The inclusion threshold is a consequence of how AI models learn. Two mechanisms play a crucial role: training bias and frequency heuristics.
Training bias
An AI model learns from the data it is trained on. If that data reflects a particular market structure — in which large, established players are dominant — the model will reproduce that market structure. That is not a conscious choice by the model builder, but a property of how machine learning works.
Concretely: if in the training data player A is mentioned 100x in the context of a product category and player B only 5x, player A has a structural advantage in answers about that category — regardless of the quality of their respective websites. That applies to banks, to fire safety installers, to SaaS tools, to law firms. The market structure in training data becomes the market structure in AI answers.
Specialised GEO tools (emerging)
Alongside training bias, frequency heuristics play a role: the model gives preference to answers that match what it has seen most frequently. That means the most-mentioned brands are also the most likely candidates for mention in an answer.
Implication: GEO is not just a content optimisation problem. It is also a presence problem. You simply have to be mentioned more often and more consistently in the right contexts — across the entire web.
Mental availability in AI models
The concept of “mental availability” from brand science — the degree to which a brand spontaneously comes to mind in a category — is directly applicable to AI models. A brand with high mental availability in an AI model is mentioned more often, described more extensively and positioned more positively.
You build mental availability in AI the same way as in humans: through broad, consistent presence in the right categorical contexts, over a longer period.
What can you do?
- Invest in long-term, broad external presence — not just on your own website
- Ensure your brand is consistently mentioned in the right categorical context (PR, partnerships, directories)
- Build Wikipedia presence — one of the most influential sources in AI training data
- Monitor your inclusion rate per prompt category and use it as an indicator of your brand penetration
Related in the hub
- 8.1 The inclusion threshold
- 8.3 Measuring inclusion rate
- 3.5 Off-site GEO and distribution
- 7.1.4 Trustpilot and review platforms as training data
→ Want to know how to measure inclusion dynamics systematically? Read 8.3.