2026-09-17, GEO · Fundamentals

What is AI SEO? The two meanings, and what each one asks of you

“AI SEO” is used with two meanings:

1. SEO for AI search: getting a site named, cited and recommended inside AI answers, from Google AI Overviews and AI Mode to ChatGPT, Perplexity, Gemini, Copilot and Grok.
2. SEO done with AI: using language models and AI tools to do the work of SEO, from keyword clustering to drafting pages.

Search engines have published guidance on both, and the two interact.

Meaning 1: optimizing for AI search

In this sense AI SEO is a synonym for generative engine optimization or answer engine optimization: the target is the answer rather than a position in the list of links. The outcomes you care about are:

• Mentioned: the answer names your brand.
• Cited: the answer links one of your pages as a source.
• Recommended: the answer suggests you for the user’s need, often in a list or a product card.
• Accurate: what the answer says about you is true.

What carries over from SEO

Most of classic technical SEO is a prerequisite for AI search visibility.

• Indexing and snippets. Google says a page must be indexed and eligible to be shown in Search with a snippet to be a supporting link in AI Overviews or AI Mode, and that there are no additional requirements or special optimizations beyond that (AI features and your website, checked 2026-09-17).
• Crawl access. The same page lists allowing crawling in robots.txt, internal links, good page experience, important content in text form and structured data that matches visible text as practices that apply.
• Ranking for the engine’s searches. Google documents query fan-out: its AI features may issue multiple related searches across subtopics and data sources to develop a response. Other assistants also search before answering; this API exposes the queries as searchQueries[] on ChatGPT (with include.searchQueries), Copilot and Grok, and search_model_queries on Perplexity. Query fan-out explained shows how to use them.

What changes

The crawler row deserves care. OpenAI documents separate agents: OAI-SearchBot surfaces sites in ChatGPT’s search features, and sites opted out of it are not shown in ChatGPT search answers, while GPTBot crawls content that may be used to train its models (OpenAI crawlers, checked 2026-09-17). Blocking “AI bots” as one group can remove you from answers you wanted to be in. AI crawlers covers each vendor’s agents.

What doesn’t exist

There is no special AI markup, AI sitemap or AI ranking factor documented by Google for its AI features; the AI features page explicitly says you don’t need new machine-readable files, AI text files or special schema.org structured data. Proposals such as llms.txt exist; llms.txt covers what is and isn’t documented about them and how to test whether one matters for you.

Meaning 2: doing SEO with AI

In the second sense AI SEO is a workflow: models draft briefs, cluster keywords, write meta descriptions, generate pages, suggest internal links or summarize competitors.

What search engines say

Google’s position is stated in two places:

• Its February 2023 post on AI-generated content is headed “Rewarding high-quality content, however it is produced”, and it tells publishers not to use automation, including AI, to generate content primarily to manipulate search rankings, which it treats as a spam policy violation (Google Search’s guidance about AI-generated content, checked 2026-09-17).
• Its spam policies define scaled content abuse as generating many pages primarily to manipulate rankings rather than help users, and list using generative AI tools to generate many pages without adding value for users as an example (Spam policies for Google web search, checked 2026-09-17).

Google draws the line at purpose and value for users, not at whether a model wrote the text.

Where AI tooling is useful and low-risk

• Clustering large keyword or prompt lists by intent.
• Extracting entities, claims and cited domains from thousands of stored AI answers.
• Drafting structure and first versions of pages that an expert then fills with specific, verified facts.
• Classifying answers: is this mention positive, a recommendation, or a warning?
• Checking consistency: does every page state the same price and plan limits?

Where it backfires

• Publishing unreviewed pages at scale for long-tail variations. That is the pattern Google’s scaled content abuse policy describes.
• Generic content with no first-hand facts. It gives answer engines nothing specific to quote, which undercuts meaning 1.
• Model-written facts about your own product. Language models state plausible numbers; a wrong price on your site can then be repeated by the engines that cite it.

How the two meanings interact

The meanings meet at one point: content quality as the engines see it. Pages that state specific, verifiable facts clearly are what AI search features can cite, and are also what Google’s AI-content guidance rewards. AI tools that speed up producing such pages help both; tools that produce volume without facts hurt both.

First-party data you can get today

Before building anything, use what the engines report to site owners.

Sources: Google says AI features are included in overall search traffic in Search Console and reported in the Performance report within the Web search type (AI features and your website, checked 2026-09-17). Microsoft introduced AI Performance in Bing Webmaster Tools as a public preview showing how publisher content appears across Microsoft Copilot, AI-generated summaries in Bing and select partner integrations, with total citations, average cited pages, grounding queries and page-level citation activity (Bing Webmaster Blog, February 2026, checked 2026-09-17). A June 16, 2026 post added intents, topics, citation share (your site’s percentage of all citations for the same grounding query) and period comparison (Bing Search Blog, checked 2026-09-17).

Microsoft’s own description notes that the citation metrics do not indicate ranking or placement within an individual answer. Neither report shows the answer text or which competitors were recommended beside you.

Collecting answers yourself

For what the first-party reports don’t show, collect answers yourself: fixed prompts, per engine and market, repeated on a schedule.

One request per engine

import os
import re
import requests

API = "https://api.answerline.dev"
HEADERS = {"Authorization": f"Bearer {os.environ['ANSWERLINE_API_KEY']}"}

PROMPT = "Which CRM is best for a 10-person real estate team?"
BRAND = re.compile(r"\bAcme CRM\b", re.IGNORECASE)
DOMAIN = "acmecrm.com"

ENGINES = {
    "chatgpt": {"prompt": PROMPT, "country": "US", "include": {"searchQueries": True}},
    "gemini": {"prompt": PROMPT, "country": "US"},
    "copilot": {"prompt": PROMPT, "country": "US"},
}

for engine, body in ENGINES.items():
    r = requests.post(f"{API}/v1/monitor/{engine}", headers=HEADERS, json=body, timeout=360)
    r.raise_for_status()
    result = r.json()["result"]
    sources = [s.get("url", "") for s in result.get("sources", [])]
    print(engine, {
        "mentioned": bool(BRAND.search(result.get("text", ""))),
        "cited": any(DOMAIN in u for u in sources),
        "sources": len(sources),
        "searches": result.get("searchQueries", []),
        "credits": r.headers.get("X-Credits-Charged"),
    })

Synchronously that is 9 credits for ChatGPT with search queries, 6 for Gemini and 7 for Copilot. The same payloads submitted as async tasks cost 7, 4 and 5, because tasks carry no synchronous surcharge.

Joining it with first-party data

Collected answers and webmaster reports answer different questions, so join them rather than choosing:

1. Bing AI Performance → prompt ideas. Grounding queries show phrases Copilot used when it retrieved your pages. Add the ones that match buyer questions to your prompt set.
2. Collected answers → competitor context. For the same prompts, collected answers show who else is named and which domains are cited, which no webmaster report shows.
3. Search Console → traffic outcome. Clicks from Web search include AI features. Watch landing pages that collected answers show being cited, and read changes against citation trends rather than in isolation.
4. Fan-out queries → SEO backlog. For queries the engines ran where you don’t rank, the fix is classic SEO. Check with POST /v1/monitor/google (3 credits per task for one page).

Doing it at scale

For a prompt set rather than a single prompt, submit async tasks in batches of up to 500 with POST /v1/async/task/batch, give each an idempotencyKey derived from prompt, engine, market and day, and let results arrive by webhook. Programmatic AI visibility tracking lays out the full system, and AI answer volatility explains how many runs per prompt make a rate trustworthy.

A working definition for your team

If you need one sentence for a strategy document: AI SEO is the practice of earning mentions, citations and accurate descriptions in AI-generated search answers, built on the same crawlable, indexable, specific content that classic SEO requires, and measured per engine with repeated answer collection plus the first-party reports search engines provide.

Write down which meaning a vendor, hire or agency proposal uses before comparing them. A tool that “does AI SEO” by generating 500 pages and a tool that “does AI SEO” by reporting your ChatGPT citation share solve unrelated problems.

Pitfalls

• Reading Search Console as an AI report. AI feature traffic is inside Web search, not separated. A traffic change there is not proof of an AI Overview change.
• Treating Bing citation counts as rank. Microsoft states the metrics don’t indicate placement within an answer.
• Blocking every AI crawler by default. Decide per agent: search-surfacing and training crawlers are separate for OpenAI.
• Generating pages to chase prompts. Scaled, low-value generation is what Google’s spam policy names; it also gives engines nothing to cite.
• Spot-checking in your browser. Personalization, location and run-to-run variance make one answer unrepresentative.

To start collecting answers across engines, follow the quickstart or read about rank tracking in AI answers.

Keep reading