Google says E-E-A-T, Experience, Expertise, Authoritativeness, Trustworthiness, isn't something their system measures directly. No counter ticks higher after adding an author bio. No switch flips when you publish a case study.
Yet if E-E-A-T describes the outcome they want, a mechanism must exist to measure it. Some system that draws a line between content that builds something real and content that just rearranges what's already out there.
Information Gain Score is the most compelling candidate for how that measurement actually works. And in a world flooded with AI-generated content, it has never been more applicable.
What is Information Gain Score?
A Google patent analysed by the late Bill Slawski in 2020 describes a system where Google evaluates documents not just on their own merits, but relative to a dataset of similar documents on the same topic. The question the system asks is straightforward: does this document add anything new?
When Google is evaluating twenty pages about the same subject, Information Gain Score identifies which pages contain ideas, data, or perspectives not present in the other nineteen. Pages with high information gain are treated as more valuable sources. Pages that rehash what everyone else has already said score lower, regardless of how well-written or technically optimised they are.
Google doesn't just want good content. It wants content that adds something new to the total body of knowledge on a topic. Information Gain Score is how it measures that addition.
Why this matters enormously right now
Across the internet, AI-generated content keeps pouring in at a scale that has no historical precedent. Most of it is, by definition, low information gain, assembled from patterns in existing content, optimised for readability, and published at volume.
Google has a strong incentive to surface the original sources that AI systems drew from to generate that content, rather than the AI-generated output itself. Information Gain Score is the mechanism that makes this possible.
Staying visible means producing content that machines cannot stitch together from existing patterns. Fresh studies, firsthand accounts, data you gathered yourself, perspectives shaped by doing the actual work rather than reading about it.
What counts as information gain?
Surveys you ran, experiments you conducted, figures from your own systems. Data that doesn't exist anywhere else on the web cannot be replicated or synthesised. By its nature, it carries strong signal.
A point of view that contradicts the consensus, or frames a familiar problem in a genuinely novel way. Not contrarianism for its own sake, but insight grounded in real experience that challenges received wisdom.
What actually happened when you tried something. Case studies, failure analyses, before-and-after accounts from real work. This is the category most directly aligned with the first E in E-E-A-T: demonstrated experience.
Charts and diagrams built from your own data. Google can recognise unique visual assets as a signal that content was produced through genuine effort rather than assembled from existing sources.
The AI content trap
AI-generated content is almost entirely low information gain by construction. Large language models are built to produce fluent text that resembles the best of what already exists. The output is statistically typical rather than informationally novel.
This doesn't mean AI tools have no place in content production. They help with drafting, structuring, editing, and scaling, so long as original insight gets added after. What fails every time is treating the AI output as the finished product.
A page written entirely by AI, covering a topic covered a thousand times, optimised for a keyword, published at scale, this is exactly what Information Gain Score was designed to penalise. It doesn't matter how well it reads or how clean the on-page SEO is. If it adds nothing new, it will struggle.
What this looks like in practice
Two pages on the same topic: improving local SEO for a small business. Page A is a well-structured AI guide covering the standard advice. Page B covers the same ground but includes data from 50 real client audits showing which tactics actually moved results in regional Australian markets, with specific percentages and named examples.
Page B has high information gain. Page A does not. The ranking outcome is predictable.
Information Gain and AI citation
This dynamic plays out across both Google and AI search platforms. When ChatGPT, Perplexity, or Google AI Overviews decide which sources to cite, they face the same problem: how do you select the best source from hundreds of pages covering the same topic?
The answer, consistently, is that AI systems cite sources that add something. Original data. Specific case studies. Practitioners who ran experiments and published results. High information gain content is more likely to be cited by AI systems because it's the kind of content those systems are trying to surface.
Content with high information gain ranks better in Google and gets cited more often by AI systems. Original research, real case studies, and genuine insight work harder across every channel simultaneously.
How to audit your own content for information gain
Pick a piece of content you want to rank for. Search Google for the same topic. Read the top five results. Then ask: does my content contain anything those five pages don't? Specific data, a different framing, a case study, a conclusion they haven't reached?
If the honest answer is no, you have two options: enrich the content with original insight, or accept that it will struggle to outperform the incumbents regardless of how well optimised it is.
If the answer is yes, the next question is whether that unique information is clearly structured and accessible. Information gain buried in paragraph eight of a 3,000-word article is harder for Google to extract than information gain surfaced early, clearly labelled, and structured for readability.
What to do about it
The shift is in how you think about content production. Volume is not the lever. Novelty is. One piece of content with genuinely original insight will outperform ten pieces of rehashed advice, both in Google rankings and in AI citation frequency.
For most businesses, the richest source of original content is their own operational experience. Client results, before-and-after data, process documentation, lessons from failures. This material exists inside the business. It just hasn't been published in a structured, accessible form that search engines can find and evaluate.
That is the gap worth closing. Not more content. Better content, grounded in experience only you have.
Our private AI visibility diagnostic reviews content authority, citation readiness, entity clarity and technical signals before recommending what to fix first.
20+ years in SEO and digital strategy. Founder of Digital Dominator, douglord.com, and private AI visibility diagnostic systems. Based in Byron Bay, working with clients worldwide.