Google does not penalise AI content. It penalises content with nothing in it.

What the guidance actually says

Google's position has been consistent and public: the guidelines are about content quality, not about how the content was produced. Automation used to generate helpful, original material is fine. Automation used to generate large volumes of pages that exist only to catch search traffic is not, and that has been against the rules for well over a decade under names like scaled content abuse, spun content and doorway pages.

Read that carefully, because the distinction is the whole thing. The rule has never been about the tool. It has been about whether the output is worth a person's time. A model can produce something genuinely useful, and a human can produce a thousand words of nothing. The guidelines catch the second regardless of who or what typed it.

There is no detector deciding your rankings

The fear depends on a classifier that reliably identifies machine-written text. No such thing exists at the accuracy the fear requires. The public detectors are poor, they produce confident false positives on ordinary human writing, and they get worse with every model release. Building a ranking penalty on that would be building it on noise, and it would misfire constantly on legitimate publishers.

It also would not serve Google's interest. What Google needs to know is whether a page answers the query well, which it can assess directly from the page and from how the result performs. Authorship method is a proxy for that at best, and a bad one. This is the same proxy-mistaken-for-the-thing pattern that runs through most SEO myths.

What genuinely goes wrong with model-written pages

Plenty, and none of it is a secret penalty. The failures are ordinary and they are all about substance.

Sameness is the big one. A model trained on the existing web, asked to write about a subject, produces a competent synthesis of what is already ranking. That is by definition not additive, and "accurate summary of the current top ten" is the exact profile of a page Google sees no reason to index, which shows up as crawled, currently not indexed.

Then confident errors, which are expensive in any field where being wrong matters and disqualifying in the regulated ones. Then the absence of first-hand experience, the specific numbers, the thing that went wrong on a real project, the opinion someone would argue with, which is precisely the material that makes a page worth citing. And finally volume: the tools make it trivial to publish a hundred pages a month, which runs straight into the fact that you did not need a page per keyword and that thin pages compete with each other.

Where it helps, honestly

Drafting from material you already have. Restructuring something written badly. First passes at descriptions and summaries. Working through an outline. Translation and tone adjustment. All of that is real work made faster, and none of it manufactures substance that was not there.

The line we hold on our own writing is simple: a page goes up when there is a point worth making that we actually believe, and the drafting method after that is a detail. What we will not do is publish something with no position in it, because a page like that fails on the merits whoever wrote it. It also fails on the newer surface, since being cited by a model depends on having a concrete, attributable claim, and a page assembled from everyone else's claims does not have one.

The general lesson

"Will Google know it was AI" is the wrong question, and it is comforting precisely because it makes the problem technical and external. The real question is the old one: does this page deserve to exist. If it does, use whatever tools you like to get it written. If it does not, no amount of humanising the prose will save it, and the thousand near-identical pages produced in the meantime are a volume problem that costs crawl budget and dilutes the pages that were working.