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Good Vs. Bad pSEO: Learnings From A Decade Of Building Scalable Content Engines via @sejournal, @_nitman

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Good Vs. Bad pSEO: Learnings From A Decade Of Building Scalable Content Engines via @sejournal, @_nitman

Build programmatic SEO pages that earn their place with unique data, launch checks, and pruning rules that keep scaled publishing from becoming spam.

Generative AI has removed the barrier to publishing at scale. A brand can produce thousands of pages in an afternoon, and even be bold enough to call it programmatic SEO. For many brands, it worked, bringing in massive search traffic and organic revenue.

Management teams also buy in easily, because time to market is short and production costs are low.

Those same two things are now what make it fail. Aggressive programmatic SEO initiatives like these are collapsing or drawing manual penalties, because no one asks whether these pages deserve to exist.

Google has been clear about this. On March 5, 2024, it strengthened its scaled content abuse policy to apply whether automation, humans, or a combination produces the pages, and later reported 45% less low-quality, unoriginal content in results.

On Sept. 7, 2026, John Mueller replied on Bluesky to a site that generated pages from every combination of domain names, technologies, and attributes. He wrote that programmatic SEO of that kind often leads to a site that’s spam, borderline spam, or low quality, and that pages already published may keep shaping how Google judges the whole site.

I have been building programmatic SEO systems since 2016, from static templates to natural language generation engines such as Rosae NLG to AI-assisted pipelines. Some compounded for years and became true case studies for me. Some failed fairly quickly.



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