101 · 15 canons

AEO

What answer engines retrieve and cite, how unstable that evidence is, and which publishing changes deserve to be tested instead of repeated as folklore.

TL;DR AEO is not one ranking system. Treat every result as engine-, prompt- and date-specific; preserve SEO fundamentals; and read vendor studies as hypotheses whose samples and denominators matter.

Curated by Ali Abouelatta

Cofounder of Lazyweb (a16z Speedrun). Previously grew subscriptions and built native ads at Duolingo. Writes First1000 (90,000+ subscribers).

Voices in this 101

Pranjal Aggarwal

Google Search Central

Reading path 1

Understand what is being measured

These sources separate generated-answer composition, citations, intent and user clicks—the metrics most AEO claims blur together.

01
GEO: Generative Engine Optimization ↗
Pranjal Aggarwal
Lead author of the Generative Engine Optimization research paper.
TL;DR
The original GEO paper tests how textual changes affect source visibility inside a fixed generative-engine benchmark; it does not prove a universal live-search ranking recipe.
What you'll read
The benchmark, visibility metric, nine editing methods, domain differences and the limits of a five-source GPT-3.5 simulation.
02
AI features and your website ↗
Google Search Central
Google's official source for search crawling, indexing and AI-feature guidance.
TL;DR
Google says the same crawlability, indexability and people-first content foundations apply to AI Overviews and AI Mode; no special AI schema is required.
What you'll read
Official eligibility, preview controls, measurement guidance and the clearest line between supported optimization and invented AEO requirements.
03
AI platform citation patterns ↗
Profound
AI-visibility platform publishing large citation and prompt studies.
TL;DR
Different answer engines lean on different source stacks, so an aggregate 'AI visibility' score can hide the engine where a brand is actually absent.
What you'll read
A large cross-platform citation analysis, source-share comparisons and the methodological gaps that remain without the raw prompt sample.
04
Citation overlap strategy ↗
Profound
AI-visibility platform publishing large citation and prompt studies.
TL;DR
Low citation overlap between ChatGPT and Perplexity argues for engine-specific monitoring rather than one universal source strategy.
What you'll read
A 100,000-prompt domain-overlap study and the important distinction between shared domains, shared pages and shared answer claims.
05
AI Search Shift ↗
Profound
AI-visibility platform publishing large citation and prompt studies.
TL;DR
ChatGPT's source alignment with Google can change quickly, but even aligned citations do not behave like ordinary position-one search clicks.
What you'll read
Citation-alignment trends, a smaller position-overlap sample and a useful warning against translating ranking position directly into AI traffic.

Reading path 2

Respect volatility and behavior

Before acting on a visibility score, understand how much sources move and whether users click at all.

06
AI Search Volatility ↗
Profound
AI-visibility platform publishing large citation and prompt studies.
TL;DR
Large shares of cited domains changed across a one-month comparison, making one-run visibility snapshots unsafe planning inputs.
What you'll read
Repeated prompt samples across four platforms, domain-drift rates and the difference between citation churn and a brand disappearing entirely.
07
Introducing Prompt Research Reports ↗
Profound
AI-visibility platform publishing large citation and prompt studies.
TL;DR
Prompt-volume claims depend on retrieval, clustering and coverage choices, so the product's own method description belongs beside any market-size number.
What you'll read
Profound's launch explanation of its conversation corpus, prompt selection and the moving denominators behind large reported totals.
08
Google users click less when an AI summary appears ↗
Pew Research Center
Nonpartisan research organization studying public behavior, including AI-search clicks.
TL;DR
In Pew's browser-panel sample, search sessions with an AI summary produced fewer clicks to traditional results and very few clicks on cited links.
What you'll read
An independent behavioral sample, disclosed methods and a useful separation between summary exposure, citation presence and outbound clicking.
09
AIO impact on Google CTR: 2026 update ↗
Seer Interactive
Search agency publishing longitudinal click-through studies of AI results.
TL;DR
Seer's longitudinal client data suggests AI summaries continue to reshape both paid and organic click-through rates, with branded demand behaving differently.
What you'll read
Observed CTR changes across query cohorts, the dataset's agency-client boundary and practical questions for search forecasting.
10
The data on Reddit and AI search ↗
Profound
AI-visibility platform publishing large citation and prompt studies.
TL;DR
Reddit is a meaningful but not dominant aggregate citation source; source type, topic and content age matter more than the slogan 'AI loves Reddit.'
What you'll read
A multi-billion-citation analysis, post-age findings, platform differences and the disclosure that Profound worked with Reddit.

Reading path 3

Turn evidence into a publishing plan

Use structure and prompt research as inputs to experiments—not guarantees. These readings show what to test and what still lacks causal proof.

11
How to optimize for answer engines ↗
Profound
AI-visibility platform publishing large citation and prompt studies.
TL;DR
Tables, headings, lists, schema and complete answers are plausible structural signals, but the public study does not publish a causal lift for any one change.
What you'll read
A 2,000-page pattern study that is useful as an experiment backlog when read with its missing controls and undisclosed query set.
12
ChatGPT intent landmark study ↗
Profound
AI-visibility platform publishing large citation and prompt studies.
TL;DR
AI prompt intent does not map cleanly onto classic search intent, so keyword-derived prompt lists can misstate what people ask assistants to do.
What you'll read
A large vendor-classified prompt sample, its intent distribution and the missing details you need before treating it as a market census.
13
Prompt Research Reports ↗
Profound
AI-visibility platform publishing large citation and prompt studies.
TL;DR
Prompt research is a retrieval-and-clustering problem, not a direct equivalent of exact keyword volume from a search ad system.
What you'll read
Profound's own description of retrieval, ranking, clustering and coverage selection over its conversation corpus.
14
Beyond the SERP: Visibility Layer and Trust Stack ↗
Kevin Indig
Growth Memo author and organic-growth advisor studying AI search.
TL;DR
When citation clicks are rare, the strategic job expands from winning a click to becoming a trusted source across the surfaces that shape the answer.
What you'll read
A practical framework for zero-click visibility, source trust, brand mentions and measuring influence beyond sessions.
15
Winners and losers of the AI disruption of search ↗
Kevin Indig
Growth Memo author and organic-growth advisor studying AI search.
TL;DR
AI search redistributes attention toward recognizable sources and away from interchangeable pages, making brand trust part of organic performance.
What you'll read
A synthesis of traffic shifts, trust signals, category differences and the strategic choices available to publishers and product companies.