Digital marketers claim that Google’s latest AI Overviews may ironically embody the same practices their own guidelines warn against: the use of content that is copied without originality or additional value.
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This trend potentially undermines content creators, leading to decreased traffic as original efforts are overshadowed by AI-generated summaries.
The question arises: what’s the point of producing high-quality content when it’s destined to be transformed into extensive answers that diminish the incentive for users to engage with the original sources?
Repurposing Content and Ethical Implications
Google’s AI Overviews extend beyond traditional Featured Snippets by offering comprehensive answers that not only address user queries but also anticipate subsequent questions.
However, this approach raises concerns about the originality and integrity of the content being presented.
Understanding Plagiarism in AI Context
The practice of AI Overviews repurposing existing content without adding unique perspectives parallels academic definitions of plagiarism, where original work is used without proper attribution or enhancement.
Instead of generating fresh insights, Google’s AI Overviews rely on restructuring published material, which results in content that mirrors existing sources without providing additional analysis or value.
This method can be likened to students copying essays without contributing their own ideas, thereby compromising the quality and authenticity of the information presented. The fundamental issue with AI Overviews is their inability to offer novel insights or critical analysis.
By merely rearranging existing information, these overviews fail to enhance the original content, rendering them ineffective in providing meaningful value to users seeking in-depth understanding.
Case Study: Rewritten Content Example
An illustrative instance of this phenomenon can be observed in the experience of Lily Ray, a prominent figure in the SEO community, who encountered firsthand the implications of Google’s content rewriting practices.
Lily Ray’s Experience with AI Overviews
Lily Ray highlighted the issue by publishing an article that not only brought attention to the spam problem within AI Overviews but also demonstrated how her content was subsequently rewritten by Google’s system.
In her LinkedIn post, Lily explained how SEOs manipulated AI Overviews to insert answers, exploiting the lack of rigorous fact-checking.
When she checked Google to see if her article was ranking, she found that the AI had essentially recreated her entire piece, offering an answer nearly identical in length and content to her original work.
This led her to express frustration on social media, pointing out the lack of originality in the AI-generated responses.
This example underscores the potential for AI Overviews to replicate and rephrase existing content without adding substantive value, thereby diminishing the original author’s efforts and reducing the incentive for users to visit the source material.
Analysis of Google’s Content Rewriting
To assess whether Google’s AI Overviews are rewriting entire articles, an examination of the similarities between original content and AI-generated answers is essential.
Comparative Evaluation Using ChatGPT
Utilizing AI tools like ChatGPT to analyze both the original article and the AI Overview revealed striking similarities in the number of questions addressed and the depth of responses provided.
In the case of Lily Ray’s article, both her original content and the AI Overview answered approximately twelve questions each, with five overlapping in theme and substance. This similarity suggests that the AI is not merely summarizing but actively rephrasing existing content to fit user queries.
Such findings indicate that Google’s AI Overviews may be employing algorithms that prioritize content replication over originality, thereby raising questions about the compliance of these practices with Google’s own spam policies.
Implications of Multi-Source Plagiarism
Google’s AI Overviews are designed to synthesize information from multiple sources to provide comprehensive answers.
However, this method can lead to unintended consequences regarding content originality and authenticity.
Synthesizing Without Attribution
When AI Overviews draw from various documents to answer a single query, the resulting content often lacks proper attribution and fails to credit the original sources adequately.
In the instance of Lily’s query about spam in AI Overviews, the AI-generated answer amalgamated information from her article and another web page without distinguishing between the two sources.
This not only constitutes a form of plagiarism but also undermines the credibility of the information presented.
By combining content from multiple sources without clear attribution, Google’s AI Overviews risk violating ethical standards and diminishing the quality of information available to users.
The Bottom Line
Google’s AI Overviews appear to be repurposing web content to generate extensive answers that lack originality and added value.
By mirroring the structure and ideas of the original articles, these overviews may be infringing upon Google’s own definitions of spam.
The absence of unique analysis and the potential for uncredited content synthesis undermine the principles of Experience, Expertise, Authoritativeness, and Trustworthiness that Google upholds.
Consequently, this practice not only affects content creators by reducing their traffic but also diminishes the overall quality of information available to users.
