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Over the past couple of years, AI tools like ChatGPT have gotten really good, really fast.
And because of that, there’s been a pretty consistent narrative:
AI is coming for writing jobs.
If you spend any time on LinkedIn, you’ll see it everywhere. Writers, journalists, and editors are watching these tools improve quickly while companies look for ways to cut costs.
And honestly, that concern makes sense.
But I think that framing is a little off.
AI isn’t really replacing writing jobs across the board.
What it’s doing is something a little different.
It’s exposing which roles were already fragile to begin with.
Why AI Is Not Actually Replacing Writing Jobs
Most of the conversation around AI is focused on output.
- How fast can content be produced?
- How cheaply can it be generated?
- How many articles can be replaced?
But if you zoom out a bit, this direction isn’t new.
Newsroom jobs have been declining for years, well before generative AI showed up.
According to Pew Research Center, newsroom employment has been on a long-term decline since the late 2000s.
You can see the same thing at companies like McClatchy Media (my employer). Legacy properties have been consolidated, and many have moved toward digital-first models.
At the same time, content strategies shifted toward SEO and volume.
AI didn’t start that trend, it just made it easier to execute.
The Real Shift in Writing Jobs: From Output to Leverage
So when you look at all of this, the divide isn’t really between humans and AI.
It’s between two different types of roles.
On one side, you have roles focused on production. Writing more, faster, and cheaper.
On the other side, you have roles focused on outcomes. What gets made, why it matters, and how it performs.
And those two paths are starting to separate pretty quickly.
A good example of this is BuzzFeed.
Like many companies, they’re leaning into AI to scale certain types of content, especially formats like quizzes and personality-driven posts.
Which makes sense. Those formats are predictable and easy to systematize.
But scaling production doesn’t necessarily mean you’re improving results.
And more recently, there have been questions about how well that approach actually translates into business performance.
Where the Real Value Is in AI and Content Jobs Today
So where does that leave things?
It shifts the advantage toward people who understand how the system works.
Not just writing, but how content connects to demand, distribution, and revenue.
That shift is already visible across the industry.
Companies like The New York Times Company and McClatchy are leaning heavily into digital subscriptions and audience growth as core business drivers.
That’s where the leverage is now.
3 Ways to Stay Relevant in an AI-Driven Writing Career
1. Audit Your Role for Leverage Gaps
Start by looking at how your time is actually spent.
List your weekly tasks and separate them into two groups. Execution work like writing and editing, and decision-making work like strategy, topics, and distribution.
If most of your time is spent on execution, that’s the highest-risk area.
Example:
In my role, I noticed a steady flow of PR emails asking for coverage. Most of them were ignored.
Instead of deleting them, I reframed them as demand signals. Companies were actively looking for exposure.
By responding with a simple template and routing interested companies to the partnerships team, I turned them into qualified leads.
Action:
Shift some of your time toward identifying opportunities, not just completing tasks.
2. Tie Your Work to a Business Outcome
Writing on its own is no longer enough.
You need to understand what your work actually produces.
Take one recent piece and ask a simple question. What did this do?
Did it drive traffic? Generate revenue? Support a larger goal?
If you don’t know, that’s the gap.
Example:
At McClatchy and similar publishers, there is increasing pressure to connect content to measurable outcomes like subscriptions, engagement, and retention.
Action:
Track one metric tied to your work and use it to guide decisions. Did your article produce any affiliate sales? How many views/sessions did it produce? Where did the traffic come from?
If you don’t have this data, take the initiative and ask your editor/manager. A good leader will be thrilled when you show interest and want to improve performance.
3. Build Systems Around What Actually Works
The last piece of this is probably the most important.
There’s a lot of advice about building systems. Templates, workflows, pipelines.
That’s useful, but it’s also exactly what AI is getting better at.
The real advantage comes from figuring out what actually works in practice.
Example:
In my role, I worked with affiliate content across existing articles.
Some partners offered high payouts, $200 or more per conversion, but they weren’t generating sales.
Meanwhile, another platform, Ro, paid less but actually converted.
Likely because of advertising and Serena Williams representing the brand.
So instead of chasing higher payouts, I focused on what worked. That’s what produced consistent revenue.
AI could suggest options. It wouldn’t make that decision.
Action:
Test, measure, and double down on what actually produces results.
Final Thoughts: AI and the Future of Writing Jobs
So overall, AI isn’t really the story here. The shift was already happening. AI just sped it up.
And if you look at how companies operate, the pattern is consistent.
Pure writing roles, especially ones tied to output, are usually the first to get cut when budgets tighten.
Not because writing isn’t valuable. Because it’s easier to replace.
The risk isn’t being a writer. It’s being in a role where your only function is producing content.
The people who stick around are tied to outcomes. They connect content to traffic, revenue, and growth.
Once your work is tied to results, you’re no longer just a cost. You’re part of the system that drives the business.
That’s a much harder position to replace.
So the goal isn’t just to get better at writing. It’s to move closer to the outcome.
That’s what separates people who stay stuck from the ones who break out.
And that’s what we’re getting into in the next episode.
Thanks for reading,
Nick Hazleton
Founder of SIOS