Most AI content systems are evaluated by a simple metric:
How many articles did they generate?
I'm starting to think that's the wrong metric.
Today's workflow
My editorial pipeline:
Processed this week's topic queue
Generated new article angles
Detected duplicate topics
Verified research availability
Prepared only qualified articles for drafting
An interesting outcome:
The system intentionally skipped several topics because they would have duplicated existing content.
Why this matters
Generating another article is easy.
Avoiding unnecessary content is much harder.
Every duplicate article increases:
Editorial workload
SEO cannibalization risk
Maintenance costs
Reader fatigue
A production AI system should make decisions before generation begins.
The lesson
I'm no longer trying to build an AI that writes everything.
I'm trying to build one that knows:
what to write,
what to skip,
and why.
I think that's where the next generation of AI editorial systems will create the most value.
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