---
id: "concept-share-of-model-d25"
type: "concept"
source_timestamps: ["§ Three Practices to Build AI Recall Share"]
tags: ["marketing-metrics", "exposure"]
related: ["concept-ai-recall-share"]
definition: "A metric that measures how often a brand appears in AI-generated responses, capturing broad exposure rather than specific problem-solution fit."
sources: ["geo"]
sourceVaultSlug: "hbr-seg-geo"
originDay: 3
articleStem: "hbr-new-25-get-ai-to-surface-your-brand"
sourceUrl: "https://hbr.org/2026/06/how-to-get-ai-to-surface-your-brand"
sourceTitle: "How to Get AI to Surface Your Brand"
---
# Share of Model

Coined by **Dubois, Dawson, and Jaiswal**, *share of model* is a metric that measures the raw frequency with which a brand appears in AI-generated responses — the LLM-era analogue of "share of voice." The authors of this piece contrast share of model with their own concept of [[concept-ai-recall-share|AI recall share]].

While share of model captures broad exposure and visibility within an LLM's outputs, it does **not** necessarily account for relevance or problem-solution fit. The authors argue that optimizing for mere exposure is less effective than optimizing for fit, because AI recommendations are fundamentally driven by matching specific user conditions to specific product attributes (see [[concept-ai-recommendation-chain|AI Recommendation Chain]]).

> Enrichment note: Dubois, Dawson & Jaiswal have indeed proposed "share of model" as a metric for how often a brand appears in AI outputs, analogous to share of voice; it focuses on mention frequency irrespective of whether the brand is the best fit. External literature supports the need to distinguish raw visibility from *relevant* visibility, which is exactly the refinement AI recall share introduces.


## Related across articles
- [[concept-share-of-model-d10]]
- [[concept-ai-recall-share]]