Oct 13, 2025
Translate by Gemini
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Here’s how I do it—and how it’s changing the rules of the game
在此揭秘我的工作心法，以及它如何改变游戏规则
As AI races ahead, we try to step back from the fray every once in a while. Each quarter, we gather for a "think week” to reflect on our work from the previous quarter and come up with new ideas that we can build to keep delivering an incredible experience for our readers. In the meantime, we’re re-republishing five pieces by Katie Parrot with insights on how AI is changing our professional lives. Yesterday we re-upped her piece on how using vibe coding tools inspired her to want to learn to write her own software. Today we're running her in-depth look at how she uses AI to amplify her skills as a content creator.—Kate Lee
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随着 AI 的狂飙突进，我们时常需要从喧嚣中抽身，退一步审视。每个季度，我们都会举办一次“思考周”，反思上一季度的工作，并构思新的创意，以持续为读者提供卓越的体验。在此期间，我们将重新发布 Katie Parrot 的五篇文章，分享她关于 AI 如何改变我们职业生涯的深刻见解。昨天，我们重刊了她的一篇文章，讲述了使用直觉式编程工具如何激励她学习编写自己的软件。今天，我们将分享她的一篇深度解析，看她如何利用 AI 来极大地提升自己作为内容创作者的能力。——Kate Lee
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As a content strategist and writer, I don’t often stop to count just how much I produce—until I do, and the numbers make me question my grip on reality.
作为一名内容策略师和写手，我通常不会停下来计算自己的产出量——直到我真的去盘点时，那些数字让我开始怀疑自己是否活在现实中。
A few weeks ago, I was sitting in front of my computer, scoping out everything I would be developing for one of my freelance clients over the next month, when It suddenly occurred to me that I was on the hook for an unhinged amount of content:
8 blog articles
3 ebooks
24 LinkedIn posts
8 LinkedIn carousels
24 X posts
16 Instagram posts
8 Instagram carousels
16 Facebook group posts
24 emails
几周前，我坐在电脑前，为我的一位自由职业客户规划下个月要创作的所有内容。我突然意识到，我需要负责完成的内容数量简直多到离谱：
8 篇博客文章
3 本电子书
24 篇 LinkedIn 帖子
8 组 LinkedIn 轮播图
24 条 X 平台推文
16 篇 Instagram 帖子
8 组 Instagram 轮播图
16 篇 Facebook 小组帖子
24 封电子邮件
In the past, when I was on staff at a marketing agency, I was considered fully booked when I was producing two articles per week. The work I just listed would be enough to give three or four writers at a small content marketing shop some healthy business for two to three months.
过去，当我在一家营销代理公司任职时，每周产出两篇文章就意味着我的工作量已经饱和。而我刚才列出的工作量，足以让一家小型内容营销工作室里的三到四名写手忙上两三个月，并且业务量还相当可观。
Instead, I produced it all myself. In about two weeks. With help from AI.
然而，这些内容都是我一个人完成的。大约只用了两周时间。在 AI 的帮助下。
That’s right: I am using the tools that so many people—particularly creative professionals—worry are going to take our jobs to literally take somebody’s job.
没错：我正在使用那些让许多人——尤其是创意专业人士——担心会抢走我们饭碗的工具，去实实在在地抢别人的饭碗。
I can’t help but think: Am I okay with this?
我忍不住自问：我对此心安理得吗？
It’s one thing to hear about AI killing jobs in theory. It’s another thing to see it happening—and see your own fingerprints on the murder weapon. But I’m not just wrestling with guilt. I’m also grappling with what it means to work this way—with the power AI has given me to do more, faster, and at a scale that would have been unthinkable just a few years ago. Because the task at hand isn’t (just) keeping up—it’s deciding what kind of race I want to run. And that’s where things get interesting.
理论上听说 AI 会扼杀就业是一回事，亲眼目睹这一切发生——并且发现凶器上留下了自己的指纹——则是另一回事。但我纠结的不仅仅是负罪感，我还在努力理解以这种方式工作到底意味着什么——AI 赋予了我更强的能力，让我能以几年前无法想象的规模，更快地完成更多工作。因为眼下的任务不（仅仅）是跟上节奏，而是决定我自己想参加一场什么样的比赛。而这，正是事情变得有趣的地方。
From tool to transformation
从工具到变革
I didn’t start using AI to cut someone else out of the equation. Like so many of us, I started experimenting with these tools out of curiosity. Could they really make my job easier? Could they help me work faster or better?
我最初使用 AI 并非为了取代任何人。和我们中的许多人一样，我开始尝试这些工具是出于好奇。它们真的能让我的工作更轻松吗？能帮我工作得更快或更好吗？
I started small, asking ChatGPT to suggest titles for blog posts, summarize research, or generate rough outlines. And at first that’s all it was: a tool. Just another productivity hack in an industry that thrives on them.
我从一些小事做起，比如让 ChatGPT 为博客文章建议标题、总结研究资料或生成粗略大纲。起初，它仅仅是一个工具，不过是这个推崇效率的行业里又一个生产力小技巧罢了。
Two-odd years into my AI journey, I have to admit: AI hasn’t just helped me produce content faster—it has fundamentally changed the scale of what I can do. The limits I used to bump up against—time, energy, capacity—are way lower. The small, tedious steps—reformatting drafts, pulling in relevant links, or tweaking phrasing for clarity—are more manageable, the mental load lighter, the cognitive cost of switching between tasks reduced. I can deliver more content in less time, with less effort.
在我使用 AI 两年多的今天，我必须承认：AI 不仅帮助我更快地创作内容，它还从根本上改变了我所能做事情的规模。我过去常常遇到的瓶颈——时间、精力和能力——的门槛被大大降低了。那些琐碎乏味的步骤，如重新格式化草稿、插入相关链接或为了清晰而调整措辞，都变得更易于管理，我的心智负担更轻，任务切换的认知成本也降低了。我可以用更少的时间和精力，交付更多的内容。
But speed isn’t the only boon of my AI-powered workflows. I can also deliver higher quality work because I'm not mentally exhausted from the grunt work. I can focus on strategy, on understanding my clients' needs, on crafting unique angles and perspectives—all corners that, in a past life, I might have cut because I was racing against deadlines and drowning in deliverables. It’s become trite to say that AI frees you to focus on the human elements that truly matter…but AI has freed me to focus on the human elements that truly matter.
但速度并非我 AI 赋能工作流的唯一好处。我还能交付质量更高的工作，因为我不再因那些繁重的杂活而心力交瘁。我可以专注于策略、理解客户需求、打造独特的切入点和视角——在过去，由于要赶截止日期和应付海量的交付物，这些方面我可能都会偷工减料。有句话说，AI 让你能专注于真正重要的人文元素，这已是老生常谈……但 AI 确实解放了我，让我能专注于那些真正重要的人文元素。
I’ve come to think about AI’s role in my work in six parts, which correspond to the six parts of my workflow:
As a “second brain”
As a thought partner
As a first draft factory
As a first set of “eyes”
As a content multiplier
As a product manager
我逐渐将 AI 在我工作中的角色归纳为六个部分，这与我工作流程的六个环节相对应：
作为“第二大脑”
作为思考伙伴
作为初稿工厂
作为第一道“审查关”
作为内容倍增器
作为产品经理
(For those wondering, my exact stack is:
ChatGPT for planning and outlining
Claude for drafting
Lex for editing and refining
Spiral for content repurposing)
（如果有人好奇，我使用的具体工具组合是：
用 ChatGPT 进行规划和构建大纲
用 Claude 起草初稿
用 Lex 进行编辑和润色
用 Spiral 进行内容再利用）
Let’s look at how it all comes together.
下面我们来看看这一切是如何协同工作的。
My workflow, but make it AI
我的工作流，AI 加持版
If there’s one thing that I’ve learned, it’s that if you try to use AI out of the box, you’re going to have a bad time. These tools are powerful, but they don’t come preloaded with the context that makes content good. If you want AI to produce work that aligns with your goals—whether it’s high-quality thought leadership, brand-aligned marketing, or something else—you have to feed it the right inputs first.
如果说我学到了什么，那就是：如果你试图直接开箱即用 AI，你的体验会很糟糕。这些工具很强大，但它们并没有预装那些能让内容变得优秀的上下文信息。如果你希望 AI 产出的作品能符合你的目标——无论是高质量的思想领导力内容、与品牌一致的营销文案，还是其他什么——你必须首先为它提供正确的输入。
That means taking the time upfront to train AI on the specific elements that matter. For me, that’s resources like:
A style guide. Not just grammar rules and brand colors, but voice, tone, and key personas. AI needs to know who it’s writing for and how to sound.
Example content. A collection of past content that captures the structure, style, and level of detail I want. AI does best when it has real-world references.
Messaging. Since this is marketing, every piece needs to align with the brand’s core perspective. AI can’t just generate copy; it has to reinforce strategy.
这意味着要预先花时间，就那些关键要素对 AI 进行训练。对我而言，这些资源包括：
风格指南。不仅仅是语法规则和品牌颜色，还包括语态、语调和关键的用户画像。AI 需要知道它在为谁写作，以及应该听起来像谁。
内容范例。一系列能够体现我想要的结构、风格和细节水平的过往内容。当 AI 有了真实世界的参考时，它的表现是最好的。
核心信息。因为这是市场营销，每一篇内容都必须与品牌的核心观点保持一致。AI 不能只是生成文案，它必须能够强化品牌策略。
Let’s say I’m developing a thought leadership campaign for a client. Without AI, this would take weeks of research, multiple drafts, and endless tweaking. With AI, the process is both faster and more structured—but only because I’ve taken the time to set it up properly.
假设我正在为客户策划一个思想领导力活动。如果没有 AI，这可能需要数周的研究、多轮草稿和无休止的修改。有了 AI，整个过程不仅更快，而且更有条理——但这完全是因为我预先花时间进行了正确的设置。
Step 1: AI as a 'second brain': Ideation and strategy
第一步：AI 作为“第二大脑”：创意构思与策略制定
I start by feeding ChatGPT the brand’s positioning, audience personas, and past content. Then, I ask targeted prompts like:
What’s missing from this content strategy based on our goals?
Which audience personas are underserved in our current content?
What’s an underutilized angle we could explore?
AI surfaces gaps I might not have noticed—helping me think like a strategist, not just a writer.
我首先向 ChatGPT 输入品牌的定位、受众画像和过往内容。然后，我会提出一些有针对性的问题，比如：
基于我们的目标，当前的内容策略缺少了什么？
在我们现有的内容中，哪些受众画像没有得到充分满足？
有哪些我们尚未充分利用的切入点可以探索？
AI 会揭示出我可能没有注意到的差距——帮助我像一个策略师，而不仅仅是一个写手那样去思考。
Step 2: AI as a thought partner: Outlining and structuring
第二步：AI 作为思考伙伴：构建大纲与组织结构
Based on the strategic insights, I use Claude to generate an outline. AI ensures the structure is logical, aligned with brand messaging, and covers all key points.
基于这些策略性洞察，我使用 Claude 来生成大纲。AI 确保了文章结构逻辑清晰，与品牌信息保持一致，并涵盖所有关键要点。
Step 3: AI as a first draft factory: Drafting
第三步：AI 作为初稿工厂：起草文案
I dictate rough ideas into the AI-powered word processor Lex, which expands them into structured paragraphs. This eliminates blank-page syndrome and speeds up the process.
我将粗略的想法口述给 AI 驱动的文字处理器 Lex，它会把这些想法扩展成结构化的段落。这消除了“空白页综合征”，并加快了写作进程。
Step 4: AI as a first set of 'eyes': Editing and refinement
第四步：AI 作为第一道“审查关”：编辑与优化
AI helps me audit my work before I send it off. For example, I’ll prompt:
Is there anything missing from this content inventory based on the funnel strategy?
Are we reinforcing the right brand messages?
Does this align with our example content?
AI flags weak spots, misalignments, and opportunities I might have overlooked.
在交付工作成果之前，AI 会帮助我进行审查。例如，我会这样提问：
基于营销漏斗策略，这份内容清单中是否还缺少什么？
我们是否在强化正确的品牌信息？
这与我们提供的内容范例是否一致？
AI 会标记出我可能忽略的薄弱环节、不一致之处以及潜在机会。
Step 5: AI as content multiplier: Repurposing and distribution
第五步：AI 作为内容倍增器：再利用与分发
Once the piece is done, I use Spiral to turn it into a LinkedIn post, an email, and a Twitter thread—all in the brand’s established voice.
一旦文章完成，我使用 Spiral 将其转化为 LinkedIn 帖子、电子邮件和推特长文——所有这些都保持品牌既定的语调。
Step 6: AI as a product manager: Packaging and scaling
第六步：AI 作为产品经理：打包与规模化
Once the project is complete, I have ChatGPT bundle everything into a reusable framework including deliverables, workflows, and even pricing and packaging options. This way, I can apply the same process to future clients without starting from scratch, turning one-off work into a scalable system.
项目完成后，我让 ChatGPT 将所有内容打包成一个可复用的框架，包括交付物、工作流程，甚至还有定价和打包方案。这样，我在服务未来客户时就可以应用相同的流程，而无需从零开始，从而将一次性的工作转变为一个可规模化的系统。
Running these steps, I can turn around a thought leadership article in one day instead of three. I can batch-produce an entire month of content in a few focused work sessions.
通过执行这些步骤，我可以在一天内完成一篇思想领导力文章，而过去需要三天。我可以在几次集中的工作时段内，批量生产出整整一个月的内容。
AI isn’t just accelerating my workflow—it’s redefining what’s possible as a solo operator. And that’s exciting: It’s pushed me to think bigger about what I can build and the impact I can have on the organizations I work with.
AI 不仅在加速我的工作流程，它还在重新定义我作为一名独立工作者的可能性。这令人兴奋：它促使我以更宏大的视角去思考我能创造什么，以及我能为合作的组织带来怎样的影响。
But it also raises a question I can’t quite shake: What happens when every freelancer, agency, and marketing team starts working like this—when one person can do the work of five? If AI makes me this productive, does it also make other people redundant?
但这也带来一个我无法回避的问题：当每个自由职业者、代理机构和营销团队都开始这样工作时——当一个人能完成五个人的工作时——会发生什么？如果 AI 让我如此高效，它是否也让其他人变得多余？
There’s another way to look at this. If AI lets one person do the work of five, that doesn’t just mean more pressure—it also means more opportunity. By lowering the cost of content production, AI opens the door for new projects, new businesses, and new kinds of creative work. A solo entrepreneur can now build a full-fledged media brand. A marketing team can create at a level that was once only possible for Fortune 500 companies. And historically, technological advancements have often generated more work than they’ve displaced.
对此还有另一种看法。如果 AI 能让一个人完成五个人的工作，这不仅意味着更大的压力，也意味着更多的机会。通过降低内容生产成本，AI 为新项目、新业务和新型创意工作打开了大门。一个独立的创业者现在可以建立一个功能完备的媒体品牌。一个营销团队可以达到过去只有世界500强公司才能企及的创作水平。而且从历史上看，技术进步所创造的工作岗位往往比其取代的要多。
Still, history doesn’t guarantee the future, and the way we integrate AI into work isn’t just about what’s possible—it’s about what we choose.
然而，历史并不能保证未来，而我们如何将 AI 融入工作，也不仅仅关乎可能性，更关乎我们的选择。
Is the 15-hour workweek in the room with us?
15小时工作周的时代终于要来了吗？
In theory, AI could be the tool that finally enables a different way of working. It could allow me to scale back, meet my financial goals in fewer hours, and free up time to focus on the things that really matter—relationships, rest, and the ever-growing pile of unplayed board games sitting on my shelf.
理论上，AI 可能是那个最终能让我们实现不同工作方式的工具。它能让我缩减工作量，用更少的时间实现财务目标，并腾出时间专注于真正重要的事情——人际关系、休息，以及我书架上那堆积如山还未玩的桌游。
In some ways, it has. Tasks that once took days now take hours. A fully scoped content calendar that might have taken a team to execute is now something I can produce myself, solo, at scale. The work that once felt overwhelming is now streamlined, structured, efficient.
在某些方面，它确实做到了。过去需要几天才能完成的任务，现在只需几小时。一个需要团队才能执行的完整内容日历，现在我一个人就能大规模地完成。曾经让人不堪重负的工作，如今变得流程化、结构化、高效化。
So if AI is making me this productive, why am I still working so much?
那么，既然 AI 让我如此高效，为什么我还在工作这么长时间？
The economist John Maynard Keynes predicted in the 1930s that technological progress would lead to a 15-hour workweek. Every major leap in efficiency since then has sparked speculation—is this the moment? Are we finally there? Each time, the answer has been the same: Not yet.
经济学家约翰·梅纳德·凯恩斯在1930年代曾预测，技术进步将带来每周15小时工作制。自那以后，每一次效率的重大飞跃都引发了人们的猜测——是时候了吗？我们终于要实现了吗？每一次，答案都是一样的：还没有。
Because, for most of us, that’s not how work works.
因为，对我们大多数人来说，工作的逻辑并非如此。
Efficiency doesn’t necessarily reduce our workload—it expands it. The more we’re capable of producing, the more we expect of ourselves and others. The more output becomes possible, the further the goalposts are moved on what is considered “enough.”
效率并不一定会减少我们的工作量，反而会扩大它。我们能产出的越多，我们对自己和他人的期望就越高。当产出变得越来越容易时，关于“足够”的标准线也会被不断推高。
Instead of writing two articles a week, I can now write eight.
过去我一周写两篇文章，现在我能写八篇。
Instead of a handful of LinkedIn posts per month, I can create 24.
过去我一个月发几条 LinkedIn 帖子，现在我能创作24条。
Instead of using AI to lighten my workload, I use it to take on more.
我没有用 AI 来减轻我的工作负担，而是用它来承担更多的工作。
As a self-employed person, I have theoretical freedom to step back—to take the efficiency gains and turn them into more time off instead of more output. But when I look at the landscape of work, I know that efficiency alone doesn’t guarantee stability. Markets adjust. The work that once seemed impressive eventually becomes baseline. That’s the real reason I keep pushing. Not because I’m chasing endless growth, but because the ground beneath knowledge work is shifting.
作为一名自雇人士，理论上我有退一步的自由——将效率提升转化为更多的休息时间，而非更多的产出。但当我审视整个工作环境时，我明白单靠效率并不能保证稳定。市场会自我调整。曾经令人印象深刻的工作量最终会成为基准水平。这才是促使我不断前进的真正原因。不是因为我在追逐无尽的增长，而是因为知识工作的根基正在发生动摇。
A different kind of AI arms race
一场不同寻常的 AI 军备竞赛
The tools we’ve built are powerful. They democratize access to high-quality output, making it easier than ever for solo operators, small businesses, and massive corporations alike to create at scale. In many ways, this is a net positive—barriers are lower, opportunities are greater, and the ability to execute big ideas is more accessible than ever.
我们创造的这些工具非常强大。它们普及了高质量内容的产出能力，让独立工作者、小企业和大型企业都能比以往任何时候都更容易地进行规模化创作。在许多方面，这总体上是积极的——门槛降低了，机会增多了，执行宏大创意的能力也变得前所未有地触手可及。
They have also thrust us into a moment of transition. AI gives us incredible capabilities, but it creates a new kind of treadmill. The question isn’t just whether AI will save us time, but whether we’ll actually allow ourselves to take that time back.
但它们也将我们推入了一个转型期。AI 赋予了我们不可思议的能力，但它也创造了一种新的“跑步机效应”。问题不仅仅在于 AI 是否能为我们节省时间，更在于我们是否真的会允许自己把节省下来的时间收回。
I’d like to believe that we can use these tools to design better ways of working, not just faster ones. That instead of letting AI set the pace, we can stop chasing endless productivity gains and define what productivity actually means for us.
我愿意相信，我们可以利用这些工具来设计更好的工作方式，而不仅仅是更快的方式。我们可以不再让 AI 设定节奏，停止追逐无尽的生产力提升，而是去定义生产力对我们自己而言究竟意味着什么。
For now, I’m experimenting: testing the edges of how I use AI, figuring out where speed is useful and where it just creates more work for the sake of it. I know the tools aren’t going anywhere—so the real challenge isn’t whether to use them, but how to do so with intention.
目前，我还在探索：测试我使用 AI 的边界，弄清楚在哪些地方速度是有益的，而在哪些地方它只是为了工作而创造了更多的工作。我知道这些工具不会消失——所以真正的挑战不在于是否使用它们，而在于如何有意识地去使用。
Because the future of knowledge work isn’t just about who can produce the most. It’s about who can set the terms of their own productivity.
因为知识工作的未来，并不只关乎谁能产出最多，而关乎谁能掌握自己生产力的定义权。