In May 2025, Google brought 25 of us into a closed-door room at I/O to talk about the post-click SERP. The instruction we left with was short: create non-commoditized content.
Here’s the uncomfortable part:
For 15+ years, we never commoditized content. We commoditized the sale. We built billions of pages that took a messy human problem, targeted whatever keyword the person compressed it into, and answered with some version of “buying now is the best choice.”
We did it well for our brands, and we built a sales-first web.
Oops.
AI search is the bill for that cognitive debt finally coming due. For years, we skipped past the buyer’s real thinking and answered “buy now” instead of their actual questions.
Paying that debt off is a link building problem. Not just hyperlinks, but the links between sources, roles, risks, and decisions.
A co-citation gap analysis maps those links, showing which sources AI search trusts for each buyer role and where your content is missing from the decision.
In this piece, we’ll show you how to run it: map the sources AI search reads and cites, find the role your content doesn’t support yet, and build the asset that closes the gap.
Moving from anchor text to anchor context
We’ve been running co-citation analysis on the link graph for 15 years at Citation Labs.
In 2011, I published a six-step co-citation method for link builders: find the pages that curate a topic, count which sources they cite together, and reverse-engineer what made those pages worth citing.
What’s changed now is the unit of work: from focusing on anchor text to anchor context.
Anchor text told a search engine what a page was about.
Anchor context tells the model why that evidence belongs in a specific answer, for a specific role, at a specific point in the decision.
The work moves from describing the page to supporting the decision.
Instead of asking which pages mention a topic together, you’re asking which sources an AI assistant trusts when each buyer role asks about the same decision, and which role your content fails to support.
That missing decision support is the co-citation gap.
How to run a co-citation gap analysis by hand
A co-citation gap analysis counts what AI search reads and cites across same-phase, same-problem prompts for different buyer roles.
The overlaps and absences show which decisions your content doesn’t support yet.
You don’t need software for this, just one buyer decision, the committee around it, a set of prompts, an AI tool that shows its work, and a spreadsheet.
1. Map the committee, and write down each role’s fear
List everyone who has to say yes before the thing you sell gets bought — the real deciders, not the org chart.
For example, for a funded biotech that’s choosing a logo, the committee is the CEO, in-house counsel, ops, and marketing.
Next to each, write what that person is afraid of. That…
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