Google’s policy is clear: what matters is not how content was produced but whether it helps. Useful content written with AI ranks; worthless content written by a human does not.
But the risk is real in practice, because the default output of model use is usually worthless content.
Why content at scale collapses
- No originality. A model produces the average of its training data. Average text does not deserve to rank.
- No experience. The first E in E-E-A-T. A model has never used the product.
- No verification. Wrong figures, invented sources and dead links are common.
- Sameness. Ten competitors using the same prompt produce ten near-identical pages.
Where models genuinely help
- Outlines and structure. Fast decomposition of a topic into subheadings.
- First drafts. They fill the blank page; you add the real information.
- Rewriting. Tightening long, meandering paragraphs.
- Translation support. With human review.
- Meta descriptions and title variants. Generate dozens, pick the best.
- Structured data generation. Repetitive work that is hard to get wrong.
Where models must not be used
- Health, legal and financial content — never unverified
- Analysis based on your own data — you have the data, the model cannot invent it
- Case studies and customer stories — those transfer experience
- Product comparisons — if you have not tested it, do not write it
A workflow that holds up
- A human chooses the topic and target query
- The model drafts the outline; a human corrects it
- The model writes the draft
- A human adds original data, examples, experience and opinion — skip this and nothing else matters
- A human verifies every figure and source
- A human does the final read before publishing
The scale trap
"We can produce 200 posts a month" sounds like an opportunity; it is usually a risk. Worthless content does not merely fail to rank — it lowers the perceived quality of the whole site.
Twenty genuinely good articles bring more traffic than two hundred average ones. That has become more true in the model era, not less — because answer engines already generate the average content themselves.
A simple test
Before publishing, ask: what is on this page that a language model could not have produced on its own? If there is no answer, the page has no reason to exist.