---
id: "claim-llms-optimize-for-resolution"
type: "claim"
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: ["llm-behavior", "content-strategy"]
related: ["concept-resolution-optimization", "quote-resolution-over-attention"]
confidence: "high"
testable: true
speakers: ["David Dubois", "John Dawson", "Akansh Jaiswal"]
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."
---
# LLMs optimize for resolution, not attention

A core thesis of the article: LLMs process information fundamentally differently than social/search algorithms. Traditional platforms optimize for **human attention** (clicks, dwell time, engagement); LLMs optimize for **[[concept-resolution-optimization|resolution]]** — identifying the user's 'job to be done' and delivering a precise, factual, contextually appropriate solution. Therefore marketing must pivot **from persuasion to precision** (see [[quote-resolution-over-attention]]).

**Confidence: high (testable).**

**Enrichment / validation nuance:** Strongly supported. AI-search practitioners emphasize 'high-information-gain content,' 'content depth and completeness,' and 'authority-first content' as drivers of citation; RLHF-tuned models are optimized for perceived helpfulness and correctness, which aligns with 'resolution.' **Caveat:** some AI *products* still incorporate engagement signals, user feedback, and personalization into their platform behavior even if the *model training* objective is prediction/helpfulness. 'Resolution-only' is thus an oversimplification — correct as the **dominant** design goal, not the exclusive one.