Last year, I was doing something that felt increasingly absurd: manually reading AI-generated content to decide if it was "good enough."
PostAll — the content automation tool I've been building — was producing hundreds of blog posts per week for clients. And I had no systematic way to evaluate quality at scale. I was spot-checking. Vibes-checking, really. That doesn't work at volume.
So I built a programmatic quality analysis pipeline, ran it over 1,000 AI-generated posts, and let the numbers tell me what my gut was missing.
The findings surprised me. A few of them genuinely changed how I think about AI content quality.
What I Actually Measured
