Does Google’s Use of AI Overviews Go Against Its Own Spam Policies?

Google’s AI Overviews display spam and rewrite existing content without offering meaningful analysis or insights, key markers of low-quality content.

According to search marketers, the new long-form AI-generated answers resemble the type of scraped content that Google itself advises against: lacking originality and added value. This practice disadvantages content creators, many of whom are experiencing drops in traffic.

The concern is apparent: users have little reason to visit the source if well-crafted content is rewritten into a full answer within the AI Overview.

AI Overviews and the Line Between Summarization and Plagiarism

Previously, Google featured short excerpts from web pages as Featured Snippets, encouraging users to click through to read the full content. The new AI Overviews present broader responses that often include anticipated follow-up questions sourced from existing content.

However, this approach raises concerns. Rather than generating original analysis, the AI repurposes information directly from published sources. This type of uncredited reuse is often labeled as plagiarism in academic or professional contexts.

Because AI tools cannot provide genuine insight or interpretation, the resulting summaries may offer little added value, posing challenges for content creators whose original work is now being reformatted without direct attribution or engagement.

Example of Content Repurposing in AI Overviews

A recent article by Lily Ray, shared on LinkedIn, highlighted concerns about Google’s AI Overviews (AIO) displaying content that mirrors her original writing. In the article, she outlined how SEO professionals discovered methods to inject answers into AIO, exploiting the system’s lack of robust fact-checking mechanisms.

Following publication, a search query revealed that Google had surfaced an AI-generated response that closely resembled her article in structure and length. Ray noted on X (formerly Twitter):

“It re-wrote everything I wrote in a post that’s basically as long as my original post.”

Was the Entire Article Replicated?

Large language models and search algorithms often assess web content by identifying the specific questions it answers. This process helps categorize and match search queries with relevant pages. When comparing Lily Ray’s article with Google’s AIO summary, both appeared to address a near-identical number of questions—13 in Ray’s post and 12 in AIO’s response.

Both sources addressed five key themes:

• Spam in AI Overviews
  • AIO: Is there a spam problem affecting Google AI Overviews?
  • Ray: What problems have been observed in Google’s AI Overviews?
• Manipulation and Exploitation
  • AIO: How are spammers manipulating AI Overviews to promote low-quality content?
  • Ray: What new forms of SEO spam have emerged in response to AI Overviews?
• Accuracy and Hallucination Risks
  • AIO: Can AI Overviews generate inaccurate or contradictory information?
  • Ray: Does Google currently fact-check or validate the sources used in AI Overviews?
• Concerns in the SEO Community
  • AIO: What concerns do SEO professionals have about the impact of AI Overviews?
  • Ray: Why is the ability to manipulate AI Overviews so concerning?
• Departure from Google’s E-E-A-T Standards
  • AIO: What kind of content is Google prioritizing in response to these issues?
  • Ray: How does the quality of information in AI Overviews compare to Google’s traditional emphasis on E-E-A-T and trustworthy content?

This overlap has reignited discussions about content attribution, originality, and whether AI-generated summaries are too close to replicating source material without offering meaningful new value.

Repurposing Content from Multiple Sources in AI Overviews

Google’s AI Overviews (AIO) system is designed to provide comprehensive responses, including follow-up and related answers. This often involves synthesizing information from more than one source. In a recent example, the system appeared to merge perspectives from two distinct articles into a single AI-generated response.

One of the original articles raised concerns about Google’s lack of effective spam prevention within AIO. However, the AI-generated content contrasted this view by referencing another source that suggested Google is actively addressing spam issues. The result was a composite response that included five additional questions not covered in the original critique, seemingly drawn from separate material.

Key Observations

• Google’s AIO routinely repurposes web content to form in-depth responses that often mirror the original structure and arguments found in published articles.
• The AI-generated summaries frequently replicate the core questions answered in source materials, producing outputs with minimal added insight or originality.
• In doing so, AIO responses may conflict with Google’s own quality guidelines, particularly regarding the need for unique value and accurate attribution.
• These responses often combine content from multiple sources without distinct attribution, raising questions about content reuse and intellectual property standards.
• The perceived lack of experience, original analysis, and fact-checking mechanisms in AIO can compromise the trustworthiness of the information presented.

The ability of AIO to generate comprehensive, near-article-length responses may also diminish the motivation for users to visit the sources. This dynamic has contributed to growing concern among publishers and SEO professionals about declining visibility and traffic. One industry observer recently commented that ranking first in search results offers little benefit under this system, highlighting a broader concern within the digital publishing space.

As long as AI-generated responses closely echo the structure and ideas of original web content without meaningful contribution or transformation, their alignment with content quality best practices will remain under scrutiny.