Does Google’s AI Overview Break Its Own Spam Rules?

Google’s AI Overviews (AIO) have come under criticism for displaying spam-like content and rewording existing information without offering any additional analysis or meaningful insight. This practice raises concerns about content quality, as it mirrors the very characteristics Google advises site owners to avoid.

According to many search marketing professionals, these AI-generated long-form answers risk turning into the kind of low-value content Google typically warns against. Instead of creating unique or insightful summaries, the system often reuses content in a way that lacks originality or depth.

Content creators are particularly concerned about the impact on website traffic. As Google’s AI pulls and rewrites detailed answers directly into search results, users may feel less inclined to visit the original sources. This could potentially discourage publishers from investing time and resources into producing high-quality material.

Many are questioning the purpose of crafting well-researched content if it ends up being repackaged into an AI-generated answer—stripping away both visibility and engagement from the original authors.

Rewriting Content And Plagiarism

In the past, Google displayed Featured Snippets—short sections pulled from online articles that users could click on to explore the full content. However, with the rollout of AI Overviews (AIO), Google now offers more extensive answers that sometimes anticipate follow-up questions and respond to those as well.

Rather than generating new insights, the AIO system simply repurposes existing material from other publishers. While this might seem helpful at first glance, many argue that when a student takes an existing essay and rephrases it without contributing anything original, it’s considered plagiarism.

The concern with AI-generated content is that these systems cannot produce truly original thought or analysis. As a result, what users are seeing in AI Overviews is often just a reworded version of existing work—offering no added value. In academic circles, this would be labelled as plagiarism, and many believe the same scrutiny should apply in this digital context.

Example Of Rewritten Content

Lily Ray recently published an article on LinkedIn drawing attention to a concerning spam issue within Google’s AI Overviews (AIO). In her post, she described how SEO specialists have discovered ways to inject answers directly into the AIO feature, taking advantage of the platform’s apparent lack of fact-checking or verification. This loophole has raised worries about the reliability and quality of the content presented in these AI-generated summaries.

Curious to see how her article was performing in search results, Lily later checked Google herself. What she discovered was unexpected: instead of simply listing her article, Google’s AI had effectively rewritten her entire post and was displaying it as a comprehensive answer in the AIO format. The rewritten content was nearly as lengthy as her original article, raising questions about originality and content ownership.

Feeling compelled to share her experience, Lily took to Twitter to express her frustration. She wrote, “It re-wrote everything I wrote in a post that’s basically as long as my original post.” This statement highlights a growing concern among content creators, who fear that their original work is being repurposed without proper credit or added value, potentially reducing traffic to their sites.

This incident shines a light on the broader challenges posed by AI-generated content in search results. While AI Overviews aim to provide quick, useful answers, the risk of repackaging existing content without meaningful insight could undermine the efforts of content creators and disrupt fair attribution.

Did Google Rewrite Entire Article?

One method that search engines and large language models (LLMs) might use to analyse content is by identifying the specific questions that the content answers. This allows the content to be labelled according to the answers it provides, which helps match user queries more effectively to relevant web pages.

To explore this further, I used ChatGPT to compare Lily Ray’s article with the AI Overview (AIO) answer. Interestingly, both pieces addressed almost the same number of questions—Lily’s article answered thirteen, while the AIO response covered twelve.

Among these, five questions were answered by both sources, highlighting key overlaps in their focus. These included concerns about spam issues affecting AI Overviews, where the AIO asked, “Is there a spam problem affecting Google AI Overviews?” and Lily’s article inquired, “What types of problems have been observed in Google’s AI Overviews?”

Both also addressed the ways spammers are exploiting AI Overviews to push low-quality content. The AIO asked, “How are spammers manipulating AI Overviews to promote low-quality content?” while Lily Ray questioned, “What new forms of SEO spam have emerged in response to AI Overviews?”

Accuracy and the risk of misinformation were also common themes. The AIO questioned whether AI Overviews can generate inaccurate or contradictory information, whereas Lily asked if Google currently fact-checks or validates the sources used in these summaries.

Concerns within the SEO community about the influence of AI Overviews were another shared topic. The AIO asked, “What concerns do SEO professionals have about the impact of AI Overviews?” and Lily explored why the ability to manipulate these AI summaries is troubling.

Finally, both discussed Google’s shift away from its usual principles of E-E-A-T—Experience, Expertise, Authoritativeness, and Trustworthiness. The AIO questioned what type of content Google is prioritising in light of these issues, while Lily examined how the quality of information in AI Overviews compares to Google’s traditional standards for reliable content.

Plagiarizing More Than One Document

Google’s AI Overview (AIO) system is designed to handle follow-up and related questions by “synthesising” answers from multiple original sources. This approach is evident in the way it responded to this particular query.

While Lily Ray’s article argues that Google is not doing enough to address the issue, the AIO response instead rewrites content from a different source, suggesting that Google is actively taking steps to combat spam.

In addition, Google’s AIO answer differs from Lily’s original content by including responses to five extra questions, drawing these answers from another web page entirely.

This creates the impression that Google’s AIO is either “synthesising” information from two separate documents or, as some might argue, effectively “plagiarising” to answer Lily Ray’s search query about spam in Google’s AI Overviews.

Takeaways

Google’s AI Overviews repurpose existing web content to produce long-form answers that often lack originality or added value. These AI-generated responses closely follow the structure and ideas of the source articles, effectively copying their content to address the same questions.

This approach arguably conflicts with Google’s own quality standards, as it resembles behaviours that Google itself categorises as spam. The AI Overviews frequently display signs of plagiarism by drawing heavily from multiple sources without providing unique insights.

Furthermore, the quality and reliability of these AI-generated answers may fall short of Google’s principles of Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T). Since AI lacks true experience and there appears to be no effective fact-checking process, the responses may not meet the expected standards.

Because AI Overviews deliver essay-length answers, users have little incentive to visit the original content. This has contributed to a noticeable drop in traffic for many publishers and marketers. In fact, one search marketer joked on X that ranking number one on Google is “the new place to hide a body,” highlighting how visibility has diminished under this system.

In essence, Google’s AI Overviews could be seen as plagiarising content, as they rewrite published articles without adding unique analysis or value. This practice aligns with common definitions of scraper spam, raising serious concerns among the search and publishing communities.