There is a persistent belief that Google penalises AI-generated content. That is not what the guidance says, and the difference matters if you are using any kind of automation.
The actual position
Google's stated position is that it rewards high quality content regardless of how it was produced. What it acts against is content produced primarily to manipulate rankings, which it calls scaled content abuse.
The distinction is purpose, not method. A human writing three hundred thin pages to catch long tail queries is doing the thing Google objects to. Someone generating a well sourced explanation of a problem they actually solved is not, even if a model wrote the sentences.
In practice the signals that matter are the ones that have always mattered: does the page answer the question, does it contain information that is not available in ten other places, does anything on it suggest the author knows the subject.
Where generated content fails this
Not on the writing. On the substance.
Content generated from a prompt has no information the model did not already have, which means by definition it contains nothing that is not already on the internet. It is a recombination. It can be well written and still be redundant, and redundant is the thing that does not rank.
That is not a penalty. It is the absence of a reason to rank.
Where it passes
Generated content that starts from something only you have is different. The model is doing the writing, and the information is yours.
For a developer, that is a real advantage. Your commit history, your incident, your benchmark, your specific failure and what fixed it. Nobody else has that material, so no amount of recombination produces it.
The practical rule: if the piece could have been written by someone who had never done the work, it will not rank, whoever or whatever wrote it.
E-E-A-T in plain terms
Google's quality framework asks about experience, expertise, authoritativeness and trust. For technical content that mostly reduces to: is there evidence the author has done this?
Specific evidence helps. Version numbers, error messages, the thing you tried first that did not work, the constraint you hit. These are hard to fake and easy to recognise.
Generic evidence does not. "In my experience" followed by nothing specific is worse than no claim at all.
The practical checklist
Before publishing anything a model helped with:
- Does it contain at least one fact not available elsewhere?
- Would a practitioner learn something?
- Can every number be traced to a source?
- Does it have a point of view, or does it survey both sides and commit to neither?
- Is the title what the page is about, or what you wish people searched for?
If those pass, how it was written is not the issue.
And the one thing that is genuinely risky
Volume without substance. Publishing a hundred pages a week of generated material targeting keyword variants is the specific pattern the policy names, and it is the one that gets sites deindexed.
The defence is not writing by hand. It is having something to say each time, which naturally limits how often you can publish.