Knowledge base v2.13.0 · Community edition · data through 2 October 2026
Topic · AI and Search
Raising on its own slide the question this topic usually prompts, whether it can tell AI text from human text, Google said its ranking systems are trained on content by humans for humans and promote natural content better. Author’s view: that is not a claim of detection, and the risk is scaled, low-value content. Day 3 answered the question more fully: Google's quality talk said quality problems are quality issues, not AI versus human ones, because much good AI-assisted content exists, and the rater guidelines say the tool is not the problem but how it was used and what for. The risk is scale and deception: Google counts AI slop, mass-produced LLM content, as scaled content abuse, a Google speaker expected scaled content abuse to replace link spam as the spam type worth discussing, and the rater guidelines rate fake author personas with AI-generated headshots as Lowest. Google also said hallucinations cannot be removed with current training methods, so publishing unchecked AI output can harm a site's standing indirectly, and its closing slide said to use AI, responsibly. A second recording of Day 1 made the answer explicit: Gary Illyes said Google is not really trying to tell AI-written from human-written content because it cares more about quality than about how content was made, as the keynote also said, though he added that the more important documents in its index tend to be human-created and that the natural content its algorithms promote includes content created, or at least edited and reviewed, by humans (said at the event). On Day 2 he said AI-generated images on a site are up to the site owner and that Google shows them when users look for them, but that image models are poor at text, so sites should check them for hallucinations; Google's image metadata guide supports IPTC and C2PA labels for AI-made images.
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A Google slide headed 'Your Question' raised whether Google can distinguish between AI-written and human-written content, presented on stage as the question this topic usually prompts rather than one asked live.
Speaker Cherry Prommawin, Gary IllyesIn Day 1, 11:45 · How Search works and where's AI?Evidence slide photo, transcript
Google's answer was that ML-based ranking systems are trained on content written by humans for humans, so they understand and promote natural content better.
“ML based ranking algorithms and signals are trained on content by humans for humans. They "understand" and promote natural content better.”
Wording checked against the slide or recording
Speaker Cherry Prommawin, Gary IllyesIn Day 1, 11:45 · How Search works and where's AI?Evidence slide photo, transcript
Explaining the principle of incentivising high-quality content, Google said the content may be created by humans or by AI: what matters is that it is high quality and made for users, not for search.
“we want to incentivise high-quality content, created by humans or by AI. But the key is that it's high quality”
Wording checked against the slide or recording
Speaker Lino CattaruzziIn Day 1, 11:00 · Welcome and opening keynotesEvidence transcript
Google said it is not really trying to tell AI-written from human-written content, because it cares more about the quality of content than about how it was created.
“We care more about the quality of the content than how it was created.”
Speaker Gary IllyesIn Day 1, 11:45 · How Search works and where's AI?Evidence transcript
Google said the more important documents in its index naturally tend to be documents created by humans.
Speaker Gary IllyesIn Day 1, 11:45 · How Search works and where's AI?Evidence transcript
Google said the natural content its ranking algorithms understand and promote better includes content created by humans or at least edited and reviewed by them.
“content that was created by humans, or at least edited and reviewed”
Speaker Gary IllyesIn Day 1, 11:45 · How Search works and where's AI?Evidence transcript
The answer is not a claim that Google detects AI text. It says ranking favours text that reads as natural to people. The risk with AI content is scale without value, which falls under Google's scaled content abuse policy, not the tool itself.
Author Ibrahim AnjroAnnotates Day 1, 11:45 · How Search works and where's AI?
To the frequent question about AI-generated images on a site, Google's answer is that it is up to the site owner.
Speaker Gary IllyesIn Day 2, 13:50 · Using images to your advantage and Engaging Search users with videosEvidence transcript
Used byrequirements DEV-IMG-08, DEV-SPM-05
Google serves AI-generated images and videos in search results when users are specifically looking for them.
“We will serve users AI-generated images and videos in search results if they are looking for them specifically.”
Speaker Gary IllyesIn Day 2, 13:50 · Using images to your advantage and Engaging Search users with videosEvidence transcript
Gary Illyes showed a timeline image that Google made with an image generation model in 2025 and deliberately left unfixed as a counterexample: its text was garbled and its years jumped from 1994 to 1995 and back to 1994.
Speaker Gary IllyesIn Day 2, 13:50 · Using images to your advantage and Engaging Search users with videosEvidence transcript
Sites that use AI-generated images or videos should make sure they work for users, check them for hallucinations and regenerate them where needed.
Speaker Gary IllyesIn Day 2, 13:50 · Using images to your advantage and Engaging Search users with videosEvidence transcript
Used byrequirements DEV-IMG-08, DEV-SPM-05
Google's image metadata guide says Google Images supports the IPTC Digital Source Type values for algorithmically created images, such as trainedAlgorithmicMedia, and can show C2PA details in 'About this image', such as whether an image was created or edited with AI tools.
Publisher Google Search CentralAnnotates Day 2, 13:50 · Using images to your advantage and Engaging Search users with videos
Used byrequirement DEV-IMG-08
Google's closing slide said to use AI responsibly because AI hallucinates, and, especially when creating content briefs with AI, to make sure not to add to the sea of AI slop already flooding the internet.
Speaker Gary IllyesIn Day 3, 16:00 · Wrapping all up: AI, Search, and making sense of everything.Evidence slide photo, transcript
Used byrequirement DEV-SPM-05
Google's quality talk said that in 2026 it is easy for anyone to create a large amount of commodity content, and encouraged attendees to try it once to see what the result looks like.
Speaker GoogleIn Day 3, 11:15 · How Google thinks about QualityEvidence transcript
Google's quality talk presented its AI-content examples as the speaker's personal opinion, not as the position of Google's search quality systems.
Speaker GoogleIn Day 3, 11:15 · How Google thinks about QualityEvidence transcript
Google's quality talk called AI just a tool and asked how users would know whom to trust if AI lets anyone appear to be the absolute expert in a field.
Speaker GoogleIn Day 3, 11:15 · How Google thinks about QualityEvidence transcript
Google's quality talk showed a surface-level example article (not photographed) that the speaker said Gemini could have generated, while stressing that humans have written such content for a long time too.
Speaker GoogleIn Day 3, 11:15 · How Google thinks about QualityEvidence transcript
Google's quality talk asked what would happen to the web ecosystem if everyone used AI to flood the web with surface-level content like the example it had just shown.
Speaker GoogleIn Day 3, 11:15 · How Google thinks about QualityEvidence transcript
Google's quality talk said that with LLMs anyone can write a top-10 shoe review without using the products, and questioned whether such reviews provide any real value.
Speaker GoogleIn Day 3, 11:15 · How Google thinks about QualityEvidence transcript
Google's quality talk said quality problems should be treated as quality issues, not as AI versus human content, because a lot of good AI-assisted or AI-written content exists.
“it's very important to think about these quality issues as quality issues and not AI versus human content.”
Speaker GoogleIn Day 3, 11:15 · How Google thinks about QualityEvidence transcript
Used byrequirement DEV-SPM-05
The speaker said the web keeps growing exponentially and named AI slop, low-quality AI-generated content, as one of the reasons.
Speaker GoogleIn Day 3, 12:00 · What are quality updatesEvidence transcript
Used byglossary term AI slop
Reviewing the spam slide, the speaker said link spam would probably be replaced with scaled content abuse as the spam type worth talking about today.
Speaker GoogleIn Day 3, 12:00 · What are quality updatesEvidence slide photo, transcript
Used byrequirement DEV-SPM-02
Hallucinations can happen with any AI model, and with current training methods there is no way to get rid of them.
Speaker Gary IllyesIn Day 3, 16:00 · Wrapping all up: AI, Search, and making sense of everything.Evidence transcript
Used byrequirement DEV-SPM-05glossary term Hallucination
Publishing AI output without checking it can indirectly harm a site's standing in Search, because deceptive content rated lowest by raters feeds the labels used to improve the algorithms.
Speaker Gary IllyesIn Day 3, 16:00 · Wrapping all up: AI, Search, and making sense of everything.Evidence transcript
Used byrequirement DEV-SPM-05
Google sees fake online personas as a growing type of deception, for example a professional author bio whose headshot is an AI-generated image of a non-existent person, and pages doing this get the lowest rater rating.
Speaker Gary IllyesIn Day 3, 16:00 · Wrapping all up: AI, Search, and making sense of everything.Evidence transcript
Used byrequirement DEV-SPM-05
Scaled content abuse is becoming a problem again: in the early 2000s pages were churned out with Perl or PHP scripts, and now the same is done with LLMs.
Speaker Gary IllyesIn Day 3, 16:00 · Wrapping all up: AI, Search, and making sense of everything.Evidence transcript
Used byrequirement DEV-SPM-02
The cheaper tokens become, the more AI slop is created, and Google counts AI slop as scaled content abuse.
Speaker Gary IllyesIn Day 3, 16:00 · Wrapping all up: AI, Search, and making sense of everything.Evidence transcript
Used byrequirement DEV-SPM-02glossary term AI slop
Google's Search Quality Rater Guidelines point out that the tool used to create content is not the problem, but how it was used and what for.
“it's not the tool that was used that is the problem, but rather how the tool was used and what for.”
Speaker Gary IllyesIn Day 3, 16:00 · Wrapping all up: AI, Search, and making sense of everything.Evidence transcript
Used byrequirement DEV-SPM-05
Google encouraged brainstorming and even creating content and images with AI, while staying mindful of what AI can do, because it sometimes lies to its users and that hurts a business and its Google rankings.
Speaker Gary IllyesIn Day 3, 16:00 · Wrapping all up: AI, Search, and making sense of everything.Evidence transcript
Google's Search Quality Rater Guidelines (September 2025) list fake owner or content creator profiles, such as made-up author profiles with AI-generated images, as deception, and say pages using deception of any type should be rated Lowest.
Publisher Google Search Quality Rater Guidelines (PDF, 11 September 2025)Annotates Day 3, 16:00 · Wrapping all up: AI, Search, and making sense of everything.
Used byrequirement DEV-SPM-05
Google updated its generative AI content guidance on 1 October 2026 with information from the Search Quality Rater Guidelines, to keep its documentation in sync with the presentations used at its developer events.
Publisher Google Search CentralAnnotates Day 3, 16:00 · Wrapping all up: AI, Search, and making sense of everything.
For editorial teams, the test for an AI-assisted article is the same as for any article: whether it adds effort, originality, skill and accuracy beyond what an LLM would answer directly; disclosure of AI use alone neither saves nor sinks a page.
Author Ibrahim AnjroAnnotates Day 3, 11:15 · How Google thinks about Quality
Weight spam audits toward scaled content abuse: a Googler said that type would replace link spam on the slide, so review mass-produced pages, including AI-generated ones, before older link-building patterns.
Author Ibrahim AnjroAnnotates Day 3, 12:00 · What are quality updates
Used byrequirement DEV-SPM-02
Google said the more important documents in its index naturally tend to be documents created by humans.
Google's answer was that ML-based ranking systems are trained on content written by humans for humans, so they understand and promote natural content better.
Google said the natural content its ranking algorithms understand and promote better includes content created by humans or at least edited and reviewed by them.
Google's answer was that ML-based ranking systems are trained on content written by humans for humans, so they understand and promote natural content better.
Treat machine translation as a first draft: have a native speaker review it and adapt dates, calendars and units before publishing, because unreviewed bulk translation that adds little value can also fall under Google's scaled content abuse policy.
The answer is not a claim that Google detects AI text. It says ranking favours text that reads as natural to people. The risk with AI content is scale without value, which falls under Google's scaled content abuse policy, not the tool itself.
Reviewing the spam slide, the speaker said link spam would probably be replaced with scaled content abuse as the spam type worth talking about today.
Google's quality talk listed recent updates to Google's spam policies: back button hijacking, scaled content abuse and manipulating AI responses.
Hallucinations can happen with any AI model, and with current training methods there is no way to get rid of them.
Google's guidance on generative AI content warns that AI output can contain hallucinations and says AI-generated metadata, including structured data and image alt text, should be fact-checked before publishing and the markup validated.
Scaled content abuse is becoming a problem again: in the early 2000s pages were churned out with Perl or PHP scripts, and now the same is done with LLMs.
Reviewing the spam slide, the speaker said link spam would probably be replaced with scaled content abuse as the spam type worth talking about today.
The cheaper tokens become, the more AI slop is created, and Google counts AI slop as scaled content abuse.
Google's spam policies define scaled content abuse as generating many pages mainly to manipulate rankings, with little or no value to users, no matter how they are created, and list automated translating of scraped content among the examples.
Google's Search Quality Rater Guidelines point out that the tool used to create content is not the problem, but how it was used and what for.
Google's spam policies define scaled content abuse as generating many pages mainly to manipulate rankings, with little or no value to users, no matter how they are created, and list automated translating of scraped content among the examples.
Google's closing slide said to use AI responsibly because AI hallucinates, and, especially when creating content briefs with AI, to make sure not to add to the sea of AI slop already flooding the internet.
Sites that use AI-generated images or videos should make sure they work for users, check them for hallucinations and regenerate them where needed.
Google said it is not really trying to tell AI-written from human-written content, because it cares more about the quality of content than about how it was created.
Explaining the principle of incentivising high-quality content, Google said the content may be created by humans or by AI: what matters is that it is high quality and made for users, not for search.
Google's long-standing advice to write for people and give them what they want has become true in practice because Googlebot has become more and more human, a community speaker argued.
Google's answer was that ML-based ranking systems are trained on content written by humans for humans, so they understand and promote natural content better.
Google's quality talk said quality problems should be treated as quality issues, not as AI versus human content, because a lot of good AI-assisted or AI-written content exists.
Google said it is not really trying to tell AI-written from human-written content, because it cares more about the quality of content than about how it was created.
Google's Search Quality Rater Guidelines point out that the tool used to create content is not the problem, but how it was used and what for.
Google's quality talk said quality problems should be treated as quality issues, not as AI versus human content, because a lot of good AI-assisted or AI-written content exists.
Publish AI-generated text, images and markup only after human review, and show only real authors
Rests on 11 claims, 9 of them in this topic
Do not generate large sets of low-value pages from templates, feeds or LLM output
Rests on 7 claims, 4 of them in this topic
Keep the IPTC and C2PA metadata that marks AI-generated images
Rests on 4 claims, 3 of them in this topic