你无法让 AI 消失。你该如何与它共存？更确切地说：在 AI 世界里，你该如何谋生？这里有几个标准答案：“学会使用 AI。”或者找一份 AI 无法取代的职业，一种无法被自动化掉的工作。也许是某种体力劳动。伊桑·莫利克认为这行不通。莫利克是沃顿商学院管理学教授，已成为解读 AI 当前能力及其对必须与之共事的人类影响的颇具影响力的向导之一。
You can't make AI go away. How can you live with it? Or more precisely: How can you make a living in an AI world? Here are a couple standard answers: "Learn to use AI." Or figure out an AI-proof career, one that can't be automated away. Maybe something with your hands. Ethan Mollick doesn't think that will work. Mollick is a Wharton management professor who has become one of the more influential guides to what AI can already do — and what it will mean for people who have to work alongside it.
他的新书《共存》认为，模型的进步速度远超大多数人的认知，应对这种快速加速的唯一办法是学会如何与它们共存。这意味着要承认，你今天能做的很多事情在未来将不再那么有用，并专注于你已具备的、AI 无法复制的特质。我在我的 Channels 播客节目中与莫利克探讨了 AI 对你的工作、你孩子的未来，以及培训工人的系统开始崩溃时会发生什么。以下是我们对话的编辑摘录。
His new book, "Co-Existence," argues that the models are improving much faster than most people understand, and the only way to deal with their rapid acceleration is to learn how to live with them. That means acknowledging that a lot of what you can do today will be less useful in the future, and concentrating on the attributes you already have that AI can't replicate. I talked to Mollick about what AI means for your job, your kids' future, and what happens when the systems that train workers start to break down on my Channels podcast. The following is an edited excerpt of our conversation.
彼得·卡夫卡：人们担心自己的工作，或孩子的工作，被建议成为 AI 专家——或者寻找 AI 无法取代的职业，比如水管工或护士。这有用吗？
Peter Kafka: People worried about their jobs, or their kids' jobs, are being told to become AI experts — or to find lines of work that are AI-proof, like plumbing or nursing. Is that useful?
伊桑·莫利克：我认为这不太管用。首先，这很管用——直到“梅花 6000 号”出现。我不是机器人专家，但我认识的机器人专家从“大语言模型永远不会……这波 AI 热潮完全无关……机器人很难”转变为“实际上这里有门道”。我不知道未来会怎样。我认为没人知道，从一个 AI 专家口中听到这句话，要么让人欣慰，要么让人非常不安。看看变化最大的领域——编程。我们已经知道编程正在改变。但我们甚至不清楚编程岗位会增加还是减少。我们知道程序员的工作内容将会改变，并持续改变。
Ethan Mollick: I don't think it's very useful. First of all, that's great until PlumBot 6000 comes along. I'm not a robotics expert, but the robotics experts I know went from, "LLMs will never … this AI boom is completely unrelated … robots are hard" to, "Actually there's a thing here." I don't know what the future holds. I don't think anyone does, which is either comforting or very upsetting to hear from somebody who's an AI expert. Look at the most changed field, which is coding. We already know coding is changing. [But] we aren't even clear whether there are going to be more or less jobs in coding. We know what you do as a coder is going to change and keep changing.
编写无错代码的重要性，已不及构想想要构建什么。许多程序员比以往任何时候都更快乐。也有人更悲伤。这些工作正在演变。书中我谈到了四个能让你至少占据一些优势的因素。深度知识：对某一领域极其精通，因为这种专长能让你在与 AI 的竞争中胜出。广度知识：了解许多不同事物。我在企业中看到的一种现象是，他们说：“我们的程序员在做设计工作，设计师在做程序员工作，市场人员在做商业计划。”
Writing error-free code is now less important than conceiving of what you want to have built. A lot of coders are happier than they've ever been before. Some are sadder. These are evolving jobs. In the book, I talk about four things that give you at least some advantage. Deep knowledge: knowing an area really well, because that expertise lets you outcompete the AI. Wide knowledge: knowing about lots of different things. One thing I see in companies is they say, "Our coders are doing design work, our designers are doing coder work, and the marketers are doing business plans."
你对许多琐碎小事了解得越多，AI 能帮你的就越多。然后是品味。AI 产出大量内容。什么能防止它变成垃圾？是你挑选哪些东西的品味。最后是能动性。我认为“学习使用 AI”目前并不意味着太多。我们测试过所有教人使用 AI 的方法。那些技巧现在都不管用了。提示词工程开始更像是管理。你不再需要念魔咒般的咒语让 AI 做事。
The more you know about a lot of little things, the more AI can help you. Then there's taste. AI produces a lot. What prevents it from being slop? Your taste about which things to pick. And finally, agency. I don't think "learning to use AI" means very much right now. We've been testing all the methods we use to teach people how to use AI. None of those tricks work anymore. Prompting has started to look a lot more like managing. You don't have to do magic words anymore to make the AI do something.
取而代之的是，你指定：这是我想要的产品，这是我判断它好坏的标准，这是你该如何测试它，这是中途你该向我展示什么。给我五个不同的原型供我挑选。这对处于职业生涯中段、拥有经验、品味且擅长本职工作的人来说，似乎是个好消息。但如果你刚起步，在 AI 说“我来做调研，我来帮你写”的世界里，如何培养这些东西？
Instead, you specify: Here's the product I want, here's how I know it's good, here's how you should test it, here's what you show me partway. Give me five different mockups that I can pick among. That seems like good news for someone mid-career, who has experience, taste, and has gotten good at their job. But if you're starting out, how do you develop that stuff in a world where AI says, "I'll do the research. I'll write it for you."
这里有两个问题。一个是个人层面，另一个是我们培养人的制度。作为个人，我们热衷于认知卸载。这是我们的天性。技术一直都是为了取代烦人的劳动。没人有了洗碗机还怀念手洗碗。没人想自己搅黄油。我孩子不学连笔字。我不会用计算尺。
There are two problems. One is people as individuals, and the other is the institutions we have for teaching people. As individuals, we love to cognitively offload stuff. That's what we're built to do. Technology has always been about replacing annoying labor. No one pines for washing dishes by hand once they have a dishwasher. No one wants to churn their own butter. My kids don't learn cursive. I don't know how to use a slide rule.
但我们不擅长挑选想要外包的任务。这是所有人都在作弊完成作业的问题所在。然后还有机构。有两套学习如何做事的学习机构。一套是大学和高中教育。它们现在一团糟，但我们会想办法解决。AI其实是个非常好的导师，而且我们知道如何在没有AI的情况下测试人：让他们在教室里用蓝皮书考试，或者进行面对面交流。
But we're bad at picking which things we want to offload. That's a problem with everyone cheating on homework. And then there are the institutions. There are two sets of learning institutions for learning how to do a job. One is college and high school education. They're screwed up right now, but we'll figure that out. AI is actually a really good tutor, and we know how to test people without AI: You put them in a classroom with a blue book or have a conversation.
结果我们会拥有更好的教育，课外有AI辅导，课内进行主动学习。我担心的是另一种方式，即学徒制。我把人送到银行、咨询公司、审计事务所或汽车经销商，他们像所有人一直以来那样学习。他们干苦力活，薪水微薄。他们可能会受到一点虐待。交易在于他们能学到技能和人脉，并学会如何做事，因为我们强迫他们去做。
We'll have better education as a result, with AI tutoring outside of class and active learning inside of class. What I'm worried about is the other method, which is apprenticeship. I send people to a bank, consulting company, audit firm, or car dealership, and they learn the same way everyone always has. They do grunt work, poorly paid. They might get abused a little bit. The trade is that they learn skills and contacts, and they learn how to do stuff because we force them to do it.
中层管理者能找到人干他们的杂活。即使你是个糟糕的导师，人们依然能学会门道。如果他们证明能干好活，就会晋升。你能得到下一代优秀员工。几千年来这套机制运作得非常好。现在全坏了，因为激励机制不再一致。作为初级员工，你最好的选择是凡事用AI，因为它比你强。初级人员用AI的水平差得多，因为他们把所有东西都扔给Claude，然后把Claude的结果交给你。
And middle managers get somebody to do their crap work. Even if you're a bad mentor, people still learn the ropes. If they show they can do a good job, they advance. You get the next generation of good employees. It has worked out wonderfully for a few thousand years. That all broke because the incentives are no longer aligned. As a junior employee, your best bet is to use AI for everything because it's better than you. Junior people are much worse at using AI because they dump everything into Claude and give you Claude's results.
他们怎么能判断这是好报告还是坏报告？他们没建立起品味。管理者意识到：“我为什么要找实习生，AI能以更划算的价格帮我解决问题？”公司领导说：“我为什么要让这列火车减速？我为什么要接受实习生更差的工作？”培训机制原本是个良性循环，现在彻底粉碎了。
How could they tell you whether it's a good report or bad? They haven't built taste. And managers realize: "Why would I go to an intern when the AI will solve the problem for me, at a better rate?" And company leaders say: "Why would I want to slow this train down? Why would I want worse work from interns?" The training mechanism was this virtuous cycle, and it is completely shattered.
要么我们必须让更正规的教育得以实现——大量轻体力、白领工作将不得不更像焊工或管道工那样，带上职业学校的色彩——要么公司必须认真对待导师制，把它当成一项实实在在的业务来做。这需要时间，会降低生产力，但你能获得拥有初级员工的好处。我担心公司会说：“我们直接雇佣资深人员。”那就是危险领域。人才梯队断了，没有接班人。
Either we will have to make more formal education happen — a lot of light, white-collar work will have to look more like welding or plumbing, with a trade-school aspect—or companies are going to have to get serious about mentorship as a real thing they do. It takes time and costs productivity, but you gain the benefit of having junior people. My fear is that companies will say, "We'll just hire senior people." That's the dangerous area. The talent pipeline ends. There are no replacements.