Google Confirms AI-Generated Content Must Be Human Reviewed
In recent discussions about artificial intelligence, one of the most frequently asked questions concerns Google’s stance on AI-generated content and how it impacts both search quality and large language model (LLM) training. Gary Illyes of Google recently clarified the company’s position, emphasizing that while AI-generated content is not inherently a problem, its usefulness depends heavily on accuracy, originality, and human oversight. His comments shed light on how Google is approaching AI, both in terms of search indexing and in the development of AI-powered tools like AI Overviews and AI Mode.
AI Content: From “Human Created” to “Human Curated”
One of the more interesting points Illyes made was about the wording often used to describe Google’s policies. Many people assume Google requires content to be “human created.” However, Illyes noted that this isn’t an accurate description. Instead, he suggested that “human curated” better reflects Google’s perspective.
In practice, this means Google does not forbid the use of AI in content creation. What matters is whether a human editor has reviewed, verified, and refined the output before publication. AI tools can speed up the writing process, generate drafts, or provide summaries, but relying solely on raw AI output risks introducing errors, biases, and repetition into the web. Human oversight is therefore essential to maintain quality and factual accuracy.
Illyes stressed that this guidance is unlikely to change anytime soon. The company is less concerned about the method of creation and more focused on the outcome: accurate, reliable, and valuable information for users.
AI Overviews and AI Mode: Powered by Gemini Models
The conversation also touched on Google’s AI-powered features, specifically AI Overviews (AIO) and AI Mode. Both are powered by custom Gemini models, Google’s family of large language models. While Illyes did not provide extensive technical details about how these models are trained, he confirmed that they are not off-the-shelf but instead custom-built for these specific applications.
These models are designed to generate responses that draw from Google’s vast search index, grounding their answers in web-based data. Grounding, in this context, means connecting the generative model’s output to real, verifiable information from Google Search. This process helps reduce hallucinations (fabricated or inaccurate statements) and ensures that the answers users receive are based on factual sources rather than the model’s guesswork.
When asked whether AIO and AI Mode use separate indexes for grounding, Illyes clarified that they both rely on Google Search. Essentially, the Gemini model issues multiple queries to Google’s search engine, receives results, and then synthesizes them into an answer. This integration ensures that the AI system does not rely purely on pre-trained knowledge but instead checks its responses against up-to-date web data.
The Role of the Google Extended Crawler
Another point of clarification came when Kenichi Suzuki asked about the Google Extended crawler. Some publishers are concerned about whether AI features use their content for training or grounding, and whether opting out of Google Extended affects this process.
Illyes explained that the Extended crawler comes into play when content is being collected for AI model training. Grounding, on the other hand, is a separate process that doesn’t involve AI at all—it’s simply the search engine returning results for queries. If a publisher blocks Google Extended, Gemini will not be able to ground its answers using that site. This means the content won’t be part of the reference set for AI-generated answers.
In other words, there is a clear distinction between training data (used to build models) and grounding data (used in real-time to generate more accurate answers). Blocking Google Extended may protect your content from being used in training, but it also prevents it from being considered when Gemini models attempt to generate fact-based responses.
AI Content and the Risk of Training Loops
A major concern in the AI community is the risk of “model collapse” or training loops—where AI systems are trained on content that was itself generated by AI. This can lead to degraded quality, as models reinforce their own biases and mistakes over multiple generations.
Suzuki asked Illyes about this issue, specifically whether the proliferation of AI-generated content online could pollute LLM training. Illyes admitted this is a legitimate concern for AI model training, though not for Google’s search index. Search is robust enough to filter and evaluate quality signals, but AI model training must be more selective.
He emphasized that training data ideally should exclude AI-generated content, or at least ensure that the data has been reviewed and validated. Otherwise, there is a risk of feeding inaccurate or derivative information back into the system, which compounds errors over time.
Content Quality as the Core Priority
The heart of Google’s stance on AI-generated content boils down to one principle: quality matters most. Illyes reiterated that Google does not care how content is created—whether by humans, machines, or a collaboration of both—so long as it meets high standards of accuracy, originality, and usefulness.
However, he cautioned against two major pitfalls:
Duplication and Similarity: AI tools often generate content that is very similar to what already exists online. If content is “extremely similar” to existing material, it is unlikely to be valuable, and Google does not want to include it in its index. Redundancy adds little value for users and creates clutter in search results.
Inaccuracy and Bias: Even more problematic is the risk of inaccuracies. Training on flawed or misleading data introduces biases and misinformation into AI models. This not only reduces the usefulness of AI systems but also risks spreading false information at scale.
To avoid these problems, Google stresses the importance of editorial oversight. Human reviewers must check AI output for factual accuracy, originality, and readability before publishing.
Human Review: What It Really Means
Illyes expanded on what “human review” actually entails. Importantly, it is not about signaling to Google that content has been reviewed by humans. Simply writing “this article was reviewed by a human” on a webpage is meaningless as a ranking factor. Instead, human review refers to genuine editorial oversight—the process of fact-checking, verifying claims, and ensuring the content provides unique and reliable value.
This approach echoes traditional publishing standards. Just as professional editors review articles before they go to print, digital publishers using AI should apply the same rigor. The role of the human is not necessarily to write every word, but to curate and validate the AI’s work to maintain trustworthiness.
Key Takeaways for Publishers
Summarizing Gary Illyes’ comments, Google’s position on AI-generated content can be understood in a few clear points:
AI-generated content is not inherently bad. What matters is quality, originality, and factual accuracy.
Human curation is essential. AI output should be reviewed, fact-checked, and edited before publication.
Grounding ensures reliability. Gemini models for AI Overviews and AI Mode rely on Google Search for grounding, reducing the risk of hallucinations.
Blocking Google Extended has trade-offs. It prevents your content from being used in training but also excludes it from AI grounding.
Model training faces new challenges. If LLMs are trained too heavily on AI-generated content, they risk training loops and degraded performance.
Quality is the ultimate filter. Google’s algorithms prioritize accurate, unique, and useful content, regardless of whether it was created by humans or machines.
AI Generated Content
Google’s evolving stance on AI-generated content reflects the realities of a changing digital landscape. While some feared that AI output would be penalized, Illyes’ comments confirm that the issue is not the use of AI itself but the quality of the final product. AI can be a powerful tool for content creation, but it cannot replace human judgment.
For publishers, the message is clear: leverage AI for efficiency and scale, but always apply editorial oversight. By ensuring that AI-generated content is reviewed, accurate, and original, publishers can safely use AI without running afoul of Google’s guidelines. In short, AI content is welcome in Google’s ecosystem—but only when humans remain part of the process.
