AI Detector for SEO & Blog Content
Google's position has been consistent and is widely misread: using AI is not itself a ranking problem. What gets demoted is content with no reason to exist — and unedited generated drafts fail that test in specific, diagnosable ways. A detector is useful here not as a compliance check but as a proxy for the flatness that makes a page forgettable.
What Google actually penalises
Scaled content produced primarily to manipulate rankings, regardless of how it was made. A human writing forty thin pages a week is in the same category as a model writing them. The distinguishing question in every helpful-content assessment is whether the page adds something a reader could not get from the results already ranking. Generated drafts fail that by default, because a model's output is a summary of what already ranks — which is the definition of adding nothing.
Why a high detector score predicts poor performance
Not because of a penalty, but because the two measure overlapping things. A detector scores uniformity: even sentence lengths, conventional transitions, safe generic vocabulary. Readers experience that same uniformity as content that says nothing memorable, and they leave. The score is a cheap early signal for a problem that would otherwise only show up weeks later in engagement data — so treat a high score as a content review trigger, not a compliance failure.
The three things generated drafts always lack
First, a specific number, name, or date you can source — models hedge because they are uncertain, and hedged prose is unrankable for informational queries. Second, an opinion that could be disagreed with; balanced coverage of every side reads as authority to nobody. Third, first-hand detail: what actually happened when you tried the thing. Adding those three to a generated draft usually drops the detector score as a side effect, because they are exactly the material a model cannot produce.
AI search reads differently from Google
Generative engines cite passages, not pages. That changes what a well-optimised section looks like: each heading should be a question, and the first paragraph under it should answer that question completely without depending on anything above it. Content written as a flowing argument with 'as mentioned above' running through it gets cited far less, because no single extractable chunk stands alone. This is a structural fix, and it is largely independent of who or what wrote the prose.
Disclosure, editorial process, and ad eligibility
Ad programmes including AdSense do not prohibit AI-assisted content; they prohibit content with no added value and content generated at scale without human oversight. The practical difference is a visible editorial process: a named author, factual claims that can be sourced, an honest treatment of limitations. If your site publishes assisted content, the safest posture is not to hide it but to make the human contribution obvious enough that no reviewer has to guess.
A workflow that survives review
Draft the structure yourself, so the argument is yours. Let a model expand sections where the facts are settled. Then do three passes: verify every number against a real source and delete what you cannot verify; add one concrete example per section that only you could supply; and vary the sentence rhythm by reading it aloud. Check the score at the end — if it is still high after those passes, the piece has a substance problem, not a style problem.
Quick decision rules
A draft scores high and has no sourced numbers
It is thin. Add verifiable specifics before touching the phrasing.
A draft scores high but is dense with first-hand detail
Fix the rhythm only. The substance is there; the sentences are just too even.
You publish assisted content
Name an author, source your claims, and state limits honestly. That is what separates permitted assistance from no-value-add content.
You want AI engines to cite you
Make each section answer its heading completely on its own. Extractability beats flow for citation.
Frequently asked questions
Google has said it focuses on content quality rather than production method, and detection at web scale is unreliable for the same reasons it is unreliable on a single essay. Optimise for usefulness, not for beating a classifier.