Is GEO the same as AEO?

Effectively yes, with a caveat. Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) are used interchangeably by most practitioners and vendors in 2026, for the same goal: getting your brand cited in AI-generated answers from engines like ChatGPT, Perplexity, Google AI Overviews and Gemini. A minority draw a line (AEO for direct-answer surfaces like snippets and voice, GEO for citation inside longer generative answers), but the engines have largely merged those behaviours, so the distinction rarely changes the work. Pick one term and focus on the execution.

Is AEO replacing SEO?

No, it adds to SEO rather than replacing it. Classic ranking still feeds AI retrieval: Ahrefs found 38% of pages cited in Google AI Overviews also rank in the organic top 10, so SEO fundamentals still matter as a feeder. But the clicks are moving into the answers themselves. Pew Research found users click a result in just 8% of searches that show an AI summary, versus 15% without. So being cited inside the answer (AEO/GEO) becomes its own discipline, with different levers from classic ranking.

What's the main difference between SEO and AEO/GEO?

SEO optimises your own pages to rank and earn clicks; the metric is keyword position and the battleground is your domain. AEO/GEO optimises your whole presence, especially third-party sources like reviews, community threads and independent articles, to be cited inside AI answers; the metric is citation share and the battleground is largely off your own site. Brands are cited several times more often through third-party sources than through their own pages.

How do I measure AEO/GEO?

By citation share and visibility across AI engines for your buyer prompts, how often you're named in answers, your share of voice versus competitors, and which sources the engines cite, not by keyword rankings. AI-visibility trackers such as Peec and Profound measure this across ChatGPT, Perplexity, Google AI Overviews and Gemini. The shift is from tracking blue-link position to tracking whether the model recommends you.

Do I need different tools for AEO/GEO than SEO?

You need an additional layer rather than a replacement. Keep your existing SEO stack for crawlability, rankings and on-page work, and add an AI-visibility tracker such as Peec or Profound to measure citations across AI engines, plus an execution layer (in-house, an agency, or an AI-search agent like Schmitdy) to actually move them. The trackers tell you where you're missing; the execution layer closes the source gap.

AEO GEO SEO Answer Engine Optimization Generative Engine Optimization AI Search AI Overviews Citation Share ChatGPT Perplexity

AEO vs GEO vs SEO: The Difference, and Which One You Need in 2026

Marco Lobo

6 June 2026·Updated 12 June 2026·9 min read

TL;DR

SEO optimises your pages to rank in classic search and earn the click. GEO (Generative Engine Optimization) and AEO (Answer Engine Optimization) optimise to be cited inside the AI answer in ChatGPT, Perplexity, Google AI Overviews, Gemini and Copilot.

GEO was coined in a November 2023 Princeton and Georgia Tech research paper; AEO grew out of the SEO industry. In 2026 most practitioners and vendors use the two interchangeably, though a minority still draw a line. We explain the real distinction and why it rarely changes what you do.

This is not optional positioning. In early 2026, 68 percent of US Google searches ended without a click, and Google AI Overviews cut click-through on the top result by around 58 percent when they appear.

You still do all three. SEO feeds the pool the AI engines retrieve from; GEO and AEO win the citation inside the answer. But you measure them on citation share across engines, not on blue-link position.

Frequently Asked Questions

AEO GEO SEO Answer Engine Optimization Generative Engine Optimization AI Search AI Overviews Citation Share ChatGPT Perplexity

Marco Lobo

Founder, Schmitdy

Marco builds AI search growth systems that turn prompts, sources, content, and agents into pipeline for B2B teams.

LinkedIn X / Twitter

Handdrawn editorial illustration on a cream background split into three panels: on the left, a dashboard with a gauge and bar chart labelled tool; in the middle, three people standing together labelled agency; on the right, a friendly robot wearing a headset talking with a person labelled agent — the three layers of improving AI search visibility

AI SearchAI SearchAEO

AI Search Tool vs Agency vs Agent: Which Do You Actually Need in 2026?

There are three ways to improve your AI search visibility, and they're layers of the same stack, not rivals: a tool (Peec, Profound) measures where you stand; an agency executes the optimisation work with people; an agent — an AI-search agent — does the recurring volume under human review. A tool changes your understanding, not the answer. An agency changes the answer but costs the most and moves at human speed. An agent does the volume at mid-market cost with humans owning quality and strategy. In 2026 most teams keep a tool and add one execution layer — the real decision is which one.

Marco Lobo·6 Jun 2026·7 min read

Handdrawn editorial illustration on a cream background showing the Peec MCP agent pipeline: a dashboard of charts on the left connected by an MCP USB-C cable to a friendly robot agent in the centre, which passes through a human-approval clipboard with a checkmark to shipped outputs on the right — a content document and a JSON-LD schema block

AI SearchAI SearchPeec

How to Build AI Agents on the Peec AI MCP for AEO and GEO (2026)

In late 2025 Peec AI shipped an official MCP server, which makes its entire AI-search measurement layer — projects, prompts, per-engine brand visibility, full AI answer transcripts, the source/citation graph and scored Actions — directly callable by an LLM agent. That means you can wire a Claude or GPT agent straight to the ground truth of how ChatGPT, Perplexity, Gemini and AI Overviews answer your category, and have it reason over the data and draft the work to close the gaps. The defensible build pattern is a read-only, five-stage loop with a human ship gate: the agent triages and drafts; a person verifies and ships.

Marco Lobo·6 Jun 2026·9 min read

Handdrawn editorial illustration on a cream background: an AI-search dashboard of charts and a magnifying glass on the left feeds through a funnel in the centre, which sorts into three citation chips — a review star, a community comment, and a reference source — that flow into an AI answer speech bubble on the right; the path from dashboard data to AI citations

AI SearchAI SearchPeec

From Peec or Profound Data to AI Citations: A 5-Step Playbook (2026)

Your Peec or Profound dashboard tells you where you're invisible in AI answers, but a visibility score is an outcome, not an instruction. Turning the data into citations is a five-step job: read the gap reports rather than the vanity metric, turn the cited sources into a third-party target list, fix the pages that are retrieved but not cited, build the off-site footprint the data points to, then re-measure the delta on a cadence. The highest-leverage move is the source/domain gap report — the third-party sites citing your competitors but not you. Most of the work lands off your own domain and has to recur, which is the gap between owning a dashboard and moving its numbers.

Marco Lobo·6 Jun 2026·7 min read

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