How To Use AI Content Without Getting Burned🔥

The temptation to use AI to produce large volumes of content is hard to ignore. After all, why spend weeks researching, writing, and editing content when you can generate hundreds of articles in an afternoon? But just because you can publish content at scale doesn't mean you should. While Google doesn't explicitly prohibit AI-generated content, its spam policies make it clear that producing large amounts of low-value content primarily to manipulate search rankings can put your entire site at risk. And with each new spam update, the consequences of cutting corners could become increasingly expensive. The good news is that AI can still be a valuable part of your content strategy, provided you understand where to draw the line between efficiency and abuse. The trick isn't avoiding AI. It's knowing how to use it without turning today's productivity gains into tomorrow's SEO disaster.

Google's position on AI-generated content has remained relatively consistent since 2023: Using AI to create content is not inherently a violation of its spam policies, nor does it automatically prevent content from ranking well. Instead, Google evaluates content based on its quality, originality, usefulness, and purpose. However, Google's March 2024 introduction of its scaled content abuse policy made it clear that mass-producing low-value content primarily to manipulate search rankings is considered spam, regardless of whether it was written by AI or humans. Google continues to recommend producing helpful, people-first content that demonstrates experience, expertise, authoritativeness, and trustworthiness (E-E-A-T). This includes providing original research, first-hand knowledge, unique perspectives, and meaningful information that readers cannot easily find elsewhere. Simply using AI to rewrite existing information or expand keyword coverage is not a sustainable strategy, even when the resulting content is factually accurate.

More recently, Google has become increasingly explicit about these expectations. In May 2026, Google published additional guidance emphasizing the importance of original, non-commodity content that provides value beyond information already widely available online. Then, on October 1, 2026, Google updated its generative AI content guidance to reference specific sections of its Search Quality Rater Guidelines concerning scaled content abuse and content exhibiting little effort, originality, or added value. While these updates do not introduce new ranking factors or fundamentally change Google's policies, they reinforce a growing emphasis on content quality over production volume. Google also recommends manually reviewing AI-generated content for factual accuracy, misleading information, and fabricated citations before publication. The takeaway is that Google isn't necessarily concerned with whether AI helped create your content. It's concerned with whether that content deserves to exist in the first place.

For over 10 years, Google has made a reasonable effort to steer content creators toward higher standards of quality through its E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) framework and, more recently, its emphasis on creating unique, non-commodity content. Both recommendations have been helpful in shifting the conversation away from keyword stuffing, content volume, and other outdated SEO tactics toward producing content that offers genuine value to readers. However, while the underlying principles are sound, they have arguably created more questions than answers for SEO professionals responsible for putting them into practice. What exactly qualifies as sufficient expertise? How does Google distinguish genuinely original content from a well-written summary of existing information? Where is the line between commodity content and something that provides meaningful value? And perhaps most importantly, how do Google's algorithms actually recognize and reward these qualities at scale? Without clear, measurable standards, SEO teams are left interpreting broad philosophical guidance and translating it into actionable strategies, often with little evidence that their interpretations align with how Google's ranking systems actually work.

Instead of getting lost in different interpretations of what Google is saying, your team needs to start asking the right questions in these four areas: intent, usefulness, originality, and quality.

Before creating any piece of content, your team should be able to clearly articulate why it needs to exist. This goes beyond identifying target keywords or understanding search intent. It means establishing a legitimate purpose that aligns with both the needs of your audience and the objectives of your business. Are you trying to educate potential customers, help existing customers solve a problem, demonstrate expertise, or provide information that supports a purchasing decision? These are all reasonable objectives, but they should be established before production begins. Publishing content simply because a keyword has search volume, or because a competitor has written about the same topic, is not a sufficient strategy.

This distinction becomes especially important when AI makes it possible to produce hundreds of articles with very little effort. Just because you can publish content on a particular topic doesn't mean you should. Google has repeatedly emphasized the importance of creating content primarily for people rather than search engines. Your team should be able to justify every piece of content independently of its potential to generate organic traffic. If the only reason a page exists is to capture search impressions, you should probably reconsider publishing it.

A piece of content can be factually accurate, well-written, and properly optimized while still being completely useless to its intended audience. This is one of the biggest problems with commodity content, particularly when it is generated by AI. Simply answering a question is not necessarily the same thing as helping someone. Your content should provide enough practical information, context, or guidance for visitors to accomplish something meaningful. Depending on the topic, that might mean making an informed decision, solving a technical problem, understanding a complicated issue, or learning how to complete a particular task.

This is also where your team needs to distinguish between satisfying a search query and satisfying an actual person. A visitor searching for advice on choosing a software platform probably doesn't need another generic article explaining what the software does. They may need comparisons, pricing considerations, implementation challenges, or examples of how the software performs in specific environments. Google explicitly encourages publishers to consider whether readers leave their content feeling that they have learned enough to accomplish their goals. Your team should adopt that same standard. If visitors still need to return to Google to find the information your article should have provided, there's a good chance your content hasn't done its job.

Originality is arguably one of the most difficult standards to maintain in an environment where AI can instantly generate reasonably competent content about almost any subject. Large language models are particularly good at synthesizing existing information, but that strength can also become a weakness when the resulting content offers little beyond what has already been published. Rewriting the top five search results into a new article, even when the language and structure are completely different, doesn't necessarily produce anything original. The real question your team should be asking is what new information, perspective, or experience you are contributing to the conversation.

Fortunately, originality doesn't always require groundbreaking research or an entirely new idea. It can come from firsthand experience, original data, expert interviews, unique examples, or even a thoughtful analysis that challenges conventional assumptions. A technical tutorial based on an actual implementation can offer insights that a generic explanation cannot. Likewise, a product comparison informed by real testing can provide value that an AI-generated summary of product specifications will struggle to match. AI can still play a useful role in researching, organizing, and refining these contributions, but it shouldn't replace the work that makes them original. Before publishing, your team should be able to identify exactly what makes the content worth reading instead of the dozens of similar articles already available.

Quality is often treated as a subjective concept, but that doesn't mean your team can't establish measurable editorial standards. Start with the fundamentals: factual accuracy, appropriate topic depth, clear language, logical organization, and formatting that makes information easy to understand. Content should be comprehensive enough to satisfy its intended purpose without introducing unnecessary complexity or padding simply to reach an arbitrary word count. A short, direct answer can be considerably better than a 3,000-word article that buries the same information beneath generic explanations. Your editorial standards should reflect the needs of your audience rather than assumptions about what search engines prefer.

Your team should also establish a consistent review process that applies regardless of whether content was written by a person or generated with AI. This includes fact-checking claims, verifying sources, eliminating repetition, evaluating readability, and ensuring that the language reflects the expertise and credibility of your organization. For technical or specialized topics, qualified subject matter experts should be involved in reviewing accuracy and completeness. Google's own guidance on helpful, reliable content encourages publishers to evaluate originality, accuracy, effort, and the overall experience their content provides. None of these standards guarantees higher rankings, but together they provide a much more practical framework for evaluating content than trying to reverse-engineer an algorithm's interpretation of quality.

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Ultimately, the biggest risk associated with AI-generated content isn't the technology itself, but the temptation to prioritize production volume over meaningful value. AI has made content creation faster, cheaper, and more accessible than ever before, but none of those advantages automatically translate into better content or improved search visibility. If anything, the growing volume of AI-generated information makes originality, expertise, and usefulness even more important. Publishing hundreds of mediocre articles might look like progress on a content calendar, but it can also create technical debt, dilute your site's overall value, and expose your organization to unnecessary SEO risks.

The best way to avoid getting burned by Google's next spam update isn't to chase every new interpretation of E-E-A-T or attempt to reverse-engineer what qualifies as non-commodity content. Instead, establish clear editorial standards centered on intent, usefulness, originality, and quality, and apply them consistently to everything your team publishes. Use AI to support research, streamline workflows, and improve the writing process, but keep human judgment and accountability at the center of your strategy. The goal shouldn't be to publish more content with less effort. It should be to use AI to produce better content with the resources you already have.

Jev:

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One more thing: AI is only as good as its operator, and if you are reading this newsletter, you’re better than most!

Till next time,

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