In the current media landscape, it’s easy to feel as if AI-generated content is everywhere, and that concern is not without precedent.
What started as psychedelic dog-like face images and robotic, nonsensical text monologues have morphed into the modern, intelligent, and increasingly difficult-to-detect AI content that we encounter every day.
Every article consumed, image viewed and video watched now triggers a small paranoid question in any responsible editor or consumer’s mind: “Did a human make this?” The reality? We’ve reviewed several independent studies which suggest humans can’t detect AI content.
From helpful editing and re-wording to full-on farmed AI-generated content with the purpose of gaming Google’s search engine optimization algorithm, AI content is present in Google’s search results, and it’s here to stay.
In response, we set out to give a data-backed, statistical count of AI’s presence in Google ratings to answer the question: how much AI content is present in Google?
By sampling the top 20 search results from 500 popular keywords from the beginning of 2019 to the present day, we’re tracking the saturation of AI in search results, all the way back to the release of GPT-2.
How Much AI Content is Present in Google?
Update: Our Results — January 21, 2025
Our latest findings, reviewed on January 21, 2025, found that the amount of AI content in Google Search Results is at an all-time high of 19.10%.
This marks a notable increase from the 18.07% of AI content found in Google Search Results in November 2024.
We'll continue monitoring the presence of AI content in Google, so check back on this live dashboard to stay up to date!
AI-Content in Google Search Results — a Timeline from 2024 to Present:
- January 21, 2025: at 19.10% AI content is at an all-time high.
- November 25, 2024: at 18.07% AI content reaches a record high.
- October 28, 2024: at 17.96% AI content continues to climb.
- September 24, 2024: at 11.46% AI content moderately increased from August 2024.
- August 26, 2024: at 11.21%, there is a slight dip in AI content levels.
- July 24, 2024: at 12.59%, July demonstrates that AI continues to rise in search results.
- June 24, 2024: at 11.67% AI content is trending upwards from previous months.
- May 22, 2024: at 11.11% AI content slightly dips from April 2024 levels.
- April 22, 2024: at 11.34% AI steadily increases month over month.
- March 23, 2024: at 10.18% following a Google update AI levels skyrocket.
- March 5, 2024: at 7.43% AI has dropped from December 2023 levels (8.48%)
AI Content in Google Our Results:
In general, over the course of our study, we’ve found a continuously increasing presence of AI content.
Before GPT-2 was released to the public, AI content was detected in only 2.3% of our sampled websites.
Around five years and three Open AI GPT models later, that percentage increased almost threefold to 10.18% in March 2024.
Now, in 2025 AI content is at an all-time high of 19.10%.
Did Google’s helpful content policy impact the presence of AI content?
We found that even after Google’s implementation of its helpful content policy, first introduced in August 2022, the machine-content saturation still increased.
It is however notable that with the introduction of AI to the general public via ChatGPT, we did expect to see a spike in AI content in Google, which our data does not confirm.
This suggests that Google’s helpful content and spam policies have been at least partially successful at keeping AI Spam at bay.
To learn more about Google’s interactions with AI content, check out our study on the March 2024 Google update, and its repercussions.
Then, get more insight into AI detection accuracy and review a meta-analysis of third-party studies on AI detection.
Curious about Google Reviews? There’s AI content in those too. Check out our AI Google Reviews Study for more information.
What are the Consequences of AI Content in Google?
The increasing presence of AI in Google is not to be understated. Learning Language Model (LLM) based AI tools are trained on very large human content datasets, often sourced from the internet via an automated scraping/crawling process. If not curated carefully, as the internet becomes more and more saturated with AI-generated content, these training datasets will as well. A study from May 2023 suggests that through multiple iterations of LLM training on datasets containing machine-generated content, models generate more generic and predictable results, and become more likely to mis-perceive their learning task over time. To offset this, the authors stress the importance of the continuing availability of non-machine generated training materials for future model learning. With future models of GPT and other AI models well on their way, and the continuously increasing rate of AI content in websites, said datasets will become more and more difficult to source.
Image: An AI model becoming more uniform over time (Ilia Shumailov et al.)
Data Collection and Analysis
The data collection phase of this project was designed with the goal of generating a representative sampling of the average results one would see on Google after searching an informational keyword. We automated each step, allowing us to analyze large quantities of data. The process is novel to us, and we believe as well to the general search engine community.
Choosing the Keywords
To seed the data points for our study, 500 Google Search keywords were chosen, with the resulting set having the following properties:
- Keywords were informational; i.e., they are searched when looking for an answer to a question. Informational keywords generate search results with large amounts of article text, making them good targets for AI Scanning. Example keywords include: “how to screenshot on mac”, “best albums of all time”, and “what are carbohydrates”.
- The set of chosen keywords has a similar search volume (read: popularity) distribution to that of the set of all informational keywords. To illustrate: Imagine we are choosing 10 keywords to represent the top 100 informational keywords. If 10% (i.e., ten) of the top keywords had a search volume of 2,000/month, then following our methodology, 10% (i.e., one) of our chosen 10 keywords would have a search volume of 2,000.
- Keywords should not have large fluctuations in popularity and/or search volume over time. Keywords such as those involving movies, sports events, video game releases, etc, were not considered.
Finding Keyword Search Results Over Time
For each keyword, we used a search engine optimization (SEO) tool to find their respective top 20 search results. We repeated this process every second month, from January 2019 to present day, resulting in a list of 10,000 websites for each two month period.
Scraping Article Text
For each list of websites/time period, we checked the Internet Archive to see if a website snapshots was available within its respective period. If a snapshot was available, we used a port of the Arc90 Readability Algorithm to extract the main article text from the Archive snapshot.
AI Scanning the Article Text
All scraped text was run through a data cleaning process, fixing extraneous white space and other punctuation artifacts, removing most non-article text such as citations and footnotes and ensuring that all text was sufficiently long enough to be run through the Originality.AI detector. Text from websites that were not article based, like Youtube and Reddit, was removed.
The text was then run through the Originality.AI detector, and its score was recorded. As the originality score represents the detector’s confidence that a text contains AI from 0 to 1, we consider a score of 0.5 or above to be a positive AI detection.
Image: Text from an Archive snapshot gets scraped and run through the AI detector
In summary: How can I offset the increasing AI content in Google?
Two things are clear: AI content is becoming more present and Google is increasing its efforts to keep unhelpful AI content at bay. Using tools like Originality.ai can help ensure that the content on your website is original, helpful, and plays well within Google’s rules for search engine results.
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Sources:
Ilia Shumailov et al. (2023). The Curse of Recursion: Training on Generated Data Makes Models Forget. Retrieved from arXiv:2305.17493