Google’s September 2026 spam update has reportedly finished rolling out. Coverage has also highlighted the need to fact-check AI-generated content before publication and warnings against false author information. Alongside these developments concerning search quality, there is debate over how to evaluate SEO budgets, including spending on AI search.
Using AI to Write—and Taking Responsibility for What You Publish
Amid discussion of this update, some have suggested that Google may be issuing more “manual actions,” penalties imposed at the discretion of its staff. However, neither the scale of any increase nor a causal link to the spam update has been confirmed. The completion of the update and the reasons individual sites may have been penalized should be considered separately.
In addition to using AI to detect spam, there has been discussion of having people verify the accuracy of AI-generated content and avoiding author information that could mislead readers. The practical challenge here is not just how content is created, but also the review process before publication. There is no basis for interpreting this as meaning that content is penalized simply because it was created with AI.
For corporate communications, too, the budget for producing more content is separate from the budget for verifying it. Improving production efficiency alone does not put systems in place to prevent inaccurate product information or problems with author attribution.
Renaming SEO as GEO Doesn’t Change the Fundamentals
SEO is the practice of making information easier to find through search. GEO, by contrast, refers to efforts to get information featured in generative AI responses and similar results. Although the terms differ, the two overlap in areas such as making pages accessible to search systems and organizing product information.
Google has said that the basic requirements of traditional SEO remain relevant to its AI search features, and that no special technical optimization is required solely for those features. However, this cannot be assumed to apply to every AI service.
When considering budgets, the first step should be to identify work that overlaps. Adding tools to measure visibility in AI while leaving outdated product information and pages that cannot be crawled in place will not fix problems with the underlying information infrastructure.
Separate Investments That Drive Growth From Those That Protect What You Have
Investment goals can be grouped into four categories: increasing sales, protecting existing sales, maintaining information infrastructure, and testing uncertain outcomes. It makes little sense to measure improvements to product pages that drive purchases and efforts to preserve search traffic during a site migration using the same metrics.
For example, migration work is intended less to generate new sales than to reduce the risk of users being unable to reach existing pages. Judging it a failure because traffic did not increase would miss the point. For product information maintenance, reducing update errors and duplicated work can also count as results.
Set a Decision Deadline for AI Search Experiments
Before making new investments in AI search, companies need to decide what they want to find out. The approach should define a narrow set of questions to test—such as whether a company’s products are described accurately or which pages are cited—and use the results to decide whether to continue or stop. Rather than combining SEO and GEO across the board, distinguishing shared infrastructure work from experiments specific to each service can make overlapping costs easier to identify.
That said, traditional SEO initiatives should not be retained unconditionally either. Whether it means producing more articles that cannot recoup their costs or continuing AI experiments with unclear objectives, companies need to compare those choices with the option of reallocating the budget to other uses.
The number of mentions in AI answers can serve as a measure of visibility, but it does not by itself prove a contribution to purchases. Search traffic, sales, information accuracy, and operating costs each measure different outcomes.
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