Is good SEO good GEO? What only moves AI answers

TL;DR: Google’s official position is that AI Overviews and AI Mode run on the same core ranking systems as classic Search, so foundational SEO work carries over. The correlation data mostly backs that up for content quality, crawlability, and author expertise. It breaks down in three places: unlinked brand mentions beat backlinks roughly three to one for AI visibility, schema markup shows no measurable citation lift in a controlled Ahrefs test, and content freshness carries more weight in AI answers than it ever did in classic rankings. Good SEO is a floor, not a ceiling.

What Google actually said
The three-way test: overlap, weaker, and AI-only
What carries over completely
What carries over but matters less
What only moves AI answers
The comparison table
The decision tree: diagnose your own gap
Where the practitioner data disagrees with Google
A 30-day test plan to find your own delta
Frequently asked questions
Key takeaways

What Google actually said

In June 2026, Brendon Kraham, Google’s VP of Search and Commerce for Global Ads Solutions, published a piece on Think with Google titled “Good SEO is good GEO,” aimed directly at CMOs (Think with Google). His argument is structural: AI Mode and AI Overviews are built on top of the same core ranking and quality systems that have always powered Search results, so there is no separate “generative engine” budget line to fund (Digital Applied). Kraham told marketing leaders to stop trying to write for bots and keep writing for people, on the reasoning that whatever earns a citation in an AI answer is the same thing that earned a ranking in blue links: real expertise, first-hand experience, and a point of view a model cannot invent on its own (ppc.land).

That is a defensible position and it is also an incomplete one. Google has an obvious incentive to say the two disciplines are the same thing: it keeps the advertising and search-quality teams from having to build or fund a second optimization channel, and it keeps SEO agencies selling the same retainer under a new label. The claim is true for the signals both systems share. It is not true for the signals that only exist because an AI answer engine, not a ranked list of links, is doing the retrieving.

The r/seogrowth community asked this exact question in July 2026, and the thread pulled 48 comments in under a week, more engagement than most threads in that subreddit get in a month (r/seogrowth). Practitioners running both channels day to day do not report a clean overlap. They report a large shared core and a smaller, high-impact set of AI-only levers that a classic SEO checklist never asks about.

The three-way test: overlap, weaker, and AI-only

Sort every ranking or citation factor into one of three buckets and the “is it true” question stops being rhetorical:

Full overlap. The factor helps both classic rankings and AI citations, at similar strength. Content depth, crawlability, page speed, and author credibility land here.
Weaker in AI answers. The factor still helps rankings, but its correlation with AI citations is measurably lower. Backlinks and keyword-matched title tags land here.
AI-only or AI-dominant. The factor barely moves classic rankings but strongly predicts AI citation. Unlinked brand mentions, cross-platform presence, and answer-shaped formatting land here.

If you only run an AI visibility audit checklist built off your existing SEO checklist, you will catch bucket one, half-catch bucket two, and miss bucket three completely. That is the actual gap between “good SEO is good GEO” as a rule of thumb and as an engineering spec.

What carries over completely

Content quality and depth

Both systems reward substantive answers to a real question over thin summaries. Ahrefs’ analysis of what actually gets pages included in AI Overviews found original data, proprietary research, and detailed how-to content consistently outperform generic top-of-funnel explainers, the same pattern that has driven organic rankings since Google’s helpful content signals rolled into core ranking in 2023 (Ahrefs). Case studies and pages with pricing or methodology detail beat “what is X” pages on both fronts, because both a ranking algorithm and a language model are trying to match a real information need, not a keyword string.

Technical crawlability

An AI system cannot cite what it cannot fetch. If your robots.txt blocks GPTBot, ClaudeBot, or PerplexityBot, or your JavaScript rendering hides content from a crawler that does not execute your full bundle, you lose both classic indexing and AI retrieval at the same step. This is pure infrastructure, and it is identical infrastructure for both systems. Nobody in the research disputes this bucket.

Author expertise and E-E-A-T signals

Google added the second E, Experience, to its quality guidelines specifically to separate practitioners who have done the thing from writers who are summarizing what they read about it. That distinction now does double duty. Research tracking 15,000-plus AI Overview citations found that a large majority go to sources carrying strong E-E-A-T signals, and content attributed to a named, credentialed author gets cited roughly three times more often than anonymous content (Contently). Language models parse bylines, author bio pages, and external mentions of the author the same way a human reader would judge whether to trust a claim. This is the strongest overlap point in the entire dataset, and it is the closest thing to proof for Kraham’s argument.

Site speed and Core Web Vitals

A crawler with a compute budget behaves like a human with a patience budget. Slow, render-blocked pages get skipped by both a Googlebot crawl budget allocation and an AI retrieval pipeline running on a timeout. This one has never been AI-specific; it predates AI Overviews by a decade and applies unchanged.

What carries over but matters less

Backlinks

This is where “good SEO is good GEO” starts to strain. Ahrefs studied 75,000 brands and measured Spearman correlations between a set of off-site and on-site factors and AI Overview brand visibility. Traditional backlinks correlated at 0.218. Unlinked web mentions of the brand correlated at 0.664, three times stronger (Ahrefs). Branded anchor text and brand search volume also outranked raw backlink count. Brands in the top quartile for web mentions averaged 169 AI Overview appearances; the next quartile down averaged 14. A backlink still helps you rank in classic Search, and it still contributes some authority signal to AI systems, but treating it as your primary AI-visibility lever wastes budget that unlinked-mention work would use more effectively.

Ahrefs was explicit that correlation is not causation here: brands with strong cross-platform presence may simply also do more of everything else well. But a 3x gap in Spearman correlation, measured across 75,000 brands, is not noise, and it is not something a classic link-building program is built to move.

Schema markup

Google has stated structured data is not required for AI Overviews or AI Mode, and the controlled test backs that up more thoroughly than most SEO folklore gets tested. Ahrefs tracked 1,885 pages that added JSON-LD schema between August 2025 and March 2026, matched against 4,000 control pages that did not, and measured citation change across Google AI Mode, ChatGPT, and Google AI Overviews. AI Mode moved +2.4%, ChatGPT moved +2.2%, both statistically indistinguishable from zero, and AI Overviews moved -4.6%, a result that points the wrong direction (Ahrefs). Pages that already carry schema do get cited more often (roughly 53% of cited pages have schema, about three times the rate of uncited pages), but that is a correlation driven by sites that invest in better content also investing in better markup, not a causal lift from the markup itself. If you want the platform-by-platform truth table on this, see do you need llms.txt in 2026, which walks the same correlation-versus-causation trap for a different technical file.

Exact-match keyword targeting

Classic SEO still rewards a title tag and H1 that match search intent tightly. AI systems answer a paraphrased version of the query and pull from whichever page best matches the underlying question, not the exact string. A page built around one narrow keyword variant loses relative ground in AI answers compared to a page structured around the question cluster, even if the keyword-matched page still ranks fine in blue links.

What only moves AI answers

Unlinked brand mentions across the open web

Covered above as an overlap-but-weaker factor for backlinks; the flip side is that mentions themselves are close to an AI-only lever. A mention in a TechCrunch article, a G2 review, a YouTube video description, or a Reddit thread, with no link at all, correlates more strongly with AI Overview visibility than a do-follow backlink from a low-authority guest post. Classic Search algorithms still weight the link graph heavily. AI retrieval systems increasingly weight brand presence as a trust signal independent of whether that presence links back to you.

Cross-platform and video presence

Ahrefs’ follow-up analysis added YouTube mentions and mention impressions to the model and found they outperformed every other signal tested, correlating at roughly 0.737 with AI brand visibility, stronger than web mentions themselves (Ahrefs). A classic SEO program that never touches YouTube has no lever here at all. This is the clearest single data point against “good SEO is good GEO” as a complete strategy, because a site can execute flawless technical and content SEO and still have zero video-platform presence contributing to AI visibility.

Reddit and forum presence specifically

Reddit was the single most-cited domain across Google AI Overviews and Perplexity from August 2024 through June 2025, and ChatGPT cites Reddit in roughly 12% of US answers even though Reddit appears in only about 37% of Google’s own SERPs for the same queries (Sitebulb). That is a citation rate disproportionate to its ranking presence, meaning AI systems are pulling Reddit threads into answers at a rate classic Search never gave that domain. Building a compliant presence there is close to a pure GEO play; see how to use Reddit to get cited by AI search without getting banned for the mechanics.

Content freshness, more aggressively than classic Search

Google has always used freshness as a minor ranking factor for time-sensitive queries. AI answer engines lean on it harder across the board. Perplexity cited content published within the prior 30 days at an 82% rate in one 2026 analysis, and visible year markers in titles and headings, such as “2026,” improved citation rates by roughly 30% (Otterly AI). A page that still ranks fine in classic Search from age and accumulated authority can simultaneously lose AI citations to a newer, thinner competitor purely on recency. This is the strongest case against treating SEO equity as durable GEO equity: rankings decay slowly, AI citations decay fast.

Question-shaped, extractable formatting

Perplexity’s own behavior favors pages with H2/H3 headings organized around specific questions, visible statistics, and named sources with a stated methodology, a structural preference that classic ranking algorithms do not enforce nearly as strictly (Leapd). A page can rank well with prose-heavy paragraphs and no question-formatted subheads. It is measurably harder for an AI system to extract a clean, citable passage from that same page.

If your team is chasing share of answer instead of share of voice, formatting for extraction is the lever that moves that number, not the lever that moves a SERP position.

The comparison table

Factor	Moves classic SEO rankings	Moves AI citations	Verdict
Author expertise / E-E-A-T	✓	✓	Full overlap, invest here first
Site speed and crawlability	✓	✓	Full overlap, table stakes
Content depth and original data	✓	✓	Full overlap, but AI weights it slightly higher
Backlinks (do-follow)	✓	Weak (0.218 correlation)	Still worth doing, do not over-index
Schema/JSON-LD markup	✓ (minor)	✗ (no measurable lift)	Keep for accessibility, skip as a GEO tactic
Exact-match keyword titles	✓	Weak	AI paraphrases the query, not the title
Unlinked brand mentions	Weak	✓ (0.664 correlation)	Under-resourced by most SEO teams
YouTube mentions	✗	✓ (0.737 correlation)	Highest single correlation measured
Reddit / forum presence	Weak	✓ (most-cited domain 2024-2025)	AI-disproportionate, build deliberately
Content freshness (30-day)	Minor	✓ (82% Perplexity citation rate on fresh content)	Decays faster than SEO equity
Question-shaped H2/H3 formatting	Minor	✓	Structural, not a ranking factor per se

The decision tree: diagnose your own gap

flowchart TD
    A[You rank on Google but are not cited by AI] --> B{Is the page crawlable by GPTBot / ClaudeBot / PerplexityBot?}
    B -- No --> C[Fix robots.txt and rendering first. Nothing else matters until this is fixed.]
    B -- Yes --> D{Does the page carry a named, credentialed author?}
    D -- No --> E[Add a real author bio. This is the strongest overlap factor in the data.]
    D -- Yes --> F{Is your brand mentioned unlinked on 3rd-party sites, Reddit, or YouTube?}
    F -- No --> G[Build brand mentions. Weak on rankings, dominant on AI citation 0.664 to 0.737 correlation.]
    F -- Yes --> H{Was this page or a competing page updated in the last 30 days?}
    H -- No --> I[Refresh with a visible date. Perplexity favors 30-day-fresh content at 82%.]
    H -- Yes --> J{Are your H2/H3s written as extractable questions with stated sources?}
    J -- No --> K[Reformat around the question cluster, not the keyword string.]
    J -- Yes --> L[You have covered the known AI-only levers. Track citations directly, not just rankings.]

Run an AI visibility audit against every branch of this tree before assuming the problem is a classic SEO gap. Most teams stop at node B or D because that is where their existing SEO checklist already lives, and never reach the branches that are actually AI-specific.

Where the practitioner data disagrees with Google

Google’s framing treats GEO as a subset of SEO: do the SEO work well and the AI visibility follows. The correlation data says GEO is better described as SEO plus a distinct layer of off-page, cross-platform, and freshness signals that classic SEO programs were never built to track, let alone execute. The overlap is real and it is large, probably 60 to 70% of total effort by the shape of the data above. It is not complete.

This matters practically because budget follows framing. If a CMO believes “good SEO is good GEO” without qualification, the SEO retainer stays the same size and nothing gets allocated to Reddit presence, YouTube mention building, or a freshness-refresh cadence separate from the existing content calendar. Traditional SEO metrics also cannot capture AI search performance on their own; answer engines reward brand authority signals that keyword-rank tracking was never built to measure (X, @KDHungerford), which is one more reason the two disciplines need separate tracking even where their tactics overlap.

Separate this from tactics for a moment and look at outcomes. AI Overviews now appear on a majority of search result pages and organic click-through rates measurably drop on the queries where they show up, with Ahrefs measuring a 34-58% CTR reduction range on AI Overview queries as of 2026 (seo-kreativ, citing Ahrefs). A ranking that no longer produces a click is not the same win it used to be, even when the SEO tactics that earned it stay identical. That is the strongest argument that “good SEO is good GEO” undersells the change: the destination changed even where the map did not.

A 30-day test plan to find your own delta

You do not need to take a correlation study’s word for your own site. Run this against 10 to 20 of your highest-traffic pages:

Week 1: Baseline. Pull current rankings for your target keywords and check which pages currently get cited in ChatGPT, Perplexity, and Google AI Overviews for those same queries, using GA4 tracking for AI referral traffic plus manual query checks.
Week 2: Fix the shared floor. Add or strengthen author bios, confirm crawler access in robots.txt, and check Core Web Vitals. These move both systems, so do them regardless of what you find later.
Week 3: Test one AI-only lever per page. Pick brand mentions, freshness, or formatting, not all three at once, so you can attribute the change. Refresh dates and add question-formatted H2s on half your test set; leave the other half untouched as a control.
Week 4: Re-check citations, not just rankings. Rankings on the control pages should hold steady if nothing else changed. If the test pages pick up new AI citations while rankings stay flat across both groups, you have isolated an AI-only lever specific to your niche, and you have real internal data instead of a borrowed correlation number.

Most teams skip this because it takes discipline to hold a control group instead of changing everything at once. It is the only way to know whether the Ahrefs numbers, which describe 75,000 brands in aggregate, apply the same way to your specific site and query set.

Frequently asked questions

Is Google’s “good SEO is good GEO” claim accurate?

Partly. It holds for author expertise, crawlability, content depth, and site speed, which move both classic rankings and AI citations at similar strength. It breaks down for backlinks (0.218 correlation with AI visibility versus 0.664 for unlinked mentions), schema markup (no measurable citation lift in Ahrefs’ controlled test), and content freshness, which AI systems weight more heavily than classic Search ever did.

What percentage of SEO work also helps GEO?

There is no single verified percentage, and treat any specific number here skeptically since no study has measured “percentage of SEO effort that transfers.” The correlation data suggests the shared core (E-E-A-T, crawlability, content depth) covers most of the foundational work, while a smaller set of off-page and freshness signals need dedicated, separate effort.

Do backlinks still matter if I’m optimizing for AI search?

Yes, but treat them as a secondary lever. Ahrefs measured backlinks correlating at 0.218 with AI Overview brand visibility across 75,000 brands, compared to 0.664 for unlinked web mentions. A backlink still helps classic rankings and contributes some authority signal, but budget spent purely chasing link volume for AI visibility is less efficient than budget spent on earned, unlinked brand mentions.

Does schema markup help you get cited by ChatGPT or AI Overviews?

Ahrefs tracked 1,885 pages that added JSON-LD schema against 4,000 control pages and found no statistically meaningful citation lift on Google AI Mode or ChatGPT, and a negative result on Google AI Overviews. Google has also stated structured data is not required for AI Overviews or AI Mode. Keep schema for accessibility and rich results, not as an AI-visibility tactic.

Why does my page rank #1 on Google but never show up in ChatGPT answers?

The most common causes are AI crawler blocks in robots.txt, missing or weak author attribution, low unlinked brand-mention volume across the web, or content that has not been refreshed in over 30 days. Rankings measure link-graph authority accumulated over years. AI citations weight recency and cross-platform brand presence far more heavily, so an old, well-linked page can rank well and still lose the citation to a newer competitor.

How much does content freshness actually matter for AI citations?

More than it matters for classic rankings. One 2026 analysis found Perplexity cited content published within the prior 30 days at an 82% rate, and visible year markers such as “2026” in titles improved citation rates by roughly 30%. Classic Search treats freshness as a minor factor for time-sensitive queries only; AI systems apply it more broadly.

Is Reddit actually more useful than backlinks for AI visibility?

For citation purposes, yes, in a specific and measurable way. Reddit was the single most-cited domain across Google AI Overviews and Perplexity from August 2024 to June 2025, and it shows up in roughly 12% of ChatGPT’s US answers despite appearing in only about 37% of Google’s own SERPs for comparable queries. That is a citation rate disproportionate to its classic-ranking footprint.

Do YouTube mentions really outperform every other AI visibility signal?

In Ahrefs’ extended correlation analysis, yes: YouTube mentions correlated at roughly 0.737 with AI brand visibility, the highest of any factor tested, ahead of unlinked web mentions at 0.664. This is correlational, not proven causal, but it is the strongest single number in the dataset and a channel most SEO programs do not touch at all.

What is the difference between SEO, AEO, and GEO?

SEO optimizes for ranking position in a list of links. AEO (answer engine optimization) optimizes for being selected as the direct answer to a question, inside or outside an AI system. GEO (generative engine optimization) is the broader umbrella term for optimizing visibility inside AI-generated responses across ChatGPT, Perplexity, Gemini, and Google’s AI features. In practice the three overlap heavily and the industry uses the terms inconsistently.

Should I stop doing traditional SEO and focus only on GEO?

No. The overlap between the two is large: crawlability, author expertise, content depth, and speed all still matter, and classic Search still drives the majority of most sites’ traffic even with AI Overviews appearing on a majority of results pages. Treat GEO-specific tactics as additive to a working SEO foundation, not a replacement for one.

How do I know if my traffic drop is an SEO problem or an AI visibility problem?

Check whether classic rankings for the affected keywords held steady while clicks dropped. If rankings held and clicks fell, an AI Overview is likely absorbing the click on that query, which is a GEO/CTR problem, not a ranking problem. If rankings themselves dropped, that is a classic SEO or algorithm-update issue and needs a different diagnostic path.

Does keyword density or exact-match title matching still work for AI search?

It works less well than it used to. AI systems answer a paraphrased version of the underlying question and match against the page that best covers the question cluster, not the page with the tightest exact-match keyword string. A page built narrowly around one keyword variant tends to lose relative ground in AI answers compared to a page structured around the full question set, even where it still ranks fine in classic Search.

Is there a real controlled study on backlinks versus mentions, or is this just correlation?

It is correlation, and the source itself says so explicitly. Ahrefs measured Spearman correlations across 75,000 brands, not a controlled experiment with a treatment and control group. The 0.664 versus 0.218 gap is a strong, consistent signal, but brands with high mention volume may also simply invest more broadly in brand-building overall, which the correlation does not separate out.

What should a small site with no PR budget prioritize first?

Author credibility and crawlability first, since those cost time, not budget, and move both classic rankings and AI citations. After that, look at E-E-A-T tactics achievable without a PR budget rather than chasing YouTube or major-outlet mentions that require scale a small site does not have yet.

Does llms.txt help you get cited by AI Overviews?

No. Google’s Gary Illyes confirmed in mid-2025 that Google Search does not use llms.txt, comparing it to the old keywords meta tag. Perplexity and Claude do consume it, so it is not worthless, but it will not move Google AI Overviews specifically. See do you need llms.txt in 2026 for the engine-by-engine breakdown.

How fast do AI citations decay compared to classic rankings?

Classic rankings, once earned through accumulated authority and links, tend to hold for months or years absent an algorithm update. AI citations appear to decay faster, driven by the freshness weighting discussed above; a page that held a citation for a query can lose it to a newer competitor within weeks, independent of any change in classic ranking position.

Which AI platforms weight these AI-only factors differently?

Meaningfully. Perplexity leans hardest on structured, question-formatted content and recency. ChatGPT favors Reddit, Wikipedia, and established news domains disproportionately. Google’s AI Overviews and AI Mode stay closest to classic ranking behavior since they draw from the same index. See Perplexity vs ChatGPT vs Gemini: who cites whom and why for the platform-level comparison.

Can a page rank #1 on Google and still get zero AI citations forever?

Yes, if it fails on the AI-only factors specifically: no named author, no unlinked brand presence, stale content, and prose-only formatting with no extractable question structure. None of those four failure modes affect classic ranking position, so the page can hold #1 indefinitely while remaining functionally invisible to every AI answer engine.

Where do most teams get this diagnosis wrong?

They run their AI visibility audit using their existing SEO checklist, which only tests the overlap factors (crawlability, speed, content depth) and never asks about brand mentions, YouTube presence, or freshness cadence separate from the normal content calendar. That produces a false “everything looks fine” result while the actual AI-only gaps go unmeasured.

What is the single highest-impact GEO-only action for most sites?

Based on the correlation strength alone, building genuine unlinked brand mentions across third-party sites, YouTube, and Reddit outranks every other AI-only factor measured, at 0.664 to 0.737 correlation versus 0.218 for backlinks. It is also the hardest one to fake or automate, which is part of why it correlates so strongly.

Key takeaways

Google’s “good SEO is good GEO” holds for author expertise, crawlability, content depth, and speed. These move both systems at similar strength, and they are where you should invest first regardless of which channel you are optimizing for.
Backlinks and schema markup carry over weakly or not at all for AI citation purposes. Ahrefs measured backlinks at 0.218 correlation versus 0.664 for unlinked mentions, and found no measurable citation lift from JSON-LD schema across 1,885 tracked pages.
Unlinked brand mentions, YouTube presence, Reddit visibility, and content freshness are the clearest AI-only levers in the current data, none of which a classic SEO checklist tests for by default.
Run your own 30-day controlled test on a subset of pages before trusting any single correlation study, including this one, as a universal prescription for your site.

If your team is already running keyword research and content production for classic SEO, the AI-visibility layer on top of it is a workflow problem more than a strategy problem: someone has to track citations separately from rankings, refresh content on a shorter cadence than the standard content calendar, and publish to the channels (multi-format, multi-platform) that the correlation data actually rewards. Vrid.ai builds that AEO layer into the same workspace as keyword research and AI article generation, so the freshness refreshes and question-formatted rewrites that move AI citations do not compete for a separate budget line from the SEO work that already has one.

Start with the decision tree above on your ten highest-traffic pages this week. The gap between what ranks and what gets cited rarely closes itself.