---
id: "quote-resolution-over-attention"
type: "quote"
source_timestamps: ["§ Probing the human-AI brand awareness gap"]
source_url: "https://hbr.org/2025/06/forget-what-you-know-about-seo-heres-how-to-optimize-your-brand-for-llms"
source_title: "Forget What You Know About Search. Optimize Your Brand for LLMs."
tags: ["core-thesis", "llm-behavior"]
related: ["concept-resolution-optimization", "claim-llms-optimize-for-resolution"]
speaker: "David Dubois, John Dawson and Akansh Jaiswal"
speakers: ["David Dubois", "John Dawson", "Akansh Jaiswal"]
quote: "For what we know about LLMs is this: LLMs are not optimizing for attention; they are optimizing for resolution."
sources: ["geo"]
sourceVaultSlug: "hbr-seg-geo"
originDay: 3
articleStem: "hbr-ext-10-optimize-brand-for-llms"
sourceUrl: "https://hbr.org/2025/06/forget-what-you-know-about-seo-heres-how-to-optimize-your-brand-for-llms"
sourceTitle: "Forget What You Know About Search. Optimize Your Brand for LLMs."
---
# Resolution vs Attention

> "For what we know about LLMs is this: LLMs are not optimizing for attention; they are optimizing for resolution."

— [[entity-david-dubois|David Dubois]], [[entity-john-dawson|John Dawson]] and [[entity-akansh-jaiswal|Akansh Jaiswal]]

The central mechanical difference between traditional social/search algorithms and generative AI models — the load-bearing insight behind [[concept-resolution-optimization|resolution optimization]] and [[claim-llms-optimize-for-resolution]].