻一代以及我们什么建议？好去赚取这种高回报。[查德·琼斯] 我确实认为，我认为管理，也就是我们在商学院教的那些东西，实际上会成为 10 年、15 年、20 年后在这个世界上依然有价值的东西之一。
53:39
Why? I think we don't want to give power to the AI and let it make all the decisions unchecked by humans. Managers are the people who are going to consult with the AIs, and then they make the final call. They're going to be the thing that's, in some sense, scarce and incredibly valuable. So I actually think, you know, I'm going to be automated in two years. You guys are safe for another 15 years, I think. After 15 years, all bets are off. Own shares of the S&P 500, though, and I think you'll be okay.
为什么？我认为我们不希望把权力交给 AI，让它在没有人类监督的情况下做所有决策。管理者就是那些会去咨询 AI、然后做出最终决定的人。在某种意义上，他们会成为稀缺且极其宝贵的那一环。所以我其实觉得，你知道，我两年后就会被自动化掉。而你们还能安全大概 15 年，我想。15 年之后，那就说不准了。不过还是要持有标普 500 的股份，我想那样你们就没问题了。
54:07
So that's, anyway, that's kind of how I think about it. Yes? [AUDIENCE MEMBER] Thanks again for the talk. I thought it was great. So if speed depends on your willingness to go at the weak links, strong, won't it be possible for different societies to go at different speeds? And won't there be huge differences if someone is willing to move a lot faster? [CHAD JONES] Yeah, no, this is an excellent question that I, you know, even less than the labor market inequality question, I have thought not nearly enough.
总之，这大致就是我的看法。请讲？[观众] 再次感谢你的演讲。我觉得非常精彩。那么，如果速度取决于你是否愿意去攻克薄弱环节，让它变强，那不同社会是不是就可能以不同的速度前进？如果有人愿意跑得快得多，那不就会出现巨大的差距吗？[查德·琼斯] 是的，这是个非常好的问题，你知道，比起劳动力市场不平等的问题，我对它的思考还要更不充分。
54:38
And I would say there are lots of economists thinking about the labor market inequality question. I just happen not to be—that's not my focus yet. Almost no one's thinking about what does this look like in a global economy? Right? So I think the economies that have the software engineers and that are creating the ideas behind neural networks and OpenAI and Anthropic, and we own shares of those companies, I think one way or the other, in a world of abundance, we're fine. What about in developing countries that are much less well off, don't have claims on the S&P 500, have enormous inequality, and the least fortunate of those countries are in terrible, terrible shape?
我想说，有很多经济学家在思考劳动力市场不平等的问题。我只是碰巧不在其中——那还不是我的关注重点。但几乎没有人在思考：这在全球经济中会是什么样子？对吧？所以我认为，那些拥有软件工程师、正在创造神经网络背后的思想的经济体，还有OpenAI 和 Anthropic 这些公司，我们持有这些公司的股份，我认为无论怎样，在一个富足的世界里，我们都会没事。但那些境况差得多的发展中国家呢？他们对标普 500 没有任何索取权，国内不平等极其严重，而这些国家里最不幸的人处境糟糕透顶？
55:21
[DARON ACEMOGLU] I don't know how that works. The US redistributing from rich to poor, we already do some of that—maybe not enough of that— but the US doesn't redistribute that much to the poor people around the world, except through the ideas we invent. So the ideas we invent can help out. But this is—I think the global implications of AI for the global economy—fascinating, not studied nearly enough. I saved an article just yesterday that I'm gonna read on the plane tomorrow about this, but it's the only one I could find.
[达龙·阿西莫格鲁] 我不知道那会怎么发展。美国从富人向穷人的再分配，我们已经做了一些——也许还不够——但美国并没有向全世界的穷人再分配那么多，除了通过我们发明的思想。所以我们发明的思想能够帮上忙。但我认为——AI 对全球经济的全球性影响——非常引人入胜，研究得远远不够。我昨天刚存了一篇关于这个的文章，打算明天在飞机上读，但这是我唯一能找到的一篇。
55:51
Yes? [AUDIENCE MEMBER] First, it probably won't be replaced within two years. But just think about your example for radiologists. A lot of my friends, me included, actually become busier with the AI, and they take over the easier job, and we do the harder job and become easier or harder. So I'm just thinking fast-forward, I don't know how many years, there will be a small group of people become busier. Harder and do this job. And as you mentioned, they have higher pay, whatever. And there's a much larger group probably can just take their summer vacation all day long.
请讲？【观众】首先，两年内可能不会被取代。但想想你举的放射科医生的例子。我的很多朋友，包括我自己，其实因为 AI 变得更忙了，它们接手了比较容易的工作，我们做更难的工作，工作变得更容易或者更难。所以我在想，快进若干年后，我不知道是多少年，会有一小群人变得更忙。更难，并且做这份工作。而正如你提到的，他们拿更高的薪水，等等。然后会有一大群人，可能整天都在过暑假。
56:28
How the society will think about the wealth distribution, the resource distribution at that time? Did you ever think about that? [DARON ACEMOGLU] Yeah, yeah, yeah. This is the only answer I have, which is not satisfactory, is at least it will be a world of abundance, right? In a world of abundance, where the growth graph looks like that, we have Enough income for lots of billionaires and for the worst among us to be millionaires, right? That's what infinite income in finite time creates a lot of resources to redistribute.
到那个时候，社会将如何看待财富分配、资源分配的问题？你有没有想过这个问题？【达龙·阿西莫格鲁】是的，是的，是的。我唯一的答案，虽然并不令人满意，就是那至少会是一个富足的世界，对吧？在一个富足的世界里，增长曲线是那个样子的，我们有足够的收入让很多人成为亿万富翁，让我们当中境况最差的人成为百万富翁，对吧？这就是在有限时间内产生无限收入所意味着的——大量可供再分配的资源。
56:58
Warren Buffett and Bill Gates give away a lot of resources. The tax system transfers a lot of resources. So there's every possibility for that to work out well. It doesn't mean it has to, but I do think a world of abundance is a great world to hope for. We just need to solve this subtle, complicated redistribution question. But I'm more optimistic about that. Again. Call me Pollyanna. Yep. [MODERATOR] And this will be our last question. [ROBERTO SANTANA] Oh, wow. Yes. My name is Roberto Santana, class of 2011.
沃伦·巴菲特和比尔·盖茨捐出了大量资源。税收体系转移了大量资源。所以完全有可能把这件事处理好。这并不意味着一定会如此，但我确实认为，富足的世界是一个值得期待的美好世界。我们只需要解决再分配这个微妙而复杂的问题。但对此我比较乐观。还是那句话。说我盲目乐观也行。好。【主持人】这将是我们的最后一个问题。【罗伯托·桑塔纳】哦，哇。好的。我叫罗伯托·桑塔纳，2011 届。
13问答：DeepMind 员工质疑渐进假设
57:34
Yay! [ROBERTO SANTANA] I work at Google DeepMind. And I'm trying to reconcile the model you're showing with the natural experiments we're running, and they don't match up. So I think that the challenge I'm having with the model that you provided is that, it assumes that the constraint, that weak link constraint, is a human, and that the AI cannot take or improve in those tasks. So the coordination, judgment, taste, I've heard it in many different ways. And so it appears to me that the model gradual Gradualism is based on an assumption that AI has to be gradual.
太棒了！【罗伯托·桑塔纳】我在 Google DeepMind 工作。我一直在试着把你展示的模型和我们正在做的自然实验对应起来，但它们对不上。所以我觉得，你提供的这个模型让我感到困惑的地方在于，它假设了那个约束、那个薄弱环节的约束，是人类，而且假设 AI 无法接手或改进那些任务。也就是协调、判断、品味，我听过很多种不同的说法。所以在我看来，渐进主义（Gradualism）这个模型是建立在一个假设之上的，即 AI 必须是渐进的。
58:22
And so that is the piece that I'm trying to reconcile. [DARON ACEMOGLU] Yeah, yeah, I know. This is totally fair. And in some sense, that's-- your reaction is entirely the point of the paper because I went into it. I'm in Silicon Valley, so I hear and see and read all the things that say the world is changing tomorrow, and I'm definitely open to that. I write down the model, calibrate it to historical data. I haven't told you how I calibrated, how strong are the weak links? That's obviously a key question here.
所以这就是我想要理清的那一部分。[达龙·阿西莫格鲁] 是的，是的，我明白。这个质疑完全合理。从某种意义上说，你的这种反应正是这篇论文的意义所在，因为我当初就是带着这个问题去做的。我人在硅谷，所以我听到、看到、读到的都是说世界明天就要天翻地覆的说法，我对此绝对是持开放态度的。我把模型写下来，用历史数据去校准它。我还没有告诉你我是怎么校准的，那些薄弱环节到底有多强的约束力？这显然是这里的一个关键问题。
58:53
I don't have time to go into it now. You could argue we've calibrated that to be too strong. But again, the 50-year thing takes a long time. The example I like when I think about is: Is this plausible? Or are my friends in Silicon Valley who draw this line happening in three years? The Waymo example is really compelling to me because Waymo people were saying, like I said, in 2012, "This is going to be a solved problem." Think about how easy it is to solve a self-driving car problem. You turn the wheel left, you turn the wheel right, you step on the gas, you step on the brake.
我现在没有时间展开讲。你可以说我们把这个校准得太强了。但话说回来，五十年这个时间跨度是很长的。我思考这个问题时喜欢举的例子是：这可信吗？还是说我那些硅谷的朋友们画的那条线，三年内就会发生？Waymo 的例子对我来说非常有说服力，因为就像我刚才说的，Waymo 的人在 2012 年就说，“这个问题即将被彻底解决。”想想看，解决自动驾驶汽车这个问题该有多容易。你把方向盘往左打，往右打，踩油门，踩刹车。
59:24
That's all you have to decide. Right? We have every sensor in the world, incredibly, machine learning algorithms that can do math better than me. Simple problem, seemingly, and yet not a simple problem. Why? Because there are all sorts of bottlenecks, weak links. The physical world is complicated. The cognitive world, I'm very open to the cognitive stuff going much faster, but to get that graph going, think about computers. Computers' factor share has gone down. You've got to automate everything else in the physical world, and I think that's really kind of what is the intuition behind the slowdown.
你要做的决定就这些。对吧？我们拥有世界上所有的传感器，还有算数比我强得多的、不可思议的机器学习算法。看起来是个简单的问题，可结果却并不简单。为什么？因为存在着各种各样的瓶颈、薄弱环节。物理世界是复杂的。至于认知层面的世界，我非常愿意接受认知类的进展会快得多，但要让那条曲线走起来，想想计算机吧。计算机的要素份额一直在下降。你必须把物理世界里其他所有的东西都自动化，我觉得这差不多就是「放缓」这一判断背后的直觉。
59:57
But is it going to happen in 20 years or 100 years? I don't know. I'm pretty sure it's not going to happen in five years, right? Based on this weak link argument. But yeah, anyway, thank you so much. Hope you have a wonderful time at your reunion. (applause) (upbeat music) (silent)
但它会在 20 年内发生，还是 100 年内发生？我不知道。我相当确定它不会在五年内发生，对吧？这是基于薄弱环节这个论证得出的。不过，总之，非常感谢大家。祝你们在校友聚会上玩得开心。（掌声）（欢快的音乐）（静默）
视频总结 · 一句话概括与核心要点
一句话概括
斯坦福经济学家 Chad Jones 用"弱链接"（weak links）模型论证：AI 最终会让经济增长爆发式加速，但因为生产链上未被自动化的瓶颈始终存在，这一爆发会比硅谷预期慢得多（30–100 年而非 3–5 年），而下行风险（网络攻击、生物武器等灾难性风险）却可能更早到来。
核心要点
- 两个极端情景都有道理，真相在中间。 情景一是硅谷的"FOOM"叙事：AI 先自动化软件工程（Claude Opus 4.5 在 Anthropic 两小时招聘笔试中超过所有人类），再自动化 AI 研究本身，形成"数据中心里的天才国度"，进而设计机器人自动化物理任务，增长模型中增长率趋于无穷。情景二是"AI 只是又一项普通技术"：美国 150 年人均实际收入在对数坐标上始终贴着 2%/年的直线，电力、内燃机、抗生素、半导体、互联网都没有把这条线掰弯。
- 2% 增长率的稳定不是因为技术不重要，而是因为"点子越来越难找"。 每一类技术内部的回报都会递减（"蒸汽机会没蒸汽"），若没有下一项变革性技术，增长曲线本应向下弯曲。每项变革性技术的作用是让 2% 再延续 50 年；而且经济史表明蒸汽转电力、IT 扩散都需要几十年的工厂重组和互补性创新。
- "弱链接"是理解 AI 影响的核心概念：链条的强度取决于最弱一环。 生产 iPhone 需要设计、采购、制造、公差控制、物流、零售等上百个任务，任何一环失败价值就大打折扣（挑战者号因 25 美元的 O 型圈爆炸）。20 个环节中把 17 个变得极强，链条强度仍由剩下 3 个决定。口袋里的手机晶体管数是 1970 年代的 1 亿倍，但研究者生产率只提高了两三倍。
- 弱链接 = 稀缺 = 高回报，而计算机的 GDP 份额正在下降。 支付给计算机的 GDP 份额在 2000 年互联网泡沫顶峰约 4.5%，此后下降三分之一至约 3%——价格下跌效应压倒数量增长效应。这正是弱链接模型的预测：无限供给的要素回报趋近于零，稀缺的人类成为拿走份额的一方。
- 自动化单一任务的收益等于该任务的 GDP 份额。 模型给出简洁公式：把某任务推到无限供给，GDP 只提高该任务的份额。软件约占 GDP 的 2%，因此"无限软件"只让我们富 2%。结论是必须持续自动化弱链接，需要动态模型。
- 保守校准下增长会加速但极慢；激进校准下 30 年才完成爆发。 模型结合"自动化→新点子→更多自动化"的正反馈飞轮与弱链接约束，校准到 1950 年代以来的美国数据。若 AI 只是延续历史自动化速率：基线增长率 2050 年约 2.3%，2075 年比趋势富 15%，最终趋向 50%/年但需数百年；且未来 75 年三种情景（全自动化/3% 任务保留给人类/资本份额稳定）几乎无法区分。若"全经济体摩尔定律"（机器每年进步 10% 而非 3%）：2020 年即应有 4.7% 增长（与现实不符，说明过于激进），2040 年 7–13%，2050 年超 25%，但爆发要到 2060 年才完成。
- 长期分配取决于人类是否保留不可自动化的任务。 若一切可自动化，资本份额趋向 100%、劳动趋向 0；若像 Magnus Carlsen 下棋、Messi 踢球那样有 3% 任务保留给人类，人类反而成为唯一瓶颈，劳动份额趋向 100%。中间的基线情景资本份额稳定在约 38%。
- 就业上"工作是任务束"，自动化 75% 的任务可能反而提高工资。 Hinton 2016 年预言 5 年内放射科医生消失，但如今放射科医生更多、薪酬更高，因为 AI 让他们在会诊、复核疑难扫描等剩余任务上更有价值。反例是 Uber 司机——Waymo 把全部任务都自动化了，但自动驾驶从 2004 年 DARPA 挑战赛至今 20 多年仍局限于湾区，说明物理世界的弱链接极多。他预测软件工程师是第一个被自动化的职业，但因为把 AI 集成进每家企业本身是漫长过程，10 年后可能仍有大量软件工程师。
- 弱链接模型上行慢、下行脆。 收益要等所有弱链接被加强，但断掉一环就损失全部价值。Anthropic 未公开发布的 Mythos 模型已能在经过 25 年实战检验的软件中发现数千个人类没找到的漏洞；一旦开源版本出现，坏人可能攻击电网、金融系统或联系生物实验室设计病毒。核武器之所以安全是因为"红按钮"只有少数人握有，若 80 亿人都有按钮则难以保证无人按下。他认为这类非存亡但严重的风险在未来 3 年内有相当概率发生。
结论与值得注意的细节
- Jones 自称靠那条 2% 直线拿到终身教职，但在 AI 世界里他运行的所有情景都显示增长在 50–100 年内爆发；"爆发"是真的，只是远没有字面听起来那么快。AI 在 2015–2045 年的变革价值"抵得上多个互联网"，30 年而非 5 年不意味着影响不巨大。
- 应利用这段缓冲期为不平等、劳动力市场、政治经济和灾难性风险做准备；他自述"对未来非常紧张"，并区分了"坏行为者"与"外星智能"（Stuart Russell：如何永远保持对比我们更强大实体的控制权）两类风险。
- 关于不平等的乐观论据：AI 世界是丰裕世界，维持现有再分配制度下底层 10% 的消费也会上升；创意/认知劳动比电工水管工更早被替代，短期反而可能压缩不平等；持有 S&P 500 股份者能分享资本收入，政府通过税收也"持有 GDP 股份"。他给年轻人的建议是管理与决策能力（人类做最终裁决、与 AI 协商）在 15 年内仍稀缺，并"买 S&P 500"。
- 意义问题他以退休和"夏令营"类比：当 AI 写出比他更好的增长论文时，他会去做陶艺、和同行一起让 AI 讲最新模型。
- 问答中承认的局限：GDP 长期低估福利（Nordhaus 调查显示 20 世纪寿命延长的价值与 GDP 增长相当）；短期冲击（整块行业突然消失）尚无模拟，后续论文正在做；全球经济层面（发展中国家没有 S&P 500 索取权）几乎无人研究；服务业（幼儿园老师、夜间陪护阿尔茨海默病患者的护士）可能是最顽固的弱链接。
- DeepMind 员工的最后一问直指模型核心假设——弱链接是否必然是人类且 AI 无法逐步改进它。Jones 承认弱链接强度的校准是关键且可争议，但坚持 Waymo 案例（看似只需左右转、油门刹车的问题 20 年未解）说明物理世界的瓶颈真实存在：认知任务可能快得多，但"5 年内不会发生，20 年还是 100 年不知道"。
核心句型 · 9
1. A chain is only as strong as its weakest link.
“A chain is only as strong as its weakest link.”
「X 只强于其最弱一环」的谚语结构，用于说明整体受最差部分制约。可仿写：A team is only as strong as its least committed member.
2. How can it simultaneously be true that … and yet …?
“How can it simultaneously be true that these technologies were wildly transformative, and yet growth rates still 2%”
提出悖论式问题的句型，先并置两个看似矛盾的事实，再引出解释。适合论文和演讲中引入核心谜题。
3. It used to be that …; now …
“It used to be that driving your car was done by hand, now in San Francisco, the computer does it”
今昔对比结构，强调变化。第二句常省略 that，口语中可连续排比多组以增强节奏。
4. Just because … doesn't mean …
“Just because it takes 30 years instead of five years doesn't mean the effects won't ultimately be huge”
否定错误推论的经典句型：「仅仅因为 A，并不意味着 B」。注意主语是 just because 从句，谓语用 doesn't mean。
5. Contrary to what you might have taken from …, I'm not …
“Contrary to what you might have taken from the talk so far, I'm not a total optimist on this”
用于纠正听众可能形成的印象，引出转折。take from 表示「从……中得到的理解」，适合演讲结尾调整基调。
6. Ask yourself, do you think …? And it's easy to say no. But let me give you the scenario where we say yes.
“Ask yourself, do you think we'll have as many software engineers 10 years from now as we do today? And it's easy to say no. But let me give you the scenario where we say yes.”
先让听众给出直觉答案，再提出反方情景。三步结构（设问—承认直觉—反转）是说服性演讲的常用套路。
7. What if …? How much … would we be?
“What if we had infinite amounts of software rather than finite amounts of software? … How much richer would we be?”
思想实验的引入句型：what if 设定极端假设，再用 how much + 比较级追问后果。适合分析性写作。
8. X is very slow to Y, but it's very fragile on the downside.
“A weak link model is very slow to improve, but it's very fragile on the downside”
「非对称」描述结构：be slow to do 表示改善缓慢，on the downside 指负面方向。可用于风险、投资、组织等语境。
9. I'm pretty sure it's not going to happen in …, based on …
“I'm pretty sure it's not going to happen in five years, right? Based on this weak link argument.”
在承认不确定后给出有把握的下限判断，并附依据。学术表达中「不知道 A 还是 B，但确定不是 C」是稳妥的立场陈述法。
生词精讲 · 112 · 按出现顺序
transformative
/trænsˈfɔːrmətɪv/
adj.
0:37
变革性的，带来根本改变的
pondering
/ˈpɑːndərɪŋ/
v.
0:37
深思，反复琢磨（ponder 的现在分词）
gets to the heart of
phr.
1:13
切中要害，触及核心
caricatures
/ˈkærɪkətʃərz/
n.
1:13
漫画式夸张；此处指刻意简化的极端版本
business as usual
phr.
1:58
一切照旧，没有本质变化
luminaries
/ˈluːmɪneriz/
n.
2:36
泰斗，杰出人物
marching along
phr.
2:36
按部就班地推进（沿着既定路线前行）
take-home exam
n.
3:08
带回家完成的考试/笔试
plausible
/ˈplɔːzəbl/
adj.
3:40
貌似合理的，说得通的
scale these things up
phr.
4:16
规模化扩展，大规模部署
grippers
/ˈɡrɪpərz/
n.
4:58
（机器人）机械手爪，夹持器
bottleneck
/ˈbɑːtlnek/
n. / v.
4:58
瓶颈；卡住、限制（全篇高频核心词）
breaks down
phr.
5:35
（论证、故事）失效、站不住脚
horizon
/həˈraɪzn/
n.
6:12
（时间）跨度、期限；经济学中常指预测期
has some merits
phr.
6:12
有其可取之处
logarithmic
/ˌlɔːɡəˈrɪðmɪk/
adj.
6:43
对数的（对数刻度上直线=恒定增速）
a glimmer in Thomas Edison's eye
phr.
7:19
仿自 a twinkle in one's eye，指尚处萌芽、仅是构想阶段
diffused
/dɪˈfjuːzd/
v.
7:19
扩散，普及（技术在经济中推广）
counterfactual
/ˌkaʊntərˈfæktʃuəl/
n.
7:59
反事实情形（若某事未发生会怎样）
runs out of steam
phr.
8:43
后劲不足，动力耗尽（此处兼有蒸汽机双关）
in quotes
phr.
9:19
「加引号的」，表示该词并非字面意义
complementary
/ˌkɑːmplɪˈmentri/
adj.
10:01
互补的，配套的
bend the curve
phr.
10:31
改变趋势曲线的走向
source all the parts
phr.
11:05
采购/寻找所有零部件来源
tolerances
/ˈtɑːlərənsɪz/
n.
11:05
（工程）公差，允许的误差范围
falls apart
phr.
11:44
崩溃，瓦解
in a timely fashion
phr.
11:44
及时地，按时地
invert matrices
phr.
13:03
求矩阵的逆（线性代数运算）
like nobody's business
phr.
13:03
（口语）极其出色/飞快地
scarcity
/ˈskersəti/
n.
13:41
稀缺性（经济学核心概念）
gives rise to
phr.
13:41
引起，导致
infatuated with
/ɪnˈfætʃueɪtɪd/
adj.
14:16
迷恋的，痴迷于
market power
n.
14:56
市场势力（企业影响价格的能力）
concentration
/ˌkɑːnsnˈtreɪʃn/
n.
14:56
（市场）集中度
dig
/dɪɡ/
v.
16:05
深挖，费力查找（数据）
dominates
/ˈdɑːmɪneɪts/
v.
16:05
占主导，压过（另一效应）
plentiful
/ˈplentɪfl/
adj.
16:49
充裕的，大量的
production function
n.
17:31
生产函数（投入与产出的数学关系）
endogenously
/enˈdɑːdʒənəsli/
adv.
18:10
内生地（由模型内部机制决定，而非外部设定）
calibrate
/ˈkælɪbreɪt/
v.
18:45
校准（用数据确定模型参数）
at play
phr.
18:45
在起作用的
elegant
/ˈelɪɡənt/
adj.
19:54
（公式、证明）简洁优美的
flywheel effect
n.
20:38
飞轮效应（自我强化的正反馈循环）
textile looms
/ˈtekstaɪl luːmz/
n.
21:17
纺织织机
micro-founded
adj.
21:52
有微观基础的（宏观模型建立在个体行为之上）
a break with the past
phr.
22:32
与过去决裂，根本性断裂
splits off
phr.
23:03
分岔，分离开来
in finite time
phr.
23:36
在有限时间内
settling down at
phr.
25:44
稳定在（某一水平）
stunningly
/ˈstʌnɪŋli/
adv.
25:44
令人震惊地
in levels
phr.
26:21
以水平值（而非增长率）表示
aggregate economy
/ˈæɡrɪɡət/
n.
27:37
总体经济，宏观经济整体
well on the way
phr.
28:19
进展顺利，已走了很远
tenure
/ˈtenjər/
n.
29:51
（大学）终身教职
bet on
phr.
29:51
押注于，看好
takes over
phr.
30:33
接管，占据主导
radiologists
/ˌreɪdiˈɑːlədʒɪsts/
n.
31:41
放射科医生
documented
/ˈdɑːkjumentɪd/
v.
31:41
用证据记录、证实
bundles of tasks
phr.
32:14
任务的集合（经济学「任务模型」用语）
double-check
v.
32:14
复核，再次检查
completed the course
phr.
33:36
跑完赛道/全程
abundance
/əˈbʌndəns/
n.
34:19
富足，丰裕
redistribution
/ˌriːdɪstrɪˈbjuːʃn/
n.
34:19
（收入）再分配
plenty to go around
phr.
34:19
足够分给每个人
decimated
/ˈdesɪmeɪtɪd/
v.
34:57
严重摧毁，大量毁灭
analogy
/əˈnælədʒi/
n.
36:04
类比
pejorative
/pɪˈdʒɔːrətɪv/
adj.
36:30
贬义的，轻蔑的
bad actor
n.
37:03
恶意行为者（安全领域术语）
jailbroken
/ˈdʒeɪlbroʊkən/
adj.
37:03
被越狱的（绕过安全限制的）
oracle
/ˈɔːrəkl/
n.
37:03
神谕；此处指有问必答的全知系统
lethal
/ˈliːθl/
adj.
37:42
致命的
a handful of
phr.
37:42
极少数的
speculative
/ˈspekjələtɪv/
adj.
37:42
推测性的，缺乏实证的
retain power over
phr.
38:16
保持对……的控制权
intervening
/ˌɪntərˈviːnɪŋ/
adj.
38:51
（时间）介于其间的
fragile on the downside
phr.
39:21
在下行方向上脆弱（易受负面冲击）
battle-tested
adj.
39:21
久经考验的
zero out
phr.
39:57
清零
existential
/ˌeɡzɪˈstenʃl/
adj.
40:35
关乎生存的（existential risk 生存性风险）
around the corner
phr.
40:35
近在眼前，即将来临
political economy
n.
41:40
政治经济学（政治与经济互动的领域）
end on a down note
phr.
41:40
以低沉/悲观的调子结尾
counterintuitively
/ˌkaʊntərɪnˈtuːɪtɪvli/
adv.
42:31
反直觉地
monetized
/ˈmɑːnətaɪzd/
v.
42:31
被货币化，被变现
mismeasured
/ˌmɪsˈmeʒərd/
v.
43:08
被错误测量/低估
life expectancy
n.
43:38
预期寿命
holding constant
phr.
43:38
（分析中）保持某变量不变
a big chunk of
phr.
44:21
很大一块/一部分
trajectory
/trəˈdʒektəri/
n.
44:21
轨迹，发展路径
drawn-out
adj.
45:53
拖沓的，漫长的
double-click on
phr.
47:02
（商务口语）深入探讨某一点
variability
/ˌveriəˈbɪləti/
n.
47:02
变异性，多样性
No way
phr.
49:21
绝不可能
outsourced
/ˈaʊtsɔːrst/
v.
49:57
外包
doling out
/ˈdoʊlɪŋ/
phr.
50:36
发放，（大方地）分发
Gilded Age
n.
50:36
镀金时代（19 世纪末美国财富极度集中时期）
oligopoly
/ˌɑːlɪˈɡɑːpəli/
n.
50:36
寡头垄断
on steroids
phr.
50:36
加强版的，超级放大的
Fingers crossed
phr.
51:49
但愿如此，祈求好运
puts a floor
phr.
52:22
设定下限/托底
unchecked
/ʌnˈtʃekt/
adj.
53:39
不受约束/监督的
make the final call
phr.
53:39
做最终决定，拍板
all bets are off
phr.
53:39
一切都说不准了，之前的预测全部失效
claims on
phr.
54:38
对……的索取权/权益
well off
adj.
54:38
富裕的
implications
/ˌɪmplɪˈkeɪʃnz/
n.
55:21
影响，含义，后果
Pollyanna
/ˌpɑːliˈænə/
n.
56:58
盲目乐观的人（源自同名小说人物）
reconcile
/ˈrekənsaɪl/
v.
57:34
调和，使一致
natural experiments
n.
57:34
自然实验（非人为设计的现实观察）
Gradualism
/ˈɡrædʒuəlɪzəm/
n.
57:34
渐进主义
compelling
/kəmˈpelɪŋ/
adj.
58:53
有说服力的
factor share
n.
59:24
要素份额（某生产要素占收入的比例）
理解自测 · 11 题 · 是真懂了，还是以为自己懂
1. 讲者开篇提出了哪两个极端情景？他如何看待这两者？
两个极端分别是：一、AI 极大加速经济增长，即硅谷流行的「FOOM」情景，AI 自动化软件工程后递归改进自身，形成「数据中心里的天才之国」，最终自动化认知与体力任务，增长爆炸；二、AI 只是「照常发展的普通技术」，像电力、半导体、互联网一样，只是让 2% 增长再延续 50 年。讲者在第 2 段明言两者都是「漫画式」的极端，都不太可能原样发生，但各有道理；他的研究目的正是用模型定位「我们会落在中间哪里」。
2. 美国 150 年人均收入数据揭示了什么「谜题」？讲者的解释是什么？
谜题是：电力、内燃机、抗生素、晶体管、互联网等无数变革性技术先后进入经济，但人均实际收入在对数刻度上始终贴着一条年增 2% 的直线，从未明显加速。讲者在第 13–14 段的解释是「反事实」：任何单一技术门类内部，新想法会越来越难找到（「蒸汽机会用尽蒸汽」），若没有下一项技术接棒，增长本应放缓。因此每项变革性技术的作用不是让曲线上翘，而是阻止它下弯，让 2% 再延续 50 年。
3. 「电脑回报占 GDP 份额」这一数据是怎样变化的？它为何支持薄弱环节模型？
根据 BEA/BLS 数据，电脑资本回报占 GDP 份额在 1990 年代互联网泡沫期间上升，2000 年见顶接近 4.5%，此后下降约三分之一至 3%。这看似矛盾——电脑无处不在、口袋里晶体管多了一亿倍，份额却更少了。讲者在第 26–27 段解释：份额是价格乘数量，价格暴跌压过了数量激增。薄弱环节模型正预测这一点：充裕的要素（电脑）不再稀缺，因而分不到高回报；真正稀缺的是人类仍须完成的其他环节，份额流向它们。
4. 模型给出的「无限软件只让 GDP 富 2%」这一结论，其推理逻辑是什么？
第 31–32 段的推理链：假设把软件投入推到无穷大，相当于彻底自动化软件工程。由于生产函数包含薄弱环节（各任务互补、缺一不可），某一任务投入变为无限时，GDP 只提高该任务在 GDP 中原本的份额。软件约占 GDP 的 2%，所以经济只富 2%。这与「晶体管多一亿倍、研究产出没多一亿倍」的电脑例子一致：单点突破后，其余未改变的环节立即成为新瓶颈。由此讲者得出：单个领域自动化得再好也不够，必须动态地、逐个地消灭薄弱环节，因此需要动态模型。
5. 为什么模型中即便有薄弱环节，长期增长最终仍会爆炸？为何这一爆炸「慢得惊人」？
第 33、48 段说明模型包含两个对抗力量：自动化→新想法→更多自动化的飞轮式正反馈，以及薄弱环节的拖累。因为自动化过程是内生的，随着机器改进，薄弱环节会被逐个自动化掉，一旦大部分环节被强化，飞轮就接管一切，增长发散。但在此之前，每一步都被尚未自动化的人类任务卡住，所以加速极慢。第 41–43 段给出量化：基准情景下增速最终达 50%/年，但要几个世纪；2050 年增速仅 2.3%，人均收入仅比趋势高 4%，2075 年高 15%。
6. 「激进情景」（全经济摩尔定律）的模拟结果如何？讲者从中得出什么教训？
第 44–46 段：假设从 2020 年起整个经济的机器每年改进 10%（而非基准 3%），模型显示 2020 年增速即为 4.7%，2040 年达 7%–13%，2050 年超 25%，2030 年比趋势富 50%。讲者指出两点：一是过去六年美国并未出现 4.7% 增长，说明该情景「太激进」；二是即便如此，爆炸也要到 2060 年才完成，耗时约 30 年，远慢于 AI 2027 派的「四年内」。教训是：无论校准多激进，薄弱环节都会把爆炸拉长到数十年尺度。
7. 放射科医生与 Uber 司机的对比说明了自动化对工资的什么规律？
第 50–52 段：Hinton 2016 年预言五年内放射科医生失业，但据《Works in Progress》文章，今天美国放射科医生数量更多、薪酬更高。讲者用「工作是任务集合」解释：一份工作含上百项任务，AI 自动化 75 项后，剩余 25 项成为稀缺的薄弱环节，从业者因 AI 辅助更高产、更有价值（会诊手术、复核难片），工资反而上升。相反，Waymo 正在自动化 Uber 司机的全部任务，没有残余的稀缺环节，岗位可能消失。规律是：部分自动化提升工资，完全自动化消灭岗位，关键看是否留有人类不可替代的任务。
8. 讲者所谓薄弱环节的「双面性」是什么？它如何改变对 AI 风险时间表的判断？
第 62–66 段提出：链条模型在上行方向很慢——必须强化所有环节才能获得全部收益；但在下行方向极其脆弱——如挑战者号 O 型圈，断一环即失去全部价值。推论是收益与风险的时间表不对称：AI 的经济红利要 30 年而非 5 年，但破坏能力（如 Mythos 已能在久经考验的软件中发现数千漏洞）扩散后，攻击电网、金融系统或协助设计病毒的风险可能在三年内出现。因此讲者主张利用「中间这些年」为不平等、劳动力市场、政治经济和灾难性风险做准备。
9. DeepMind 员工桑塔纳对模型提出了什么方法论质疑？讲者如何回应？这一回应是否充分？
第 95 段的质疑是：模型假设薄弱环节是人类、且 AI 无法改进协调、判断、品味等任务，因此「渐进」结论其实是「渐进」假设的产物，与他们在 DeepMind 观察到的「自然实验」不符。第 96–99 段讲者承认这「完全合理」，校准强度是关键且可争议，并承认认知领域可能快得多。但他以自动驾驶为反证：决策空间极简（左右油门刹车）、传感器和算法齐备，2012 年就被宣称「即将解决」，20 年后仍局限于少数城市——说明物理世界的瓶颈远超预期，而增长爆炸要求自动化整个物理世界。回应有力但不完全：他没有正面回答「AI 能否接手判断与品味」，而是把论证重心移到物理世界；若 AI 驱动的机器人也突破，慢结论的根基会削弱。
10. 如果有人反驳「GDP 测不到 AI 创造的免费价值，所以增长率被低估」，讲者会如何回应？
第 69–71 段实际发生了这一交锋。讲者的回应分三层：一、承认 GDP 确实被错误测量，但它一直如此——抗生素、20 世纪预期寿命从 50 岁升至 77 岁都未被充分计入；Nordhaus 的调查显示人们认为寿命收益与 GDP 增长同等重要，说明历史上的 2% 也系统性低估了福利。二、因此测量偏差不必然改变「AI 与历代技术类似」的比较。三、他保持开放：若偏差随 AI 越来越大，则真实福利改善会比模拟更快，因为模拟假设测量方式不变。也就是说，他把这一质疑视为「结论可能偏保守」的修正，而非对薄弱环节框架的否定。
11. 把「薄弱环节是稀缺性来源」的框架用于个人职业选择，讲者给出了什么建议？这一建议在发展中国家语境下还成立吗？
第 87–88 段：讲者认为管理与决策能力将长期稀缺，因为社会不会让 AI 不受监督地做最终决定，管理者是「咨询 AI 后拍板」的那一环；他估计商学院学生还有约 15 年安全期，并建议持有标普 500 以分享资本收入。但第 90–91 段他自己承认这套逻辑依赖两个条件：本国拥有 AI 公司股权，以及有税收再分配体系托底。发展中国家既无对标普 500 的索取权，内部不平等又极严重，美国也不会向全球穷人大规模再分配，只能靠「发明的思想」外溢。因此该建议在全球南方语境下成立度很低，讲者亦称这是「几乎无人研究」的领域。
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