Google has admitted that Search Console reporting cannot currently keep up with AI search mechanics, specifically regarding “block flattening.” (block flattening: treating an entire AI Overview box as a single block and assigning every link inside it the same position rank.) This method treats entire AI-generated overviews as a single block, assigning every link inside the box a Position 1 rank regardless of its actual visibility or placement. (AI Overview: the AI-generated search feature that summarizes information and includes cited links.) This reporting gap matters to any SEO practitioner or growth engineer who needs to explain to stakeholders why a “Position 1” ranking in an AI Overview does not correlate with a proportional spike in traffic.
Is this for you?
If you report AI visibility to stakeholders, Google Search Console position rankings are currently unreliable due to block flattening and do not guarantee traffic.
- If you are an SEO consultant, you should stop using position as a primary success metric for AI features.
- If you are a growth engineer, you should treat AI visibility as a binary brand citation status instead of a ranking.
- Skip this if you are a beginner blogger, as you should look for a general guide on generative features instead.
What you can do now
- If you see a position of 1 in GSC for a query with an AI Overview, treat it as a brand citation confirmation.
- Monitor conversion volume for AIO-influenced keywords to determine actual impact on the bottom line.
- Check the live layout of your results directly to verify if links are hidden behind a Show More button.
Key figures
- Block Flattening effect
- Every link inside an AI Overview box is reported as Position 1
- AIO CTR change
- Fell by 61% according to Search Engine Journal
- Organic click impact
- Cut by around 38% when an AI Overview is present
- AIO search frequency
- Appears on 21% of searches
Google admits Search Console cannot keep up with AI search mechanics
Google Search Advocate John Mueller acknowledged on Reddit that tracking traditional rankings for AI features is “hard to do in a way that makes it useful.” Because the 1-to-10 ranking scale was designed for a linear list of links, it fails to capture the non-linear layout of modern search results. For many digital marketing leaders, this means the historical reliability of the “Average Position” metric is eroding as AI becomes a primary interface for users.
This analysis is specifically for SEO consultants and growth engineers who are responsible for reporting AI visibility to non-technical stakeholders. If you already rely on Google Search Console to prove your ROI on AI features, you must stop using position as a primary success metric. Conversely, if you are a beginner blogger just looking to understand what an AI Overview is, you should instead look for a general guide on Google’s generative features.
The mechanics of block flattening in AI Overviews
Block flattening —Google’s method of treating an entire AI Overview box as a single block, assigning every link inside it the same position rank (e.g., position 1)—creates a significant reporting discrepancy. When Google identifies an AI Overview (AIO) —the AI-generated search feature that summarizes information and includes cited links—it views the entire container as a single unit. Because these boxes typically appear at the very top of the page, every link included in that container is recorded as Position 1 in your reports.
The reporting database treats these two placements as identical
The data suggests you have top-tier visibility. A user might never have noticed your site at all. This happens because the reporting system cannot distinguish between a link card appearing in the first sentence of the summary and a plain link tucked deep inside the text. The reporting database treats these two placements as identical, even though user behavior differs wildly between them.
The contradiction between default and interactive impressions
Standard impression rules conflict with interactive buttons like “Show More” in the AI Overview. Under standard rules, an impression is logged the moment a search result loads on the page. However, Google makes an exception for anything that requires a click to open. This means links hidden behind a “Show More” toggle do not count as an impression until a user actively interacts with the UI.
Links in the main text of the AIO get "full credit" for views they
This creates a paradox for your reporting. Links in the main text of the AIO get “full credit” for views they may never have had, while useful links hidden behind a toggle look like they do not exist until clicked. If a link is buried under a toggle, does it really count as a top ranking? Google’s current reporting says yes, but user behavior says no. This distinction is vital for technical SEOs who need to understand why certain AIO inclusions aren’t driving the expected engagement.
Quantifying the impact: The 38% click drop and 61% CTR fall
The shift in user behavior is quantifiable. A field study reported by Search Engine Journal found that having an AI Overview at the top of the page cut clicks to standard organic results by around 38%. (organic results: the standard search results provided by a search engine that are not paid advertisements.) This occurs because users often find their answers within the summary and move on without clicking further. Furthermore, Search Engine Journal reported that click-through rates (CTR) for cited websites fell by 61% in certain contexts.
The math is skewed by the reporting logic. Because Search Console records so many automatic impressions for the AIO box, the overall CTR calculation can be distorted. This is particularly prevalent for informational searches, where research shows AI Overviews appear on 21% of searches and appear even more frequently when a user asks a question. In these scenarios, Google is answering the query directly rather than pointing users to a website.
Moving from ranking metrics to outcome-based reporting
Practitioners must move away from the 1-to-10 ranking scale for AI visibility and focus on measurable outcomes. Because block flattening makes position a “false positive,” you should instead treat AI visibility as a binary “brand citation” status. If your brand is cited in the AI Overview, it is a win for authority, but it should not be sold to clients as a guaranteed traffic driver.
For those needing to know exactly how a page looks to a searcher
The focus should shift to outcomes that cannot be distorted by how an impression is logged. This includes tracking actual site visits, genuine inquiries, and conversion volume. For those needing to know exactly how a page looks to a searcher, checking the live layout directly or using automated tools that inspect the live screen will provide more accuracy than Google’s flattened data. This holds as long as Google continues to treat the entire AIO box as a single block; if Google updates the Search Console to report individual link positions within the AIO, the 1-to-10 ranking scale may regain some utility.
The “Position 1” Illusion: A worked example for Eco-Gear
Suppose a company, “Eco-Gear,” wants to track its visibility for the query “best eco-friendly tents.” Eco-Gear’s link appears inside an AI Overview. Google Search Console reports a position of 1. However, the link is at the bottom of the summary, hidden behind a “Show More” button. The report shows “Top Ranking,” but the actual clicks are nearly zero because the link wasn’t visible on initial load.
Google Search Console
If the link was in the first sentence of the AIO, the outcome would change. Google Search Console would still report “Position 1,” but the actual clicks would be significantly higher. Relying on GSC position scores in this case leads to a false sense of success where content is cited but not clicked; ignoring AIO presence entirely risks missing out on the 21% of searches where AI provides the primary answer.
What this replaces for organic reporting
This shift replaces the 1-to-10 ranking scale as the primary metric for AI visibility. The new approach is better because it provides a more accurate representation of user behavior and brand impact rather than a distorted mathematical average. By moving to outcome-based reporting, you avoid the trap of over-investing in content that is cited but buried in a “Show More” dropdown.
Practical applications for AI reporting
| Use Case | Who it serves |
|---|---|
| Identify “false positive” high rankings in GSC reports | SEO Audit Teams |
| Create a new KPI for “Brand Citation Status” | Content Strategists |
| Explain click-through rate discrepancies to stakeholders | Account Managers |
| Audit the impact of “Show More” buttons on AIO visibility | Technical SEOs |
Next steps for practitioners
Stop using GSC position as a proxy for AIO success and start using citation and conversion data. If you see a position of 1 in GSC for a query where an AI Overview is present, treat this as a brand citation confirmation rather than a guarantee of high-visibility placement. Monitor the conversion volume of AIO-influenced keywords to determine the true impact of these search features on your bottom line.
Reported by Search Engine Journal on 24 September 2026: Search Console Uses Block Flattening For AIOs, Forget Position & Focus On Outcomes via @sejournal, @TaylorDanRW.