Wedge Visibility

Definition

A wedge is a brand's single sharpest positioning claim — the one differentiator that distinguishes it from competitors. Stripe's wedge is "developer-first." Linear's wedge is "fast, keyboard-driven." Notion's wedge is "flexible knowledge base." Slack's was "stop emailing each other."

Wedge visibility is the measured rate at which AI search engines surface that differentiator when the brand appears in their answer. It's not the same as brand surface rate (whether the brand is named at all). It's the second-order question: when the brand IS named, does the model also surface the brand's signature positioning?

The Linear audit (2026-05-30) measured wedge visibility for the keyboard/speed differentiator across 47 mentioned cells. The result: 77% of Linear-named cells also surfaced the keyboard/speed wedge in their answer body. That number is wedge visibility for Linear's signature claim.

Why it matters

Brand surface rate and wedge visibility move independently. A brand can have 100% surface ("ChatGPT names us in every answer") with 0% wedge visibility ("but the answers never mention what makes us different"). In that case the brand is being mentioned as a generic category member, not as the differentiated alternative the marketing team is trying to position.

The diagnostic split is actionable. Brands with high surface + low wedge visibility need content investments in what they're different at. Brands with low surface + high wedge visibility (rarer) need content investments in being included in the conversation at all. Brands with high surface + high wedge visibility are positioning correctly. Brands with low both need a category-level repositioning, not a content investment.

How AI engines decide whether to include the wedge

The mechanism that drives wedge visibility is whether the indexed source content connects the brand to the differentiator in citation-likely phrasings. If the LLM's retrieved documents say "Linear is a project management tool that emphasizes speed and keyboard navigation," the wedge gets surfaced. If the retrieved documents say "Linear, project management software for engineering teams," the brand surfaces but the wedge doesn't.

Two practical implications:

1. Linear-style brands with high-engagement third-party coverage (HN threads, Twitter threads, technical reviews) tend to have strong wedge visibility because those sources cite the differentiator directly in their headlines. Brands relying on marketing-page content tend to have weaker wedge visibility because marketing pages bury the differentiator behind feature lists.
2. Brand newsroom content surfaces the wedge weaker than third-party content surfaces it. A Linear blog post that says "we're fast" is one data point. Forty HN comments that say "Linear is fast" are forty calibration signals to the model. The latter dominates.

Query-type variance

Wedge visibility varies substantially by query type. The Linear R2 data showed:

• Category queries (e.g., "best modern issue tracker") — 93% wedge visibility
• Comparison queries (e.g., "Linear vs Jira") — 93% wedge visibility
• Branded queries (e.g., "is Linear worth it") — 50% wedge visibility (drops when pricing context suppresses positioning)
• JTBD migration queries (e.g., "how do I migrate from Jira to a faster issue tracker") — 80% wedge visibility (Round 2 reversal from Round 1's 0%)
• JTBD workflow queries (e.g., "best sprint planning workflow") — 0% wedge visibility

The variance is meaningful: content investments should target the query types where the wedge is invisible, not the ones where it already lands.

Common pitfalls

• Conflating surface rate with wedge visibility. A 90% surface rate with 30% wedge visibility is a more concerning brand state than 70% surface rate with 80% wedge visibility — the second brand is better-positioned even though it shows up less often.
• Measuring wedge visibility on the wrong differentiator. The wedge has to be the one claim the brand actually leads with externally. Measuring "Linear has integrations" when Linear leads with "Linear is fast" produces a measurement of the wrong thing.
• Treating low wedge visibility as a content-writing problem. It's usually a content-distribution problem. If your blog post says "we're fast" but no third-party citations carry that claim, the model never sees enough signal to surface it.