Preview Post- December 5, 2025
There are clear gaps in the way our industry measures visibility in the age of LLMs and soon AI agents. Traditional SEO metrics were built for a world where search engines retrieved documents, ranked them and sent traffic back. That world is changing. Modern and newer LLM models answer many questions internally and only fetch from the web when they lack confidence in answers.
This creates a new opportunity for measurement. Here’s an idea I have been framing in my head for a metric: I’m calling it grounding pressure.
Grounding pressure is the degree to which a model feels the need to fetch external information before producing an answer. If the model already holds the facts confidently, it will not ground. If it does not, or if the answer carries any risk without verification, grounding becomes necessary.
For SEOs, the easiest way to think about this is to treat grounding pressure as the LLM era equivalent of crawl rate and impressions. High grounding pressure looks like frequent LLM bot hits on a page or content cluster. The model keeps returning because it needs the information to reason properly. Low grounding pressure is the opposite. The bots rarely, if ever, touch the page because the model already knows everything it contains. In that state, your content becomes background noise.
This behaviour matters because it shows which pages the model depends on and which ones it can safely ignore. In a world where models carry huge amounts of internal knowledge, grounding pressure becomes a direct indicator of informational value. Pages with high grounding pressure still shape answers.
Pages with low grounding pressure quietly fall out of the reasoning loop. They are still online, but the model has no reason to fetch them, so they slip out of sight. It is the closest LLM era equivalent to being deindexed in Google Search.
Grounding pressure indicates whether your content contains non-generic, decision-supporting information that a model needs to answer confidently.
High grounding pressure indicates:
Low grounding pressure indicates:
In other words, grounding pressure is a direct measure of informational gain.
It does, and the reason is simple. Search engines and LLM-driven assistants still need to ground whenever the information carries risk, detail or commercial importance. They pull the live web for things like:
These are the areas where accuracy matters. A model cannot guess. It has to be checked.
If the model already holds the information confidently, it will not ground. That is the moment your content becomes optional. It still exists, but it no longer plays an active role in the reasoning process. Content that creates grounding pressure is the opposite. It supplies details that the model cannot reconstruct from memory, so it becomes part of the answer.
That is what makes it essential. This gives us a very clear way to decide where to invest human effort. Strengthen the pages that the model needs. Streamline or automate the ones it does not.
Grounding pressure is not a theoretical concept; you can track it in a practical way using the data already available in your LLM bot logs. The goal is to understand how often a model feels the need to fetch your content before responding to a user. If that behaviour increases, grounding pressure is rising. If it drops, the page is becoming less important.
The first signal is simple: how often the model hits the page. Frequent visits suggest the model is relying on the content as part of its reasoning. A decline suggests the information may no longer be needed or is too generic to influence the answer. The second signal is cross-model behaviour.
If more than one model grounds to the same page, this usually indicates that the page contains specific, non-transferable information that multiple systems consider valuable.
It is also useful to track whether grounding behaviour changes after content updates. If you strengthen a category description with better comparisons, clearer guidance or richer context, you should expect grounding frequency to increase. If it falls, the update did not provide meaningful information gain. Over time, these three signals give you a clear picture of how much the models depend on your page and whether your optimisation work is having the intended effect.
Grounding pressure gives you a simple classification model:
High pressure pages
These pages carry real informational value and influence how a model reasons about a topic. They contain insight, comparison, context or domain knowledge the model does not already hold. They should be protected, strengthened and written by someone who understands the subject. These are the pages that will continue to matter as grounding becomes more selective.
Medium pressure pages
These have some value, but the depth is uneven. They often point in the right direction but do not go far enough. They can be improved through clearer explanations, stronger comparisons and better decision guidance. With the right work, many medium pressure pages can be pushed into the high pressure category.
Low pressure pages
These are generic and interchangeable. The model already knows everything they say. They add no information gain and do not influence reasoning. These pages can be automated, consolidated or removed entirely. They are unlikely to play a role in future visibility.
This framework gives you a rational way to decide where human effort pays off and where automation is acceptable. Is all this just E-E-A-T rebadged? In a way, yes, but this is a measurement metric I’m talking about here.
Grounding pressure does not rely on rankings, impressions or any of the surface level signals we usually measure, and it is not affected by how a results page is designed. It measures the behaviour of the model itself. That instantly makes it more stable than traditional SEO metrics. It becomes the LLM equivalent for crawl rate and impressions combined into a single, clearer signal: how often the model actually needs your content.
If grounding becomes rarer in the next few years, which I think it will as LLMs get smarter, the pages with the very highest grounding pressure will be the ones the system continues to rely on. They contain the information the model cannot answer from memory. As the web becomes smaller, more structured and more dependent on genuine expertise, grounding pressure remains a reliable way of understanding which pages will still matter and which will quietly fall away.
Grounding pressure feels like a new idea, but strangely, it takes us right back to what the web was meant to be. The early web was built on people sharing what they knew because it mattered. Just publishing content online in those early days required some technical expertise and money. It was slow, messy and honest. But, somewhere along the way, that purpose got buried under content at scale and the race for easy traffic. The informational layer ballooned, and most of it added nothing.
What I have realised through all of this is that the new informational age is not destroying the web. It is correcting it. It is stripping out the noise and rewarding the pages that actually carry insight. Pages that create grounding pressure. Pages that give the LLM models something they cannot already answer with ease. Pages written with intent, expertise and clarity. Everything else fades, and honestly, that is not a bad outcome.
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