ai 工作法｜ai 抬高了知识工作的地板，不是天花板

ai 不能创造（至少目前如此）。也就是说，ai 没有突破人类工作的上限/天花板。

但是，ai 抬高了知识工作的下限/地板。在抬高的下限之下，ai提供的认知资源免费、量大，20美金包月。最终，下限之下，让人类做这件事就不符合经济原则。

例如，“翻译界哈佛”蒙特雷今年停止招生了，等2年后已有学生毕业，学院就直接关门了。ai的冲击并没有取代毁灭所有的同传需求，但是，除了最顶级的小部分人，大多数人首当其冲。

ai 对翻译领域带来的影响，只是一个典型案例。同样的事情，也在coding、写作、医疗咨询等诸多领域，悄悄上演。

ai 工作法

借机梳理一下我 关于“ai与工作”的思考：

工作大致分为3类：地板工作（基础性工作）、天花板工作（创造性工资）、以及不上不下工作（中间工作，过渡性工作）。

对于地板工作：关键是用AI提升自己，不要用AI偷懒，进一步抑制自己的成长。这样只会加速被取代；

对于天花板工作：关键是与AI合作。

你的工作不可能全是创造性的，全是天花板的；工作构成中，可能30%是基础的，50%是中间的，只有20%是天花板的；

用AI高效搞定80%的非天花板工作，你就可以把20%的天花板工作做的更快更好。

对于不上不下工作：一方面学习AI，一方面提升自己。过渡性工作，既不是完全机械，也未达到创造成层面。所以，一方面学习使用ai，更快更好完成工作，另一方面借机提升自己，逐渐让自己靠近创造性工作贡献。

这也是最容易被忽视的部分。对成年人讲，真正的职业成长，就发生在这个区间。

整体原则：广泛实验，重度应用，最终培养出针对具体问题来具体应用ai工具的“ai元技能”。

ai不是抽象的万能钥匙，ai是一个工具，而且是学习和应用门槛不低的工具。如果你想用玩抖音玩小红书的方式来玩ai，不如不学。

上面是我的个人思考。思考的前提是得有材料，材料提出好的问题，你的思考就会自然发生。

下面是我对着思考的阅读材料，我做成双语对照版本，骗你一次读两遍。哈哈～

AI 提高的是下限，而不是上限

title: AI is a Floor Raiser, not a Ceiling Raiser

link: https://elroy.bot/blog/2025/07/29/ai-is-a-floor-raiser-not-a-ceiling-raiser.html#the-future-is-already-here-its-just-not-very-evenly-distributed

重塑学习曲线

A reshaped learning curve

Before AI, learners faced a matching problem: learning resources have to be created with a target audience in mind. This means as a consumer, learning resources were suboptimal fits for you:

在 AI 出现之前，学习者面临一个匹配难题：学习资源的制作总是针对某一特定受众。这意味着作为学习者，你能找到的学习资源往往并非完全适合你：

• You're a newbie at $topic_of_interest, but have knowledge in related topic $related_topic. But finding learning resources that teach $topic_of_interest in terms of $related_topic is difficult.
• 你是 $学习主题 的新手，但在 $相关主题 上已有一些知识。然而，要找到以 $相关主题 为视角来教授 $学习主题 的学习资源却很困难。
• To effectively learn $topic_of_interest, you really need to learn prerequisite skill $prereq_skill. But as a beginner you don't know you should really learn $prereq_skill before learning $topic_of_interest.
• 要有效学习 $学习主题 ，你其实需要先掌握 $前置技能 。但作为初学者，你并不知道在学习 $学习主题 之前确实应该先学 $前置技能 。
• You have basic knowledge of $topic_of_interest, but have plateaued, and have difficulty finding the right resources for $intermediate_sticking_point
• 你对 $学习主题 已有基础认知，但已进入瓶颈期，很难找到针对 $学习瓶颈 的合适资源。

Roughly, acquiring mastery in a skill over time looks like this:

大致而言，随时间推移掌握一项技能的学习曲线是这样的：

What makes learning with AI groundbreaking is that it can meet you at your skill level. Now an AI can directly address questions at your level of understanding, and even do rote work for you. This changes the learning curve:

AI 颠覆学习体验之处在于它能针对你的技能水平提供帮助。现在，AI 可以直接解答与你理解程度相符的问题，甚至替你完成一些机械重复的任务。这改变了学习曲线：

精通：仍然很难！

Mastery: still hard!

Experts in a field tend to be more skeptical of AI. From Hacker News:

在某一领域的专家往往对 AI 更加持怀疑态度。正如 Hacker News 上的评论所言：

    [AI is] shallow. The deeper I go, the less it seems to be useful. This happens quick for me. Also, god forbid you're researching a complex and possibly controversial subject and you want it to find reputable sources or particularly academic ones.

    AI 很肤浅。我稍微深入一点，就很快觉得它派不上什么用场了。另外，要是你正在研究某个复杂、甚至有争议的课题，并指望 AI 替你找到可靠的来源——尤其是学术资料——那你就别想了。

This intuitively makes sense, when considering the data that AI is trained on. If an AI's training corpus has copious training data on a topic that all more or less says the same thing, it will be good at synthesizing it into output. If the topic is too advanced, there will be much less training data for the model. If the topic is controversial, the training data will contain examples saying opposite things. Thus, mastery remains difficult.

直觉上，这种观点有道理，，因为 AI 完全依赖训练数据。假如 AI 的训练语料库中针对某个主题有海量且内容大同小异的数据，那么它就很擅长将其综合为输出。如果主题过于高深，模型可用的训练数据就会少得多。如果主题存在争议，训练数据中就会出现观点相左的示例。因此，要达到精通仍然困难重重。

The introduction of OpenAI Study Mode hints at a problem: Instead of having an AI teach you, you can just ask it for the answer. This means cheaters will plateau at whatever level the AI can provide:

OpenAI “学习模式” 的推出，暗示了人们在使用AI时的一个问题：用户不是让 AI 教，而是直接让AI给答案。这意味着，作弊者的水平将止步于 AI 能提供的程度：

Cheaters, in the long run, won't prosper here!

长远来看，靠作弊是行不通的！

全新学习曲线带来的影响

The impact of the changed learning curve

Technological change is an ecosystem change: There are winners and losers, unevenly distributed. For AI, the level of impact is determined by the amount of mastery needed to make an impactful product:

技术变革，是生态系统级别的变化：其中必然有赢家和输家，而且分布并不均衡。就 AI 而言，其影响程度取决于打造一款有影响力的产品所需的精通程度：

编程

Coding: A boon to management, less so for large code bases

When trying to code something, engineering managers often run into a problem: They know the principles of good software, they know what bad software looks like, but they don't know how to use $framework_foo. This has historically made it difficult for, as an example, a backend EM to build an iPhone app in their spare time.

工程经理在自己编写程序时经常遇到一个问题：他们懂得优秀软件的原则，也知道糟糕软件是什么样子，但并不会使用 $framework_foo 。结果就是，长期以来，比如一位后端工程经理想在业余时间开发一款 iPhone 应用都是很困难的。

With AI, they are able to quickly learn the basics, and get simple apps running. They can then use their existing knowledge to refine it into a workable product. AI is the difference between their product existing or not existing!

有了 AI，他们就能快速学会基础知识，让简单的应用运行起来。然后，他们可以利用自身已有的知识将其优化为一个可用的产品。AI 直接决定了他们的产品能否面世！

For devs working on large, complex code bases, the enthusiasm is more muted. AI doesn't have context on the highly specific requirements and existing implementations to contend with, and is less helpful:

对于维护大型复杂代码库的开发者来说，他们对 AI 的热情要淡得多。AI 无法了解其中高度特定的需求和现有的实现细节，因此所能提供的帮助相当有限：

创意作品

Creative works: not coming to a theater near you

There is considerable angst about AI amongst creatives: will we all soon be reading AI generated novels, and watching AI generated movies?

创意行业的人对 AI 存在相当多的焦虑：难道我们很快就要读 AI 写的小说、看 AI 拍的电影了吗？

This is unlikely because creative fields are extremely competitive, and beating competition for attention requires novelty. While AI has made it easier to generate images, audio, and text, it has not increased production of ears and eyeballs, so the bar to make a competitive product is too high:

这种情况不太可能发生，因为创意领域的竞争极为激烈，要在争夺注意力的竞争中胜出必须依靠新意。尽管 AI 让生成图像、音频和文本变得更容易，但它并没有让人们长出更多的眼睛和耳朵，因此要打造一款有竞争力的作品门槛依然过高：

Novelty is a hard requirement for successful creative work, because humans are extremely good at detecting when something they are viewing or reading is derivative of something they've seen before. This is why, while Studio Ghibli style avatars briefly took over the internet, they have not dented the cultural position of Howl's Moving Castle.

新颖性是创意作品取得成功的硬性要求，因为人类非常善于察觉自己正在观看或阅读的东西是不是在模仿以前见过的内容。这也就是为什么吉卜力动画风格的头像曾经短暂风靡网络，但依然无法撼动《哈尔的移动城堡》的文化地位。

日常需求

Things you already do with apps on your phone: minimal impact

One area that has not seen much impact is in tasks that already have specialized apps. I'll focus on two examples with abundant MCP implementations: email and food ordering. AI Doordash agents and AI movie producers face the same challenge: the bar for a new product to make an impact is already very high:

有一个领域受到的影响微乎其微，那就是那些已经有专门应用来完成的任务。我将着重讨论两个已有大量成熟解决方案的例子：电子邮件和点餐。让 AI 充当 DoorDash 外卖智能体和让 AI 当电影制片人都面临同样的挑战：一个新产品要产生影响，其门槛本来就已经非常高：

Email would seem like a ripe area for disruption by AI. But modern email apps already have a wide variety of filtering and organizing tools that tech savvy users can use to create complex, personalized systems for efficiently consuming and organizing their inbox.

电子邮件看上去似乎是一个容易被 AI 颠覆的领域。然而，现代的电子邮件应用已经提供了各式各样的过滤和整理工具，精通技术的用户可以利用它们打造出复杂且个性化的体系，高效地阅读并整理自己的收件箱。

Summarizing is a core AI skill, but it doesn't help much here:

概括总结是 AI 的一项核心技能，但在这里并没有多大帮助：

• Spam is already quietly shuffled into the Spam folder. A summary of junk is, well, junk.
• 垃圾邮件已经被系统悄悄移进了垃圾箱。对垃圾的摘要归根结底还是垃圾。
• For important email, I don't want a summary: An AI is likely to produce less specifically crafted information than the sender, and I don't want to risk missing important details.
• 对于重要邮件，我并不想要摘要：AI 提供的内容很可能不如发件人写得那样具体，而我也不想冒遗漏重要细节的风险。

Similar with food ordering: apps like DoorDash have meticulously designed interfaces. They strike a careful balance between information like price and ingredients against photos of the food. AI is unlikely to produce interfaces that are faster or more thoughtfully composed.

点餐方面也是如此：像 DoorDash 这样的应用拥有精心设计的界面，在价格、配料等信息与食物照片之间取得了巧妙的平衡。AI 不太可能设计出比这更快捷或更周到的界面。

未来已来——只是分布不均

The future is already here – it’s just not very evenly distributed

AI has raised the floor for knowledge work, but that change doesn't matter to everyone. This goes a long way towards explaining the very wide range of reactions to AI. For engineering managers like myself, AI has made an enormous impact on my relationship with technology. Others fear and resent being replaced. Still others hear smart people express enthusiasm for AI, struggle to find utility, and think I must just not get it.

AI 提高了知识工作的下限，但这种改变并非每个人都在意。这在很大程度上解释了人们对 AI 的反应为何会千差万别。对于我这样的工程经理而言，AI 极大地改变了我与技术的关系。有些人则害怕甚至憎恨自己会被取代。还有一些人听到聪明人都在热情推崇 AI，但自己怎么也找不到用处，于是觉得肯定是自己没弄懂。

AI hasn't replaced how we do everything, but it's a highly capable technology. While it's worth experimenting with, whoever you are, if it doesn't seem like it makes sense for you, it probably doesn't.

AI 并没有改变我们处理一切事务的方式，但它是一项能力极强的技术。无论你是谁，尝试一下 AI 总是值得的。然而，如果你觉得它对你来说没什么用，那很可能它确实对你没什么用。

one more thing

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