Every internal link is a relationship statement. Each one should assert a clear connection: cause/effect, overview/deep-dive, problem/solution that helps retrieval systems understand how your content pieces relate. Use descriptive anchor text that tells users and machines what to expect on the destination page. Structure your site as hub-spoke clusters where authority flows intentionally and every page you care about has at least one internal link pointing to it.
~70% of websites have orphan pages with no inbound links. Those pages are invisible to crawlers, ineligible for AI Overviews, and excluded from retrieval-based answers. AI systems can only cite content they can find and contextualize.
Key Takeaways:
- Internal links are prerequisite to AI retrieval (unindexed pages can’t be cited)
- Anchor text functions as a compact description of the destination page
- Contextual links carry more semantic weight than navigation-only links
- Structure sites as hub-spoke clusters with one URL per intent
What Signals Do Internal Links Send to Search Systems?
Internal links communicate four distinct signals to search systems:
Discovery: Google uses links to find new pages. Without crawlable links, pages may never be indexed. Unindexed pages are ineligible for AI Overviews, ChatGPT search features, or any retrieval-based citation.
Semantic context: Context comes from both anchor text and the linking page’s topic. A link from a topically related page carries more meaning than one from an unrelated page.
Importance weighting: Google’s Reasonable Surfer patent assigns different weight to links based on click probability. Links in body content, above the fold, with descriptive anchor text pass more weight than footer or sidebar links with generic text.
Authority distribution: When a page earns external backlinks, internal links determine how that authority flows across your site. Pages with more internal links from authoritative pages tend to rank higher and become retrieval candidates more often.
Understanding these signals is one thing. Applying them through site architecture is another.
How Should You Structure Your Site for AI Retrieval?
The hub-spoke (pillar-cluster) model maps naturally to how AI systems retrieve content:
Hub pages serve as canonical answers to parent intents. They link down to detailed subpages covering specific facets. In AI retrieval, hubs often become the “retrieval representative” when multiple pages cover similar ground.
Spoke pages cover sub-intents in depth. They link back to the hub and across to sibling spokes where context overlaps. When AI features perform query fan-out (searching multiple related queries), well-linked spokes increase your chances of matching at least one sub-query.
Support content (FAQs, glossaries, checklists) fills gaps. These pages answer edge-case questions and provide extractable answer capsules that AI systems can cite directly.
The rule: one URL per intent. Links flow down to spokes, across to siblings where relevant, and back up to the hub. Avoid creating loops that dilute topical focus.
Why Isolated Pages Underperform
~70% of websites have pages with no inbound internal links. These orphan pages are invisible to crawlers, waste crawl budget, and receive no authority distribution. Even if indexed through sitemaps, orphan pages generally rank poorly and are unlikely to surface as AI citation candidates.
Google recommends that “every page you care about” should have at least one internal link from another page.
Structure gets pages discovered, and Anchor text determines how they’re understood.
How Should You Choose Anchor Text?
Anchor text functions like a compact, query-like description of the destination page. Google’s 2003 anchor text patent describes it as “highly likely to be a precise summary/description of the linked document.” Post-BERT, algorithms also consider text surrounding the anchor, treating the full sentence as the semantic unit.
The Selection Framework
Start with the destination page’s unique concept: Choose the shortest phrase that uniquely identifies the page’s main entity or task. “Internal linking audit checklist” tells users and machines exactly what to expect.
Add a disambiguator when needed: If your site has multiple similar pages, use head term + qualifier: “internal linking strategy” vs “internal linking audit checklist.”
Let surrounding text carry secondary meaning: Google explicitly states the words before and after the link matter. Write anchor text that fits naturally in the sentence; don’t force keywords.
Match anchor to destination intent: Use action phrases for how-to pages (“set up DMARC records”), comparison phrases for comparison pages (“compare X vs Y pricing”), and concept phrases for definition pages (“what harmonic centrality measures”).
Anchor Distribution Guidelines
| Anchor Type | Target Range | Role |
| Descriptive / Semantic | 50-60% | Provides context for AI retrieval |
| Branded | ~20% | Builds entity recognition |
| Exact Match | 10-15% | Signals core topic (use sparingly) |
| Naked URL | <5% | Low value but looks natural in citations |
| Generic (“click here”) | 0% | Avoid entirely |
Google warns against cramming keywords into anchor text. If 80%+ of internal links use the same exact-match phrase, it triggers spam signals and creates embedding redundancy for AI systems.
What Mistakes Break AI Visibility?
Non-crawlable link formats
Google states it can “generally” crawl links when they are <a> elements with an href. Script-event links, JavaScript-only navigation, and links without href attributes may not be parsed. If discovery fails, indexing fails, and AI citation becomes impossible.
Orphan pages
Pages without internal links contradict Google’s recommendation and miss authority distribution. AI systems can’t cite what they can’t find or contextualize.
Keyword-stuffed anchors
Google ties keyword stuffing to spam policy violations. Beyond penalties, repetitive anchors create embedding redundancy, the AI sees the same signal repeatedly without new relationship information.
Navigation-only linking
Relying solely on menus, breadcrumbs, and footer links is insufficient. The Reasonable Surfer model assigns higher weight to contextual in-body links. Navigation provides hierarchy; contextual links provide semantic relationships.
Excessive inline links
Analysis of ~2M sessions found that cluttered formatting. including excessive inline links near core answers, correlated with lower AI citation rates. When answers are interrupted by dense link clusters, extraction becomes messier.
A practical guardrail: no more than three contextual links per 300 words of body content.
What Are Common Misconceptions About Internal Linking?
It does not provide mechanical PageRank sculpting: Google has called internal PageRank sculpting with nofollow “a waste of time.” The nofollow-based strategy has been dead since 2009. Modern “sculpting” is about intentional architecture, not tag manipulation.
It does not guarantee AI citations: Google states there are no special optimizations required for AI Overviews beyond standard SEO best practices. Eligibility (being indexed and snippet-eligible) is necessary but not sufficient. AI features use query fan-out and select from varying sets of sources.
It does not replace crawlable architecture with sitemaps: Google explicitly states sitemap submission should not substitute for crawlable link architecture. Crawlers typically won’t use on-site search boxes or pulldown navigation to discover content.
More links does not mean better: Excessive cross-linking creates noise. If pathways are inconsistent or incomplete, retrieval models struggle to select the right content. Link where context genuinely overlaps.
Internal linking for AI search is fundamentally about making your content discoverable, interpretable, and selectable.
- Crawlers need paths to find pages.
- Retrieval systems need semantic signals to understand relationships.
Both depend on intentional architecture and descriptive anchor text, not keyword manipulation or link volume.
If your content is indexed but still not appearing in AI answers, the gap is usually visibility, not structure. ReSO helps you understand where your brand is (and isn’t) being cited across AI systems, which prompts trigger your presence, and where citation opportunities are missing.
Book a call with ReSO to analyze your AI share of voice, identify citation gaps, and understand how your content actually performs across ChatGPT, Perplexity, and AI Overviews.
Frequently Asked Questions
1. Do internal links directly affect AI Overview inclusion?
Google confirms AI Overviews use query fan-out and identify supporting pages while generating responses. Internal links affect this indirectly: they determine which pages get indexed, how authority distributes across your site, and which page becomes the “retrieval representative” when multiple pages cover similar topics. No direct link-graph boost has been confirmed, but the prerequisite effects are substantial.
2. Should anchor text match the destination page’s target keyword?
Exact-match anchors work for linking to canonical definition pages for that specific concept. Beyond that, descriptive phrases that fit naturally in sentences perform better. Google warns against forcing keywords into anchor text. The goal is helping users and machines understand what to expect on the other side of the link.
3. How many internal links should a page have?
Google states there is no ideal number and warns that excessive adjacent links reduce clarity and surrounding context. The practical constraint is maintaining semantic coherence. If a link doesn’t assert a meaningful relationship (cause/effect, overview/deep-dive, problem/solution), it probably shouldn’t exist.
4. Does image alt text count as anchor text?
Yes. For linked images, alt text functions as anchor text. Google has confirmed this explicitly. Blank alt attributes on linked images reduce the semantic signal that link provides.
