AI Turned Me Into a Content Agency of One

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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

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.

Instead, I produced it all myself. In about two weeks. With help from AI.

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. 

I can’t help but think: Am I okay with this?

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. 

From tool to transformation

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? 

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. 

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.

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. 

I’ve come to think about AI’s role in my work in six parts, which correspond to the six parts of my workflow:

1. As a “second brain”
2. As a thought partner 
3. As a first draft factory 
4. As a first set of “eyes” 
5. As a content multiplier 
6. As a product manager 

(For those wondering, my exact stack is:

• ChatGPT for planning and outlining 
• Claude for drafting 
• Lex for editing and refining
• Spiral for content repurposing) 

Let’s look at how it all comes together. 

My workflow, but make it AI 

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.

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.

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.

Step 1: AI as a 'second brain': Ideation and strategy

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.

Step 2: AI as a thought partner: Outlining and structuring

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.

Step 3: AI as a first draft factory: Drafting

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.

Step 4: AI as a first set of 'eyes': Editing and refinement 

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.

Step 5: AI as content multiplier: Repurposing and distribution

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.

Step 6: AI as a product manager: Packaging and scaling

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.

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. 

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. 

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?

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.

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. 

Is the 15-hour workweek in the room with us? 

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.

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.

So if AI is making me this productive, why am I still working so much?

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.

Because, for most of us, that’s not how work works.

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.”

Instead of writing two articles a week, I can now write eight.

Instead of a handful of LinkedIn posts per month, I can create 24.

Instead of using AI to lighten my workload, I use it to take on more.

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. 

A different kind of AI arms race

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.

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.

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. 

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.

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.

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