Does Google’s AI Overviews Violate Its Own Spam Policies? via @sejournal, @martinibuster

Search marketers assert that Google’s new long-form AI Overviews answers have become the very thing Google’s documentation advises publishers against: scraped content lacking originality or added value, at the expense of content creators who are seeing declining traffic.

Why put the effort into writing great content if it’s going to be rewritten into a complete answer that removes the incentive to click the cited source?

Rewriting Content And Plagiarism

Google previously showed Featured Snippets, which were excerpts from published content that users could click on to read the rest of the article. Google’s AI Overviews (AIO) expands on that by presenting entire articles that answer a user’s questions and sometimes anticipates follow-up questions and provides answers to those, too.

And it’s not an AI providing answers. It’s an AI repurposing published content. That action is called plagiarism when a student does the same thing by repurposing an existing essay without adding unique insight or analysis.

The thing about AI is that it is incapable of unique insight or analysis, so there is zero value-add in Google’s AIO, which in an academic setting would be called plagiarism.

Example Of Rewritten Content

Lily Ray recently published an article on LinkedIn drawing attention to a spam problem in Google’s AIO. Her article explains how SEOs discovered how to inject answers into AIO, taking advantage of the lack of fact checking.

Lily subsequently checked on Google, presumably to see if her article was ranking and discovered that Google had rewritten her entire article and was providing an answer that was almost as long as her original.

She tweeted:

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

It re-wrote everything I wrote in a post that’s basically as long as my original post ☠️ pic.twitter.com/ucNCSMHsY4

— Lily Ray 😏 (@lilyraynyc) May 18, 2025

Did Google Rewrite Entire Article?

An algorithm that search engines and LLMs may use to analyze content is to determine what questions the content answers. This way the content can be annotated according to what answers it provides, making it easier to match a query to a web page.

I used ChatGPT to analyze Lily’s content and also AIO’s answer. The number of questions answered by both documents were almost exactly the same, twelve. Lily’s article answered 13 questions while AIO provided answeredo twelve.

Both articles answered five similar questions:

• Spam Problem In AI Overviews
  AIO: “s there a spam problem affecting Google AI Overviews?
  Lily Ray: What types of problems have been observed in Google’s AI Overviews?
• Manipulation And Exploitation of AI Overviews
  AIO: How are spammers manipulating AI Overviews to promote low-quality content?
  Lily Ray: What new forms of SEO spam have emerged in response to AI Overviews?
• Accuracy And Hallucination Concerns
  AIO: Can AI Overviews generate inaccurate or contradictory information?
  Lily Ray: Does Google currently fact-check or validate the sources used in AI Overviews?
• Concern About AIO In The SEO Community
  AIO: What concerns do SEO professionals have about the impact of AI Overviews?
  Lily Ray: Why is the ability to manipulate AI Overviews so concerning?
• Deviation From Principles of E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness)
  AIO: What kind of content is Google prioritizing in response to these issues?
  Lily Ray: How does the quality of information in AI Overviews compare to Google’s traditional emphasis on E-E-A-T and trustworthy content?

Plagiarizing More Than One Document

Google’s AIO system is designed to answer follow-up and related questions, “synthesizing” answers from more than one original source and that’s the case with this specific answer.

Whereas Lily’s content argues that Google isn’t doing enough, AIO rewrote the content from another document to say that Google is taking action to prevent spam. Google’s AIO differs from Lily’s original by answering five additional questions with answers that are derived from another web page.

This gives the appearance that Google’s AIO answer for this specific query is “synthesizing” or “plagiarizing” from two documents to answer the question Lily Ray’s search query, “spam in ai overview google.”

Takeaways

• Google’s AI Overviews is repurposing web content to create long-form content that lacks originality or added-value.
• Google’s AIO answers mirror the content they summarize, copying the structure and ideas to answer identical questions inherent in the articles.
• Google’s AIO arguably deviates from Google’s own quality standards, using rewritten content in a manner that mirrors Google’s own definitions of spam.
• Google’s AIO features apparent plagiarism of multiple sources.

The quality and trustworthiness of AIO responses may  not reach the quality levels set by Google’s principles of Experience, Expertise, Authoritativeness, and Trustworthiness because AI lacks experience and apparently there is no mechanism for fact-checking.

The fact that Google’s AIO system provides essay-length answers arguably removes any incentive for users to click through to the original source and may help explain why many in the search and publisher communities are seeing less traffic. The perception of AIO traffic is so bad that one search marketer quipped on X that ranking #1 on Google is the new place to hide a body, because nobody would ever find it there.

Google could be said to plagiarize content because AIO answers are rewrites of published articles that lack unique analysis or added value, placing AIO firmly within most people’s definition of a scraper spammer.

Featured Image by Shutterstock/Luis Molinero

Google Launches Generative AI Certification With Free Training via @sejournal, @MattGSouthern

Google launches a generative AI certification with free training, focusing on Google’s tools. Is this credential worth adding to your resume?

• Google has launched a generative AI certification program.
• The certification is focused exclusively on Google’s own AI tools.
• The certification exam costs $99, but all training materials are free.

Google Links To Itself: 43% Of AI Overviews Point Back To Google via @sejournal, @MattGSouthern

New research shows that Google’s AI Overviews often link to Google, contributing to the walled garden effect that encourages users to stay longer on Google’s site.

A study by SE Ranking examined Google’s AI Overviews in five U.S. states. It found that 43% of these AI answers contain links redirecting users to Google’s search results. Each answer typically includes 4-6 links to Google.

This aligns with recent data indicating that Google users make 10 clicks before visiting other websites. These patterns suggest that Google is working to keep users within its ecosystem for longer periods.

Google Citing Itself in AI Answers

The SE Ranking study analyzed 100,013 keywords across five states: Colorado, Texas, California, New York, and Washington, D.C.

It tracked how Google’s AI summaries function in different regions. Although locations showed slight variance, the study found that Google.com is the most-cited website in AI Overviews.

Google appears in about 44% of all AI answers, significantly ahead of the next most-cited sources, YouTube, Reddit, Quora, and Wikipedia, appearing in about 13%.

The research states:

“Based on the data combined from all five states (141,507 total AI Overview appearances), our data analysis shows that 43.42% (61,437 times) of AI Overview responses contain links to Google organic results, while 56.58% of responses do not.”

Image Credit: SE Ranking

Building on the Walled Garden Trend

These findings complement a recent analysis from Momentic, which found that Google’s “pages per visit” has reached 10, indicating users spend significantly more clicks on Google before visiting other sites.

Overall, this research reveals Google is creating a more self-contained search experience:

• AI Overviews appear in approximately 30% of all searches
• Nearly half of these AI answers link back to Google itself
• Users now make 10 clicks within Google before leaving
• Longer, more specific queries trigger AI Overviews more frequently

Google still drives substantial traffic outward; 175.5 million visits in March, according to Momentic.

However, it’s less effective at sending users away than ChatGPT. Google produces just 0.6 external visits per user, while ChatGPT generates 1.4 visits per user.

More Key Stats from the Study

The SE Ranking research uncovered several additional findings:

• AI Overviews almost always appear alongside other SERP features (99.25% of the time), most commonly with People Also Ask boxes (98.5%)
• The typical AI Overview consists of about 1,766 characters (roughly 254 words) and cites an average of 13.3 sources
• Medium-difficult keywords (21-40 on the difficulty scale) most frequently trigger AI Overviews (33.4%), whereas highly competitive terms (81-100) rarely generate them (just 3.7%)
• Keywords with CPC values between $2-$5 produce the highest rate of AI Overviews (32%), while expensive keywords ($10+) yield them the least (17.3%)
• Fashion and Beauty has the lowest AI Overview appearance rate (just 1.4%), followed by E-Commerce (2.1%) and News/Politics (3.8%)
• The longer an AI Overview’s answer, the more sources it cites. Responses under 600 characters cite about five sources, while those over 6,600 characters cite around 28 sources.

These statistics further emphasize how Google’s AI Overviews are reshaping search behavior.

This data stresses the need to optimize for multiple traffic sources while remaining visible within Google’s results pages.

U.S. Copyright Office Cites Legal Risk At Every Stage Of Generative AI via @sejournal, @martinibuster

The United States Copyright Office released a pre-publication version of a report on the use of copyrighted materials for training generative AI, outlining a legal and factual case that identifies copyright risks at every stage of generative AI development.

The report was created in response to public and congressional concern about the use of copyrighted content, including pirated versions, by AI systems without first obtaining permission. While the Copyright Office doesn’t make legal rulings, the reports it creates offer legal and technical guidance that can influence legislation and court decisions.

The report offers four reasons AI technology companies should be concerned:

1. The report states that many acts of data acquisition, the process of creating datasets from copyrighted work, and training could “constitute prima facie infringement.”
2. It challenges the common industry defense that training models does not involve “copying,” noting that the process of creating datasets involves the creation of multiple copies, and that improvements in model weights can also contain copies of those works. The report cites reports of instances where AI reproduces copyrighted works, either word for word or “near identical” copies.
3. It states that the training process implicates the right of reproduction, one of the exclusive rights granted to emphasizes that memorization and regurgitation of copyrighted content by models may constitute infringement, even if unintended.
4. Transformative use, where it adds a new meaning to an original work, is an important consideration in fair use analysis. The report acknowledges that “some uses of copyrighted works in AI training are likely to be transformative,” but it “disagrees” with the argument that AI training is transformative simply because it resembles “human learning,” such as when a person reads a book and learns from it.

Copyright Implications At Every Stage of AI Development

Perhaps the most damning part of the report is where it says that there may be copyright issues at every stage of the AI development and lists each stage of development and what may be wrong with it.

“A. Data Collection and Curation

The steps required to produce a training dataset containing copyrighted works clearly implicate the right of reproduction…

B. Training

The training process also implicates the right of reproduction. First, the speed and scale of training requires developers to download the dataset and copy it to high-performance storage prior to training.96 Second, during training, works or substantial portions of works are temporarily reproduced as they are “shown” to the model in batches.

Those copies may persist long enough to infringe the right of reproduction,160 depending on the model at issue and the specific hardware and software implementations used by developers.

Third, the training process—providing training examples, measuring the model’s performance against expected outputs, and iteratively updating weights to improve performance—may result in model weights that contain copies of works in the training data. If so, then subsequent copying of the model weights, even by parties not involved in the training process, could also constitute prima facie infringement.

C. RAG

RAG also involves the reproduction of copyrighted works.110 Typically, RAG works in one of two ways. In one, the AI developer copies material into a retrieval database, and the generative AI system can later access that database to retrieve relevant material and supply it to the model along with the user’s prompt.111 In the other, the system retrieves material from an external source (for example, a search engine or a specific website).181 Both methods involve making reproductions, including when the system copies retrieved content at generation time to augment its response.

D. Outputs

Generative AI models sometimes output material that replicates or closely resembles copyrighted works. Users have demonstrated that generative AI can produce near exact replicas of still images from movies,112 copyrightable characters,113 or text from news stories.114 Such outputs likely infringe the reproduction right and, to the extent they adapt the originals, the right to prepare derivative works.”

The report finds infringement risks at every stage of generative AI development, and while its findings are not legally binding, they could be used to create legislation and serve as guidance for courts.

Takeaways

• AI Training And Copyright Infringement:
  The report argues that both data acquisition and model training can involve unauthorized copying, possibly constituting “prima facie infringement.”
• Rejection Of Industry Defenses:
  The Copyright Office disputes common AI industry claims that training does not involve copying and that AI training is analogous to human learning.
• Fair Use And Transformative Use:
  The report disagrees with the broad application of transformative use as a defense, especially when based on comparisons to human cognition.
• Concern About All Stages Of AI Development:
  Copyright concerns are identified at every stage of AI development, from data collection, training, retrieval-augmented generation (RAG), and model outputs.
• Memorization and Model Weights:
  The Office warns that AI models may retain copyrighted content in weights, meaning even use or distribution of those weights could be infringing.
• Output Reproduction and Derivative Works:
  The ability of AI to generate near-identical outputs (e.g., movie stills, characters, or articles) raises concerns about violations of both reproduction and derivative work rights.
• RAG-Specific Infringement Risk:
  Both methods of RAG, copying content into a database or retrieving from external sources, are described as involving potentially infringing reproductions.

The U.S. Copyright Office report describes multiple ways that generative AI development may infringe copyright law, challenging the legality of using copyrighted data without permission at every technical stage, from dataset creation to model outputs. It rejects the use of the analogy of human learning as a defense and the industry’s broad application of fair use. Although the report doesn’t have the same force as a judicial finding, the report can be used as guidance for lawmakers and courts.

Featured Image by Shutterstock/Treecha