Remember when it was easy to rank partial-match domains and headings to commercially intended search queries?
When paired with the right methodologies and conversion-optimized widgets, you could silently earn tens of thousands of dollars in affiliate revenue per month with minimal maintenance.
It was possible to get by with just updating articles for relevancy and freshness signals, for example.
Pressure-testing Google’s spam update
Before the experiment, I had spent several months scaling an affiliate initiative in a much more above-board way for a longstanding website in a YMYL category.
We had success with hiring subject matter experts (SMEs) to write helpful, educational content that actually informed readers.
While the new content primarily targeted commercially intended keywords, that wasn’t the website’s sole purpose for existing. There were also thousands of pages of user-generated content (UGC) that inspired the new content, and visitors would navigate from them to convert, as well.
We had brand trust, original research, expert insights, and everything else you’d expect from a reputable publisher.
It was a perfect mix: verticalized legacy UGC with thousands of earned backlinks and a commercial lever that served a preexisting demand while adhering to industry best practices. It was a truly helpful experience.
The experiment: Scaling AI without trust
If the first model was built on trust and earned authority, this one would remove those signals entirely.
During that time, influencers on LinkedIn were doing the same thing. Except they were using AI to generate thousands of pages by scraping and rewriting content, or by programmatically aggregating public data.
That’s when I searched in my couch pillows for a few dollars and bought three domains that partially matched the following queries: “best welding schools,” “best plumbing schools,” and “best electrical schools.”
The goal? Intentionally test a set of low-trust, high-scale tactics that are commonly promoted online and see how long they would persist.
I then used AI to make the websites pretty, fetched public data with a vibe-coded Python API call, and used ChatGPT to template all of the subheadings and paragraph text you would typically see ranking across the web.
Within a few hours, with the help of liquid content, I published thousands of bottom-funnel pages across three websites. I was able to inject public data, target superlatives by program type and state, and include a directory with individual, templated pages per school.
I even leveraged aggressive internal linking practices that prioritized crawl coverage over user intent.
The setup violated almost every long-term trust signal — which made it a useful test of how the system would react.
All three sites shared the same traits:
- Zero brand signals.
- Programmatic AI-generated content.
- Public data aggregation.
- Aggressive internal…
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