AI writing tools make it cheap to publish a lot. That is the problem. Google does not penalise content for being AI-generated, but it has spent years building policies and systems against pages made at scale to rank rather than to help, and AI makes those pages easy to produce. AI helps growth when it speeds up work around a person with something to say. It hurts when it replaces the part that makes a page worth reading.
What Google has said, in its own words
Google’s position has been stable since early 2023, and the documents are short. Read them yourself; summaries (including this one) lose the qualifiers.
Quality, not method
In its February 2023 guidance, Google said its ranking systems aim to reward “original, high-quality content” that demonstrates E-E-A-T (experience, expertise, authoritativeness and trustworthiness), “however it is produced”. Its FAQ adds that “appropriate use of AI or automation is not against our guidelines”. The line it draws is purpose: using automation, including AI, “to generate content with the primary purpose of manipulating ranking in search results is a violation of our spam policies”.
Scaled content abuse
In March 2024, Google announced three new spam policies. One was scaled content abuse: “when many pages are generated for the primary purpose of manipulating Search rankings and not helping users”. It applies “no matter whether content is produced through automation, human efforts, or some combination”. The spam policies page gives examples: using generative AI tools to create many pages without adding value for users, scraping and rewording content, and stitching together content from different pages without adding value.
The same month’s core update went after low-quality, unoriginal content. When the rollout finished, Google reported that people would see 45% less of it in search results. It had expected 40%.
What quality raters are told
Google’s Search Quality Rater Guidelines do not rank pages directly, but they show what Google’s systems are meant to reward. The September 2025 edition says generative AI “can be a helpful tool for content creation, but like any tool, it can also be misused”. Raters are told to give the Lowest rating when all or almost all of a page’s main content is copied, paraphrased, embedded, “auto or AI generated”, or reposted “with little to no effort, little to no originality, and little to no added value for website visitors”. The same document says “the use of Generative AI tools alone does not determine the level of effort or Page Quality rating”.
The working guidance
Google’s guidance on using generative AI content makes three points you can act on. Generative models “don’t retrieve facts, but predict a likely sequence of words”, so fact-check everything. The same review applies to metadata: titles, meta descriptions, structured data and image alt text. Merchants have extra rules: AI-generated images must carry IPTC metadata marking them as AI-generated, and AI-generated product titles and descriptions must be specified separately in Merchant Center.
On disclosure, Google’s people-first content page suggests explaining how content was made where a reader might wonder. It calls fabricated creator profiles, “such as by using AI-generated headshots, made-up names, or false credentials”, deception.
Where AI-assisted writing helps
In every case that works, a person supplies the knowledge, data or judgement. AI cuts the time spent on everything around it.
| Task | Why AI helps | What a person must still do |
|---|---|---|
| Turning interviews and calls into drafts | Transcripts are long; AI finds structure fast | Choose the angle, check every quote and claim |
| First drafts from your own notes or data | Gets past the blank page | Rewrite for voice, add examples and judgement |
| Outlines and question research | Surfaces sub-questions to cover | Decide what your reader needs |
| Repurposing a long piece into posts, emails, summaries | Format conversion is routine | Check nothing got distorted |
| Editing for clarity and length | Good at cutting and simplifying | Keep the meaning and the voice |
| Help docs and support answers from product specs | Specs are the source of truth | Test the steps in the product |
| Titles, descriptions and alt text drafts | Many variations quickly | Check accuracy and length |
| Translation and localisation drafts | Speeds up a first pass | Native review for meaning and tone |
| Structured pages from real data | Consistent wording across many records | Make sure each page adds unique value |
Two rows deserve more than a table cell.
Help content has a clear source of truth, readers who want answers fast and a lot of repeated structure. AI can draft articles from specs and support tickets while a support or product person checks the steps. Well-structured docs can also become a search channel.
Programmatic SEO works when each page answers a specific query with data a reader cannot easily find elsewhere, such as prices, integrations or local information. AI can write consistent descriptive text around that data, but the data is what makes each page useful. Strip it out, and if the pages read the same, you are close to the scaled content abuse examples in Google’s policy.
Where it hurts
Hundreds of articles on topics you have no stake in
The classic failure: hundreds of AI-written articles on keywords near your niche, with no first-hand experience or data behind them. That is the pattern Google’s scaled content abuse policy describes, and the damage need not stay on those pages. Google’s spam policies say sites that violate them may rank lower or not appear in results at all.
Confident errors
AI tools write fluent text that can be wrong. In January 2023 The Verge reported that CNET had issued corrections on 41 of the 77 stories it had published using an AI tool. One wrong number in a pricing comparison or a tax explainer can cost more trust than ten correct articles earn.
Pages built from page one
If your article is assembled from what already ranks, it adds nothing to page one. It will also struggle to earn links, mentions or citations in AI answers, because there is nothing in it to cite. The pieces that do get cited usually contain something the tool could not produce: original research, real customer results, opinions from experience, or a worked example from your own data.
Fake expertise
Invented author personas, AI-generated headshots and implied first-hand testing that never happened cross from “AI-assisted” into deception. Google says so directly. Readers notice too.
An editorial workflow for AI-assisted content
People stay responsible for the judgement calls; AI does the parts where it saves time. Adapt the roles to your team. In a team of one you play every role, so do the steps on different days.
- Brief (person). Who is this for, what decision or task does it help with, and what do we know that others don’t? If you cannot answer the last question, stop. The page probably should not exist.
- Gather source material (person). Interviews, call notes, product data, support tickets, test results, screenshots. AI cannot invent this part.
- Outline (AI-assisted). Ask for sub-questions and structure based on your brief and sources. Cut anything your reader does not need.
- Draft (AI-assisted). Draft from your sources, not from the open web, and tell the tool to flag any statement it cannot tie to your material.
- Rewrite (person). Add the judgement, examples, trade-offs and voice. Remove filler, hedging and repeated points. Expect to change a large share of the draft.
- Fact-check (person, separate pass). Check every number, name, date, feature and price against a primary source and link to it. Remove anything you cannot verify.
- Metadata (AI-assisted, checked by a person). Draft the title, description and alt text, then check accuracy and length. A meta tag generator helps with the preview.
- Sign-off (person). One named owner on the team approves publication and is responsible for corrections.
- Review on a schedule. Re-check facts and prices on important pages every quarter, and after any product change.
Ten questions before an AI-assisted page goes live
A page that fails two or more is not ready.
- Does it contain something we know that a generic AI answer would not: data, an example, a test, a customer story or a clear opinion?
- Is every number, price, date and product detail checked against a primary source and linked?
- Would an expert in the subject find a factual error or a misleading simplification?
- Is the “who” accurate? Is it clear the content comes from our team, with no invented authors or credentials?
- Would we be comfortable telling readers how it was made?
- Does it answer the reader’s actual question near the top, without padding?
- Would we publish it if search engines did not exist? (Google’s “why” test in plain words.)
- Is it better than the pages already ranking for the topic, in a way a reader would notice?
- Are the title, description and alt text accurate and specific?
- If this is one of many similar pages, does each one add unique, useful information?
The same list works backwards on what you have already published. Read Google’s four short documents linked above (the 2023 guidance, the generative AI content page, the spam policies and the people-first content page), then run your last 30 pieces through these questions. Merge, improve or remove the ones that fail two or more, starting with anything produced in bulk on topics you have no first-hand knowledge of. Write the nine-step workflow down with a named owner for fact-checking and sign-off, and pick one piece of original material to build content around this month: a customer interview, a dataset or a test you ran.
Used that way, AI makes a small team faster at producing content that is theirs. Used as a substitute for knowledge, it produces exactly the page Google’s policies are written against.
Questions people ask
Does Google penalise AI-generated content?
Not for being AI-generated. Google's guidance says appropriate use of AI or automation is not against its guidelines. What breaks its spam policies is generating content primarily to manipulate search rankings, including scaled content abuse, whether a person or a tool produced it.
What is scaled content abuse?
Google defines it as generating many pages for the primary purpose of manipulating search rankings and not helping users. It usually means large amounts of unoriginal content with little or no value, no matter how it is created. Examples Google lists include using generative AI to create many pages that add no value.
Should I label content as AI-assisted?
Google suggests adding information on how content was created where readers would reasonably expect it, especially if automation substantially generated the content. It also warns against fake author profiles, such as AI-generated headshots or made-up names.
Can AI write meta titles and descriptions?
Yes, but review them. Google's guidance on generative AI content says the same accuracy review should apply to metadata such as title elements, meta descriptions, structured data and image alt text, because these can appear in search results.
Sources
- Google Search Central Blog: Google Search's guidance about AI-generated content
- Google Search Central: Guidance on using generative AI content on your website
- Google Search Central: Spam policies for Google web search
- Google Search Central Blog: What web creators should know about our March 2024 core update and new spam policies
- Google: New ways we're tackling spammy, low-quality content on Search
- Google: Search Quality Rater Guidelines (September 2025)
- Google Search Central: Creating helpful, reliable, people-first content
- The Verge: CNET found errors in more than half of its AI-written stories
