In early 2024, we let go of 15 writers and rebuilt our publishing operation around AI agents. Most of our writers had worked with us for years, some since a hiring bet in 2020 that helped 10x our revenue in six months. It wasn’t an easy decision, but readers had moved to AI chatbots and the editorial expenses no longer made sense.
The results? Our costs dropped by 80% and we can now produce quality content faster than ever before, but our revenue is still lower than before too.
For us, AI is both a threat and a solution.
What we’re experiencing in digital publishing will probably affect most sectors as AI gets better at answering customers’ demands. I think our experience could thus be valuable across all industries.
This article explores what we built, what works, and what we haven’t figured out yet.
Why did we stop employing writers?
We stopped employing human writers because our audience had moved, not because AI had improved.
Revenue for our publishing businesses fell sharply in early 2024 as readers turned to AI chatbots for the searches that used to bring them to us.
Chatbots are now answering the queries that had driven millions of visitors to our sites. Our content is often cited as a source, but LLMs don’t generate any revenue and barely send us any traffic in return.
Cutting expenses was the only way to preserve our profits.
It wasn’t easy.
We had worked with most of our writers for several years and deeply valued their work and contributions. They were freelancers we’d hired during a hiring bet in 2020. At the time, we received over 800 applications in a single week, interviewed 30 candidates, and confirmed 15 long-term hires.
That bet paid off spectacularly.
Our revenue grew 10x in six months and 20x in a year. We were publishing over 30 articles per month at 90% margins.
But by 2024, revenue was falling. We tried everything we could to recover ranking, then realised the issue wasn’t our content. Instead, our audience had moved to different platforms, and the whole revenue model needed rethinking.
Reader habits had changed. We had to move with them.
How did we build the AI infrastructure?
We began replacing our editorial team with an AI pipeline. The infrastructure covers market research, writing, editorial review, SEO, and GEO. Each stage is a specialised AI agent, with quality gates between them.
While we are still iterating on the workflow, one non-engineer operator now manages the entire content production flow that previously required a whole team to execute.
Once we’d made the decision to migrate to an AI system, the real question wasn’t which tool to use, but which workflow would best support our operations.
The editorial review agent took the longest to get right. The very first version produced generic content. AI slop, the kind readers spot quickly and stop reading. Not what we publish.
Over several weeks, we used our entire library of content and the publication’s style guide as training materials for our AI agents. We wrote custom skills and instructions to produce articles tied to specific categories or content types. Every new article became better and better as we fed our internal models with more data and feedback.
We also adjusted the scope along the way.
The original plan included a dedicated fact-checking AI agent, but it ended up adding complexity without any real benefit. Instead, the human operator at the end of the pipeline is the one responsible for accuracy and fact-checking. The AI agents each prepare a note listing their sources so that step is easier for the operator, but only a human is held accountable for the output.
Every decision came back to the same question: If we were designing this today, from zero, with AI as part of the foundation, would we build it this way?
That’s the logic of Zero-Base Operations. Justify every workflow and tool from scratch instead of patching AI onto what existed before. Applied to editorial operations, the answer looked nothing like the team it replaced.
We created an AI-native publishing structure: a model where specialised AI agents, each carefully designed with custom instructions, handle research, drafting, editorial review, SEO, and GEO by default. Human operators then oversee strategy and take the final quality decisions rather than doing the production work themselves.
Did the rebuild actually work?
Operational costs are down 80% and we can publish three to four times faster when we need to, but revenue is still lower than it was before the 2024 decline began.
What improved:
- Operational cost: Down by 80%. The infrastructure costs a fraction of the editorial team payroll.
- Publishing velocity: We can produce 3–4x the content volume when we need to, though volume is rarely the goal.
- Consistency: The AI agents follow style guides more reliably than any human team. Every article goes through the same quality gates.
- Coverage: We can now cover topics that weren’t economically viable with human writers. The marginal cost of an additional article is near zero.
What didn’t improve, or got harder:
- Original reporting: AI can’t do interviews, attend events, or build source relationships. For publications that depend on original reporting, the entire model doesn’t work. We need additional humans in the loop.
- Voice distinctiveness: Even with 3,000-word style guides, the output tends toward a median. Getting genuine personality into AI-generated content still requires human editing.
- The bottleneck shift: AI compressed the writing stage but moved the constraint to visual creation and human review, rather than removing it.
- Revenue: This is the big one. The traffic decline that forced the transformation hasn’t reversed. We’re producing better content more efficiently, and we’ve protected our margins, but the audience is still migrating to AI interfaces.
We now have hundreds of articles ready to publish across Worthbury and a stealth e-commerce brand on Shopify. The constraint is no longer writing. It’s visual creation and human-in-the-loop review for brand safety.
The bottleneck moved.
It didn’t disappear. I’ve written about this in more detail in the bottleneck shift in AI publishing.
While we’ve successfully reshaped the operating model, we haven’t solved the revenue problem.
It is now a business model question: What is the future of online publishing when users are gated by LLM companies and AI agents are trained on content scraped from the web without compensating the source for their work and research?
I don’t have the answer to that question yet.
| Before (2020–2024) | After (2024 onwards) | |
|---|---|---|
| Team | 15 writers + editors | Small team of operators |
| Monthly output | 30+ articles | 3–4x capacity when needed |
| Operational cost | High editorial payroll | Down 80% |
| Writing | Human-authored | AI-drafted, quality-gated |
| Style enforcement | Editorial oversight | 3,000+ word encoded prompts |
| Revenue | Growing | Still rebuilding |
| Primary bottleneck | Writing throughput | Human time (visuals + review) |
What would I do differently?
If you’re thinking about starting this today, there are three things you should keep in mind, regardless of your industry:
-
Start early: We integrated AI into our workflows rapidly because we had to. We’re a small and agile team, so it worked, but the same probably wouldn’t be true for larger organisations. The learning curve is steep and it’s better to build the AI infrastructure without time or revenue pressure.
-
Design self-learning quality controls from the start: I find that the best system is one that constantly learns and improves itself. In our situation, the hardest part wasn’t the content production, but the quality control and incremental improvements that AI agents now handle autonomously.
-
Identify where the bottlenecks will move: For us, research and writing used to be the most time-consuming part of our work. Visual production was just something we did in parallel. By the time a story was ready, our photos and visuals usually were too. But once AI accelerated research and writing, a real disconnect happened as our visual workflow wasn’t designed to scale that fast. This is the ceiling we’re now working against.
The AI transformation isn’t done when AI can solve one problem. It’s done when the entire human workflow has been rebuilt around what AI has changed.
With everyone having access to the same models and tools, what sets you apart is what you bring that no one else has. Your unique data, expertise, and opinions.
The org-level argument for why most publishers are solving the wrong problem is in a companion piece: Most media publishers are solving the wrong AI problem.
If this is a problem you’re working on too, I’d love to hear about it. Find me on LinkedIn, X, or via the contact page.
I’ll keep writing about this as the picture gets clearer.
Cited byPerplexity10Gemini9ChatGPT6Google AI3
Spotted something I got wrong? hello@simonbeauloye.com
