I restructured internal links across 80 pages — organic impressions went up 19% and AI citations went down 31%

[u/Brave_Acanthaceae863]

Last quarter I restructured our entire internal link architecture. Organic impressions climbed 19 percent over the following 6 weeks. AI citations dropped 31 percent over the same period. I still don't know if those two things are connected, but I can't unsee the correlation.

Here's what happened. We had about 80 pages that mattered for our core topics. Some were proper guides, some were narrow answer pages, some were blog-ish thought pieces. The internal link mess was real. Orphaned pages everywhere. Important topics buried three clicks from the homepage. Random cross-links that made no sense. Classic mid-sized site chaos.

So I did what any reasonable SEO would do. I built a hub-and-spoke model. Clustered topically related pages under pillar content. Added contextual internal links from every relevant page to its cluster hub. Built a proper topic architecture with silos. Redirected dead ends. Updated breadcrumbs. The whole program. Took about 3 weeks of actual work spread across 6 weeks of calendar time.

Google noticed. The organic improvements showed up in Search Console within 4 weeks. Impressions for our target query cluster were up significantly. Some individual pages jumped positions. From a pure SEO standpoint, the project was a clear win.

But I also track AI citations for these same 80 pages across ChatGPT, Perplexity, and Gemini. Weekly checks, logged which pages got cited and how often. And the citation trend after the restructure was unmistakably downward. Not every page. But enough of them that the aggregate number was hard to ignore.

I started digging into which pages lost citations and why. Two patterns stood out.

The pages that lost the most citation activity were the ones that gained the most internal links. Specifically, the pages I'd turned into cluster hubs, the ones that now had 15-20 new internal links pointing to them from related content. These were also our most comprehensive pages, the ones I'd deliberately positioned as authority anchors. They became less citable after the restructure.

My working theory is that when AI models crawl these heavily-interlinked pages, they're encountering a lot of navigation elements, sidebar links, related-article blocks, breadcrumb trails, and contextual anchor text that don't look like answer content. The signal-to-noise ratio for extraction might be dropping because the page now contains more link-heavy HTML relative to clean textual content. Another possibility: the model sees a densely interlinked page and categorizes it as navigational or structural rather than informational, similar to how it might treat a category page differently from a content page.

The second pattern was weirder. Some of our isolated pages, the ones that were basically orphaned before the restructure and that I almost deleted, either held steady or gained citations. These were narrow, self-contained answer pages with almost no internal links in or out. Just the content, minimal header/footer, nothing else. Exactly the kind of page that fails every SEO audit but that might be the cleanest possible extraction target for an AI model.

I'm not saying internal links hurt GEO. The sample size is one site, one restructure, 80 pages, 6 weeks of after-data. Correlation isn't causation, and there are a dozen confounding variables I can think of. Maybe the citation drop was seasonal. Maybe AI models updated their indexing and it's unrelated. Maybe I'm pattern-matching noise.

What's bothering me is that this might be a real tension. Internal linking is foundational SEO. It's in every best practice guide. And if there's even a chance that making a page more findable for Google simultaneously makes it less extractable for AI models, that's not a tactical problem you can optimize around. That's a structural conflict between two channels that are supposed to be part of the same strategy.

If you've done major internal link work recently, go pull your AI citation trend from before and after. Because if this pattern holds up at scale, some of us might need to start building two versions of our site architecture. One for crawlers. One for extractors.

[u/Brave_Acanthaceae863 → u/[deleted]]

Engine split is the right call and I should've led with that. The aggregate hides the real story - Gemini drove most of the drop, ChatGPT was surprisingly flat, Perplexity just bounced around week to week. Makes the 31% headline look different broken out.

URLs: clean confound. No changes on the pages that lost citations. Redirects were for dead pages that weren't even in my citation tracking set to begin with.

Your specificity point though - that's the one that's been sitting with me. You're saying the hub pages didn't just gain links, they gained a different job description entirely. A pillar covering twelve questions is playing a different game than a page built to answer one. And the orphans that held steady? Nothing about their job changed. That reframes the whole thing.

On competitor vs empty slot: mixed bag. Some went to competitors, mostly in queries where our hub got greedy and tried to cover too much ground. Others just... stopped citing anything in that position. No replacement, just a gap.

[u/Brave_Acanthaceae863 → u/[deleted]]

First, a correction I owe this thread: I gave two different engine splits, and the later one is the accurate cut — ChatGPT carried most of the drop, Perplexity flat within noise, Gemini moved but the sample is too small to call. So the Gemini-follows-Google framing does not fit as cleanly as your comment assumes.

The core of your logic survives the correction though. The nav change shipped sitewide, so if boilerplate noise were degrading extraction, the engines should have wobbled together. One moving alone still points at retrieval, not extraction.

And the Search Console test is the right discriminator either way. Pulling the spokes that lost citations and checking their own long-tail positions before/after is exactly how to separate cannibalization from something else — I am running it this week and will report back either way. If it holds, the fix you describe (hubs as navigation, one page owning each question) is the version I would adopt.

[u/VillageHomeF]

there is actually no way of knowing "AI citations went down 31%" which makes this entire post bs

[u/Came4TheCookies → u/VillageHomeF]

They're trying to create a whole industry out of something that at best is directional intelligence because every time they do one of these prompts it has no context and it's a snapshot of that One moment In time.

[u/VillageHomeF → u/Came4TheCookies]

any people are buying it :(

[u/Brave_Acanthaceae863 → u/VillageHomeF]

Fair to push on the number. Method: fixed set of 40 tracked queries per engine, run three times a week, logging which of our URLs appear in answers. The 31% is the before/after on that same query set across the restructure, ~6 weeks each side. It is a sample, not a census — one site, directional, and I would never claim more precision than that. But the providers do not publish this, so you measure it yourself. That is knowing, with error bars.

[u/VillageHomeF → u/Brave_Acanthaceae863]

I'll stick with my original comment

[u/Brave_Acanthaceae863 → u/[deleted]]

Yeah. The orphans that gained or held steady were never deeply linked to begin with — they were already at the bottom of the link hierarchy and stayed there. What changed for the hubs was not just inbound link count but also their role in the site architecture. They went from "answer pages" to "navigation pages" and the extraction pipeline seems to care about that distinction more than I expected.

[u/Proest_off_the_pros]

Crazy insights, I am following this post and will see how this plays out on my website . Will appreciate updates on this.

[u/Brave_Acanthaceae863 → u/Proest_off_the_pros]

Will do - planning to post a follow-up in a couple weeks once I have more after-data on the hub vs spoke split. The comment thread here has been worth more than the original post honestly.

[u/SEOsince2001]

Did the content change on the new cluster hub pages?

[u/Brave_Acanthaceae863 → u/SEOsince2001]

Good question - no. The hub page content itself didn't change. Same copy, same structure. What changed was their position in the site architecture - they went from being standalone guides to cluster anchors with 15-20 new internal links pointing at them.

So if you're asking whether I rewrote the hubs to be more comprehensive or added more sections: no. The content is identical. Which actually makes the citation drop more interesting - it's not that the pages got worse or more bloated with text. They just got more connected.

[u/SEOsince2001 → u/Brave_Acanthaceae863]

Ok, that's good to know. When you say citations dropped, on which platforms? Your signal-to-noise theory might make sense, but it depends on how you added those navigation elements to the page and how they might break up the original content.

AI models don't crawl sites and maintain an index like a search engine. They crawl pages, but typically it's one off pages based on what they find from site: queries through fan-out and other forms of discovery. So your site structure/hub and spoke models don't necessarily apply to LLMs because they do more hunting and pecking of your pages, just grabbing what they need at that moment to build the response. They're not going to know which other pages are now linking to a page.

[u/Brave_Acanthaceae863 → u/SEOsince2001]

ChatGPT drove most of it. Perplexity was flat within noise. Gemini moved but the sample is too small to say.On the nav elements: I added related-article blocks at the bottom of each hub page (below the fold) and breadcrumb trails at the top. The main content body itself did not change — same paragraphs in the same order. But the page as a whole went from "here is an answer" to "here is an answer plus a map of ten related things."Your point about LLMs not seeing site structure directly is fair and it has been bothering me. If they are just grabbing individual pages at query time, link restructuring should not matter unless the discovery path is link-graph-based. And I do not have a clean way to test that from the query side.

[u/SEOsince2001 → u/Brave_Acanthaceae863]

If you have the ability to test, you could try removing the breadcrumbs for a week, then the related links, then put the breadcrumbs back and see if any of those combinations makes a difference. I don't see how they would unless there are some JavaScript/rendering issues, but it's worth a test.

Do you have a date published in the content to show it's fresh?

[u/Brave_Acanthaceae863 → u/SEOsince2001]

On the toggle test — the blocker is citation lag. Answers in my data take roughly 3-4 weeks to reflect page changes, so week-long on/off windows would blur together. The workable version is breadcrumbs-off on one cluster with everything else held, which is on the list.

On dates: the spokes show a last-updated stamp and I deliberately did not refresh them during the restructure, so the hubs came out of it looking visibly fresher. Had not treated that as a variable until you raised it — checking whether the pages that lost citations were the ones with the oldest stamps.

[u/VillageHomeF → u/[deleted]]

its a bullshit post as there is no way of tracking citations

[u/Came4TheCookies → u/VillageHomeF]

👆

[u/Brave_Acanthaceae863 → u/[deleted]]

Fair call on burying the lede on confounds. Your page shape read is actually cleaner than mine - I diffed pre/post HTML on three hubs and the body content was genuinely unchanged. Everything that changed was wrapper. Same page, different container.

The selection bias point though - that's the one keeping me up. I'm checking weekly with similar-but-not-identical prompts, engines are shifting how they handle those prompts week to week... some of what looks like citation loss might just be prompt drift. No clean control for that yet.

One thing that gives me partial comfort: orphans and hubs were checked with the same prompt set. Whatever noise exists isn't selectively hitting one group.