普林斯顿教授 Narayanan 在 ICML 2026 发表主旨演讲：AI 作为正常技术，人类应主动适应而非躺平

上周，我有幸在首尔举行的国际机器学习大会 (ICML) 上发表主题演讲：https://icml.cc/virtual/2026/invited-talk/67274，题为《我们将剩下什么工作可做？》。我讨论了随着人工智能能力提升，我们应如何适应的普遍焦虑。我对演讲的反响感到非常兴奋，因此我在这里提供了我的幻灯片：https://www.cs.princeton.edu/~arvindn/talks/icml-2026-annotated-slides/，其中附有稍作编辑的演讲稿。你也可以直接在本页面下方查看，但在线版本：https://www.cs.princeton.edu/~arvindn/talks/icml-2026-annotated-slides/ 拥有动画效果、可点击的链接，并提供更佳的使用体验。

我提出了三个论点。首先，‘人工智能作为普通技术’的框架是正确且有用的，用以思考人工智能的影响，除非将来出现某种断裂性事件，例如递归自我改进。第二，尽管我们应认真对待递归自我改进，但没有任何实验室里公司可能达成的里程碑会突然让我们全部失业。第三，也是最后一点，未来的工作将会发生彻底变化，需要大量的适应。我分享了我的思考：这可能会呈现出怎样的变化，并以人类/人工智能“共同超智能”的愿景作结。

现在是人工智能领域的激动人心时期，但同时也是人工智能社区极度焦虑的时期。我希望直面这种焦虑。我们如何为那个人工智能将能够完成越来越多我们今天所做工作的未来做好准备？

我在普林斯顿大学领导一个团队，致力于推进人工智能代理评估科学：https://sage.cs.princeton.edu/。我们试图超越通常的说法“看，能力在基准测试上提高了！” 这些说法往往被公众误解为暗示代理即将取代我们的所有工作。

也许这种情况会发生。但在我们的工作中，我们试图理解能力之外对实际部署重要的因素，并将这种理解融入评估中。

我更为人所知的作品是我与Sayash Kapoor共同撰写的论文《AI作为常规技术》：https://knightcolumbia.org/content/ai-as-normal-technology。它提供了一种思考中期人工智能未来的方法，以及如何适应人工智能——进而如何将其适应社会和经济的需求的方法。

所以我们一直在撰写这些关于律师应该如何适应，或者记者应该如何适应的文章。但或许讽刺的是，首先面临适应问题的是我们自身的社区。无论是软件工程还是人工智能研究本身，这些领域的人工智能能力当然都在迅速发展。

我们对这一时刻的反应，超越了这个社区的重要性。整个世界都在关注。如果我们只是顺从接受未来很多工作将由人工智能完成，而不是设定明确的界限，我认为这将导致对人工智能的政治反弹比现在看到的还要强烈。因此，我认为这个问题不仅仅是针对我们，而是针对全世界的。

从人工智能诞生之初，历史上就存在这两种交锋的叙事。在过去，这一区别主要是学术和哲学上的，但现在已成为一个非常实际的问题。我们每个人都必须决定自己属于哪一阵营，或者在这个光谱中的位置，因为相信其中一种叙事与另一种叙事的实际后果非常非常不同。

如果你认为这是一项在几年内就能够取代我们今天所做一切的技术，那么也许正确的反应是在我们的技能变得无用之前尽快积累财富。而这是硅谷许多人选择的道路。你可能听说过“永久下层阶级：https://www.nytimes.com/2026/04/30/opinion/ai-labor-work-force-silicon-valley.html ”的说法。

另一方面，如果你像我一样相信，这是一项能够极大地放大我们潜力的技术，那么现在正是建立技能的最佳时机——尤其是那些将与人工智能正在做和未来能够做的事情互补的技能——以及构建围绕它的一切事物，例如自主性、品味和判断力。

如果你选择了第一条路径，而事实证明人工智能最终是作为增强技术而不是替代技术，那么我会认为在接下来的几年里，你可能已经错过了历史上最好的时机去培养这些将赋予我们超能力的技能。这就是为什么我们都需要思考这个问题，即使我们最终不会都走到同一个地方。

人工智能作为普通技术是我今天演讲的智识框架。当我们说人工智能是普通的，我们并不是说它就像锤子或牙刷一样，是某种平凡的技术。

我们在文章中明确地承认，这是与工业革命同等规模的变革性技术。我们并不是人工智能怀疑论者。

这不是一句口号。这是一个框架——有点像一个因果模型，说明人工智能能力如何影响经济和社会。这是一篇1.5万字的文章，我们正在把它改编成一本书。我提到这一点是因为人们经常听到“普通”这个词时，他们会认为自己明白我们的意思，但这会导致误解。

首先，我将论证，这个框架是正确且有用的，是思考人工智能影响的方法，除非将来出现某种不连续事件——比如通过递归自我改进——导致未来影响与过去影响看起来截然不同。

第二，我会讨论为什么，尽管我们应该认真对待递归自我改进，但我并不会为此特别焦虑不安。

第三也是最后，我想明确表示，我并不是说未来的工作会和现在的工作完全一样。将需要大量的适应。因此，我想提供一些初步思考，说明我认为我们的角色将如何变化，以及我们如何才能最好地适应这些变化。

过去的强大技术，如电力，已经得到了彻底研究，我们有很好的框架来理解技术进步如何带来经济影响。

发明：发现电磁学原理，交流与直流的区别等等。

创新：人们不会直接使用“电力”。我们使用电器。这些必须被发明出来，所以这是一种下游创新——即框架的第二阶段。

扩散：指人们逐渐开始采用创新的过程。

在我们的文章中，我们将这一框架应用于人工智能，并将其扩展成一个四部分框架。

基本情况如下，以软件工程为例。方法/能力：模型正在快速改进。产品/应用：我们并不直接使用大型语言模型。它们在我们所有工作中如此有影响力的原因是因为编码代理。这些是将潜在能力转化为对工作者有用且可用的产品。早期采用：起初人们主要尝试“感觉编码”，现在我们知道，这并不是开发生产软件的最佳方式——因此现在我们有了更复杂的代理工程方法。适应：（或结构性转变）——第四阶段，也是最慢的阶段。我今天演讲的很大一部分将讨论这一点。我认为这个阶段需要几十年。即使在像软件工程这样对编码代理相对早期采用的领域，它实际上还没有真正开始。

我们不知道适应阶段会是什么样子——只能进行推测。请允许我推测一下。如果未来编码代理能够创建一千万行代码的代码库，并且不会充满漏洞和安全隐患，那么我们创建一个让数十亿人使用的软件就显得没有多大意义。让软件根据每个人或每个团队的需求量身定制会更有意义。这就是我所说的极端个性化。

这不仅仅是技术上的变化——这也是行业的变化。例如：我们还需要软件公司吗？也许软件开发将大幅向内部转移，进入实际使用软件的公司和团队。同样，这是推测，但关键是，这种组织变革和人类变革——非常缓慢，需要几十年——将使我们能够充分利用人工智能的潜力，无论是在软件工程还是其他领域。这就是文章的核心见解之一。当我们回顾过去的技术时，这种变化通常非常缓慢。

在电力出现之前，工厂通常像左边的图片那样。一个大型蒸汽机产生动力，并通过机械齿轮和皮带在工厂中传输。因此，当电力出现时，工厂主试图用电力发电机取代这些蒸汽锅炉。他们认为这样会更加高效。但这种直接替换的想法并没有奏效。我们今天在人工智能代理的语境中经常听到这个术语——它们将成为人类工人的直接替代品。在电力的情况下，这并没有奏效。

真正奏效的做法，而且花了40年才发展起来的是，认识到电力是一项非常不同的技术。它是可移动的，所以你可以将电力传送到你需要的地方。这允许你围绕装配线逻辑重新组织整个工厂布局。而这又要求改变工人的培训、招聘和解雇方式，制定新的劳动法等等。所以，为了在工厂中获得电力的好处，需要进行这种类型的组织适应。

我们的观点是，这就是人工智能将带来的过程类型。几十年后，我们将从根本上重新组织工作。我们不知道它将会是什么样子，这是摆在我们所有人面前的挑战。这不仅仅是人工智能公司需要完成的任务，就像电力公司并不负责要弄清楚工厂应该如何重新组织一样。在我们看来，这是人工智能导致经济影响的四个阶段中最慢的阶段。今天，这个过程实际上还没有真正开始。

为什么不同职业的人们在使用人工智能方面存在巨大的差距，不论是可以用它做的事情还是实际在做的事情？其中一个原因可能是人们采用新技术较慢，这无疑是我们框架中的一部分。

但我们想知道，也许那些正在部署人工智能但并不十分成功的人，了解一些人工智能实际限制，而人工智能行业却不了解。让我们对能力与部署之间的关系保持一些谦逊。

鉴于人们最关心的问题是可靠性，我们希望尝试衡量可靠性是否是人工智能代理实际有用性的一大障碍，并与能力区分开来。

我们观察了10-12个可靠性指标，并将它们聚类为四个维度。一致性：假设我们听说一个AI代理的准确率为70%。这是否意味着它能完成70%的任务，而且在完成的任务中每次都能成功？这对于部署来说非常好——你可以在这部分任务上部署它。或者，它是否意味着在任何给定任务中，它可能会以30%的概率不可预测地失败？从部署角度来看，这几乎没用。或许令人惊讶的是，我们研究的代理基准都没有区分这两者。两者都被表现为70%的准确率。鲁棒性：我们观察了鲁棒性：当环境稍微变化时会发生什么？校准：代理是否能够回顾自己的记录，并判断其任务是否正确完成？运行安全：当它失败时，是否可以恢复，还是会像删除生产数据库那样严重？

对于人类工作者，如果我们认为某人在某份工作上有能力，这涵盖了所有这些方面，而不仅仅是准确性。但事实证明，我们只是在测量代理的准确性。

我们使用两个互补的基准来测量能力和可靠性，对象是过去24个月左右发布的来自这三家前沿AI公司的模型。

这是一个准确性或能力显著上升的时期（左图）。

但可靠性（右图）仅增加了五到十个百分点。

有很多影响，但让我强调一点。目前行业并没有区分自动化代理和协作代理。如果你让一个代理以无界面模式运行，我猜你可以认为它成为了一个自动化代理。这种看法不太好，因为像可靠性这样对自动化代理非常重要的属性，对用于提升创意写作之类任务的协作代理来说可能实际上是一种障碍。对于那种代理，你不希望它像机器人一样每次都执行相同的操作。你希望它具有创造性，探索不同的可能性并且不可预测。

根据它是驱动协作代理还是自动化代理，搭建框架甚至模型的后期训练方式都应有所不同。

我的希望是可靠性会继续提高，并且随着时间的推移自动化会变得更容易。但目前我认为协作型代理将继续更加成功。许多仓促尝试使用代理自动化业务流程的公司正在认识到其中的局限性和成本，甚至法律责任——比如当代理删除生产数据时。

目前，你只能在代理中拥有以下三种属性中的两种：通用型（基于语言模型的代理，可以被指示执行不同任务，而不是像传统软件那样为特定任务构建）、部署在高风险场景中，以及自动化。

这也是我认为目前人工智能仍然更多地是一种协作技术，而不是一种能替代员工的技术的原因之一。

让我们以软件工程为案例研究。这是一个很好的先行指标，因为编码代理被采用得尤其迅速。

你可能会想：好吧，我们无法完全自动化软件工程。但如果代理让软件工程师的生产力提高十倍，那么我们就需要十倍更少的软件工程师。这不是显而易见的结果吗？

嗯，数据完全与此相矛盾。我们在后续的文章中进行了观察。在我们查看的每一个案例中，公司都面临财务压力，而将裁员归咎于人工智能反而更为方便。

为什么人工智能到目前为止没有替代软件工程师？我们早已知道——这是2019年的一篇论文——编写代码实际上并不是瓶颈。

在过去一年里，当软件工程师开始采用编码代理并开始意识到它似乎并没有减少他们的工作量时，许多博客文章重新发现了编写代码并不是瓶颈的事实。这里是一个小样本。

那么，实际的瓶颈是什么？

这个框架是我们对这个问题的回答。

决策层：理解客户需求、制定规范、进行规划等。人工智能并没有压缩这部分工作。

执行层：实际的编码和调试。这部分工作确实被压缩了，但它在最初可能仅占三分之一的工作量。

交付层：深刻理解你的代码到足以对你发布的内容负责；进行客户系统的集成、维护、测试等工作。这个层次也不会被压缩。

事实上，随着人工智能压缩中间层，第一层和第三层可以说正在扩展——我会回到这一点。

我认为在软件工程中已经如此，并且在许多职业中这种情况将越来越普遍，我们可以把知识工作者看作类似于起重机操作员或叉车操作员。机器极大地放大了人体进行体力工作的潜力——机器承载了所有的重活，但人仍然保持控制权。我认为这正是认知工作中正在发生的事情。机器将越来越多地承担认知重活，但人仍然保持控制权。

整个工作被重新概念化为操作机器、理解机器和控制机器，而不是我们自己去做认知工作。

所有这些看起来可能像是巨大的变化，但在某种意义上，它只是软件工程中一次又一次发生的延续。从机器码时代开始，我们经历了多轮技术浪潮，每一轮都几乎为我们带来了数量级的生产力提升。更重要的是，在我们登上这一阶梯的过程中，软件工程的需求不仅没有减少，反而增加了大约一万倍。这仅仅是因为需要编写的代码数量已经增加了好几个数量级。

经济学家一再发现这一点。你可能听说过杰文斯悖论这个词；我喜欢用“劳动块谬论”这个词。

自动取款机使得银行在经济上能够开设大量的区域分行。而这些分行仍然需要人工出纳处理自动取款机无法处理的事务。因此，矛盾的是，反而就业量实际上增加了。

杰夫·辛顿十年前做出了著名的预测，说放射科医生将在五年内基本灭绝。但事实证明，放射学的就业实际上是在增长的。这并不是因为放射科医生拒绝 AI。他们实际上是热情地采用它。就业增长的一个原因是，当一项任务的执行速度更快、成本更低时，对它的需求也会增加。

我们写了一篇论文，探讨律师应如何适应。里面内容很多，但有一个简单的观点是，AI 让提起诉讼变得容易得多。这意味着律师的工作会更多。如果我们不喜欢生活在一个好诉讼的社会中，可能会对此不满，但从律师就业的角度来看，这是个好消息。

I had the honor of giving a keynote ：https://icml.cc/virtual/2026/invited-talk/67274 at the International Conference on Machine Learning in Seoul last week titled “What will be left for us to work on?” I addressed the widespread anxiety about how we should adapt as AI capabilities increase. I was thrilled by the talk’s reception, so I have made my slides available here ：https://www.cs.princeton.edu/~arvindn/talks/icml-2026-annotated-slides/ , annotated with a lightly edited transcript. You can also view them below right here on this page, but the online version ：https://www.cs.princeton.edu/~arvindn/talks/icml-2026-annotated-slides/ has animations, clickable links, and a much nicer experience overall.

I made three arguments. First, the AI as Normal Technology framework is a correct and useful as a way to think about AI’s impacts, unless and until there is some future discontinuity such as through recursive self-improvement. Second, even though we should take recursive self-improvement seriously, there is no milestone that companies might achieve in the lab that will suddenly put us all out of work. Third and finally, jobs of the future will be radically different, and a lot of adaptation will be needed. I shared my thinking about what this might look like and ended with a vision of human/AI “co-superintelligence”.

Now is a time of great excitement in AI, but it’s also a time of great anxiety in the AI community. I want to address that anxiety head on. How do we prepare for a future where AI will become capable of doing more and more of the work that we do today?

I lead a team at Princeton University trying to advance the science of AI agent evaluation：https://sage.cs.princeton.edu/ . We try to go beyond the usual claims of “Look, capability is going up on benchmarks!” Those claims tend to be misinterpreted by the broader public as implying that agents are soon about to take all our jobs.

Maybe that will happen. But in our work we try to understand the factors beyond capability that matter for real-world deployment, and bring that understanding into evaluations.

The work that I’m better known for is the essay I co-authored with Sayash Kapoor called AI as Normal Technology：https://knightcolumbia.org/content/ai-as-normal-technology . It’s a way to think about the medium-term future of AI and how to adapt to it — and in turn how to adapt it to the needs of society and the economy.

So we’ve been going around writing these essays about how lawyers should adapt, or maybe how journalists should adapt. But perhaps ironically, the question of how to adapt has been hitting our community first. Whether it’s software engineering or AI research itself, AI capabilities in these areas are of course advancing very rapidly.

Our response to this moment matters beyond this community. The whole world is watching. If we simply roll over and accept that a lot of our work will be done by AI in the future, instead of setting clear boundaries, I think it will lead to an even stronger political backlash against AI than what we are seeing today. So I think this question is not just for us but for the whole world.

From the beginning of AI, historically there have been these two battling narratives. In the past, the distinction was academic and philosophical, but now it has become an acutely practical question. Each one of us has to decide which camp we’re in, or where on this spectrum we’re in, because the practical consequences of believing in one versus the other are very, very different.

If you think this is a technology which in a few years is going to be able to replace everything we do today, then perhaps the correct response is to build wealth as quickly as possible before our skills become irrelevant. And this is the path that many have chosen in Silicon Valley. You may have heard of the “ permanent underclass：https://www.nytimes.com/2026/04/30/opinion/ai-labor-work-force-silicon-valley.html ” meme.

On the other hand, if you believe, as I do, that this is a technology that will greatly amplify our potential, then now is the best time to build skills — especially the skills that are going to be complementary to what AI is doing and is going to be able to do — as well as to build all the things around it, such as agency and taste and judgment.

If you choose the first path, and it turns out that AI actually ends up being an amplifying technology as opposed to a replacing technology, then I would argue that over the next few years you’ve perhaps lost the best time in history to build these skills that will give us superpowers. That’s why we all need to think about this question, even if we won’t all land in the same place.

AI as Normal Technology is the intellectual framework for my talk today. When we say AI is normal, we don’t mean that it’s just like a hammer or a toothbrush, some kind of mundane technology.

We acknowledge prominently in the essay that this is a transformative technology on the scale of the industrial revolution. We’re not AI skeptics.

This is not a slogan. It’s a framework — sort of a causal model how AI capabilities impact the economy and society. It’s a 15,000-word essay and we’re turning it into a book. And I mention that because people often hear the word normal and they assume they know what we mean, but that leads to misunderstandings.

First, I’ll argue that this framework is correct and useful as a way to think about AI’s impacts, unless and until there is some future discontinuity — such as through recursive self-improvement — that leads to future impacts looking very different from past impacts.

Second, I will talk about why, even though we should take recursive self-improvement seriously, I’m not particularly losing sleep over it.

Third and finally, I want to be clear that I’m not saying that jobs of the future will be just like jobs of the present. A lot of adaptation will be needed. So I want to give some preliminary thinking on how I think our roles are going to change and how we can best adapt to the changes.

Powerful technologies of the past, like electricity, have been thoroughly studied, and we have good frameworks to understand how technological progress leads to economic impacts.

Invention: discovering the principles of electromagnetism, AC versus DC, etc.

Innovation: People don’t use “electricity” directly. We use electrical appliances. Those had to be invented, so that is a kind of downstream innovation — that’s the second phase of the framework.

Diffusion: This refers to the gradual process by which people start adopting innovations.

In our essay we apply this framework to AI and flesh it out into a four-part framework.

Here’s the basic picture, illustrated with software engineering as an example. Methods/capabilities: Models are rapidly improving. Products/applications: We don’t use LLMs directly. The reason they’ve been so influential in all of our work is because of coding agents. These are products that take those latent capabilities and turn them into something useful and usable for workers. Early adoption: At first people were mostly trying vibe coding, and now we know that that’s not really the best way to develop production software — so now we have more sophisticated ways of doing agentic engineering. Adaptation: (or structural transformation) — the fourth and slowest phase. Much of my talk today is going to be about that. I claim that this stage takes decades. It has not really started yet, even in a field like software engineering, which is a relative early adopter of coding agents.

We don’t know what the adaptation phase will look like — we can only speculate. Permit me to speculate for a minute. If it’s going to be the case that coding agents are going to be able to create ten-million-line code bases in the future that are not full of bugs and security vulnerabilities, then it won’t make a lot of sense for us to create one piece of software that billions of people should use. It’ll make a lot more sense for software to be tailored to the needs of each individual or team. And that’s what I mean by extreme personalization.

It’s not merely a technological change — that’s also a change for the industry. For instance: do we even need software companies anymore? Maybe software development will massively shift in-house, into the companies and teams that are actually using the software. Again, this is speculation, but the point is that it is this kind of organizational change, human change — that’s very slow, that takes decades — that will allow us to take advantage of the full potential of AI, whether it’s in software engineering or in any other field. So that’s one of the central insights of the essay. When we look at past technologies, this kind of change tends to be very slow.

Before electricity, factories used to look like the picture on the left. A massive steam engine generated power and it was moved throughout the factory by mechanical gears and belts. So when electricity came along, factory owners tried to replace those steam boilers with electric generators. They thought it would be much more efficient. But this idea of a drop-in replacement did not work. We keep hearing that term in the context of AI agents today — that they will be drop-in replacements for human workers. That did not work in the case of electricity.

What actually worked, and what took 40 years to develop, is to recognize that electricity is a very different technology. It’s portable, so you can move the power to wherever you need it. That lets you reorganize the entire layout of the factory around the logic of the assembly line. And that required changing the way that workers are trained, hired, and fired, new labor laws, and so forth. So that’s the kind of organizational adaptation that it took in order to reap the benefits of electricity in factories.

Our claim is that this is the kind of process that we will go through for AI. A couple of decades from now, we will have fundamentally reorganized work. We don’t know what that’s going to look like, and that is the challenge in front of all of us. And that’s not just a job for the AI companies to do, much like it wasn’t the job of the electric utility to figure out how factories should be reorganized. In our view, this is the slowest of the four stages through which AI leads to economic impacts. Today, this process has not really gotten started.

Why is there a huge gap between what people in various occupations could be using AI for and what they’re actually using it for? One reason could be that people are slow to adopt technology, and that’s certainly part of our framework.

But we wondered if maybe the people who are deploying AI and are not having much success at it know something about the practical limitations of AI that the AI industry doesn’t. Let’s have a bit more humility about the relationship between capabilities and deployment.

Given that the #1 concern people cite is reliability, we wanted to try to measure whether reliability, distinct from capability, is a barrier to the practical usefulness of AI agents.

We looked at 10-12 reliability metrics and clustered them into four dimensions. Consistency: Suppose we hear that an AI agent has a 70% accuracy. Does this mean it works on 70% of the tasks, but on the ones that it does, it does so every time? That’s great for deployment — you can deploy it on that subset of the tasks. Or does it mean that on any given task it might unpredictably fail with a 30% probability? That’s pretty useless from a deployment perspective. Perhaps shockingly, none of the agent benchmarks that we looked into make a distinction between these two. Both of these are represented as 70% accuracy. Robustness: We looked at robustness: what happens when the environment changes a little bit? Calibration: Can the agent look back at its transcript and tell if it performed the task correctly? Operational safety: When it does fail, is it recoverable, or is it something like deleting the production database?

For a human worker, if we think of someone as being competent at a job, it’s all of these things, not just accuracy. But it turns out we were measuring agents only on accuracy.

We measured capability and reliability using two complementary benchmarks, for models from these 3 frontier AI companies that were released over the last 24 months or so.

This is a period during which accuracy or capability shot up dramatically (left).

But reliability (right) only increased by five or ten percentage points.

There are a bunch of implications but let me highlight one. Right now the industry is not treating automation and collaboration agents differently. If you run an agent in headless mode, I guess that becomes an automation agent. That is not a good way to look at it, because properties like reliability that are very important for automation agents can actually be a hindrance for a collaboration agent that you might be using to improve your creative writing or something like that. For that kind of agent, you don’t want it to behave like a robot that does the same thing every time. You want it to be creative and explore different possibilities and be unpredictable.

Scaffolds and even the post-training of models should be different based on whether it is supposed to be driving a collaboration agent or an automation agent.

My hope is that reliability will continue to improve and automation will become easier over time. But for now I think collaboration agents will continue to be much more successful. Many companies that hastily rushed to automate business processes using agents are recognizing the limits and costs, and even legal liabilities — like when an agent deletes production data.

For the time being, you can have only two out of these three properties in agents: general-purpose (a language model based agent that can be instructed to do different tasks rather than purpose-built for a task like traditional software); deployed in high-stakes scenarios, and automated .

This is one of the reasons that I think that for now, AI remains much more of a collaboration technology than a technology that automates workers away.

Let’s take software engineering as a case study. It’s a good leading indicator because coding agents have been particularly rapidly adopted.

You might think: okay, we can’t completely automate away software engineering. But if agents make software engineers ten times more productive, then we need ten times fewer software engineers. Isn’t that an obvious consequence?

Well, that is completely contradicted by the data. We looked at this in a follow-up essay. In every case we looked at, the company was under financial pressure, and it turns out to be more convenient to blame AI for the layoffs instead.

Why is AI not replacing software engineers so far? We’ve known for a while — this is a paper from 2019 — that writing code is not really the bottleneck.

Over the last year, as software engineers started adopting coding agents and started to recognize that it doesn’t seem to be cutting down on the amount of their work, there have been many blog posts rediscovering the fact that writing code is not a bottleneck. Here is a small sample.

So what actually is the bottleneck?

This framework is our answer to the question.

The decide layer: understanding customer requirements, developing the specification, planning, etc. That is not getting compressed by AI.

The execute layer: the actual coding and debugging. This is getting compressed, but it was only maybe one-third of the work to begin with.

The deliver layer: Understanding your code deeply enough to be accountable for what you release; carrying out integration into customer systems, maintenance, testing, etc. This layer is not getting compressed either.

In fact, the first and third layers are arguably expanding as AI compresses the middle layer — and I’ll come back to that point.

I think it is already the case in software engineering, and will increasingly be the case in many professions, that we can think of knowledge workers as similar to a crane operator or a forklift operator. The machine greatly amplifies the human potential to do physical work — it’s doing all the heavy lifting, but the person still remains in control. And I think this is what is happening with cognitive work. Machines are going to increasingly do the cognitive heavy lifting, but the person still remains in control.

The entire job gets reconceptualized as being about operating the machine, understanding the machine, and controlling the machine, as opposed to doing the cognitive work ourselves.

All this might seem like a dramatic change, but in a sense it is only a continuation of what has been happening over and over and over in software engineering. Starting from the days of machine code, we’ve had many waves of technology, each of which gives us nearly an order of magnitude increase in productivity. And far from decreasing the demand for software engineers, during the time that we’ve climbed up this ladder, the amount of software engineering employment has increased by a factor of something like 10,000. And that’s simply because the amount of code that there is to write has gone up by orders and orders of magnitude.

Economists have found this repeatedly. You might have heard the term Jevons’ paradox; I like the term “lump-of-labor fallacy”.

ATMs made it economically feasible for banks to open lots of regional branches. And those branches still needed human tellers to handle the things ATMs couldn’t. So, paradoxically, employment actually grew.

Geoff Hinton made the famous prediction a decade ago that radiologists would be basically extinct in five years. But it turns out radiology employment has in fact grown. And it’s not because radiologists are rejecting AI. They are actually enthusiastically adopting it. One reason for job growth is that when a task gets faster and cheaper to perform, there’s more demand for it.

We’ve written a paper looking at how lawyers should adapt. There’s a lot in there, but one simple point is that AI has made it a lot easier to file lawsuits. And this means more work for lawyers. We might be unhappy about this if we don’t like living in a litigious society, but from the point of view of employment for lawyers, this is great news.