What each term actually refers to
The vocabulary is newer than the work, and the terms were coined by different people selling different things, which is why they overlap so untidily.
| Term | What it optimises for | Where it came from |
|---|---|---|
| SEO | Position in a ranked list of links | Twenty-five years of practice around crawlers and rankings |
| GEO, generative engine optimization | Being drawn on when a model generates an answer | Academic and vendor writing about generative search results |
| AEO, answer engine optimization | Being the source an answer engine cites | The assistant and voice-answer side of the same shift |
| LLM SEO / AI SEO | The same work, named for the model or the umbrella | Practitioners renaming their existing service line |
GEO and AEO describe nearly the same activity from two angles, and no measurable difference in the work follows from choosing one label over the other. SEO is the one that genuinely refers to something else, which is what makes GEO vs SEO the comparison worth making carefully.
GEO vs SEO, side by side
The row that explains all the others is the reader. Everything below follows from a parser having replaced a browser at the moment your page is consumed.
| SEO | GEO | |
|---|---|---|
| Who reads the page | A crawler, then a human in a browser | A model, under a token budget, mid-conversation |
| What you are competing for | A position among ten links | Inclusion in one composed answer |
| When the visit happens | On the crawler’s schedule | Seconds after a user asked something |
| What layout contributes | Engagement, dwell time, conversion | Cost, paid in context before your facts |
| Client-rendered content | Often rendered and indexed | Absent entirely from the response |
| Response size | A speed signal | A hard limit on how much of you is read |
| Keyword placement | Still carries some weight | Nothing to act on, there is no position |
| How you see results | Rank tracking, search console | Your own request log, agent by agent |
| Time to feedback | Weeks | One request |
What carries over unchanged
This is the part vendors underplay, because a large shared foundation is a poor argument for a separate budget. In practice, a site with genuinely good SEO starts GEO from a strong position.
| Transfers unchanged | Stops counting | Newly load-bearing |
|---|---|---|
| Crawlability and fast, stable responses | Keyword density and heading stuffing | Answering Accept: text/markdown |
| Titles that state the page’s subject | Layout, visual hierarchy, above the fold | Response size, measured in tokens |
| Internal linking that reaches every page | Word-count targets | How deep the first core fact sits |
| Accurate structured data where a spec exists | Anything that appears only after JavaScript | Bot rules in the CDN and the WAF |
| Topical authority and real citations | Interstitials, carousels, modals | A curated index at /llms.txt |
The middle column is not neutral, it is expensive. Every kilobyte of layout markup is context spent before your facts arrive, and anything rendered client-side is simply missing. A page that looks excellent in a browser and scores badly for GEO is the normal case rather than an anomaly.
Where AEO fits: SEO vs GEO vs AEO
Three-way comparisons are the most common way this question gets asked, and the honest shape of the answer is two against one rather than three equal columns.
- SEO targets a ranked list. Different surface, different success condition, largely shared foundations.
- GEO targets inclusion in generated output, and is usually written about from the model’s perspective.
- AEO targets being the cited source of an answer, and is usually written about from the assistant’s perspective. The AEO guide has the method.
GEO and AEO prescribe the same fixes in the same order, so treat the pair as one workstream. If someone proposes separate GEO and AEO engagements with separate deliverables, ask which fix appears in one and not the other. There generally is not one.
KPIs for GEO vs SEO
Reporting is where the two disciplines separate most sharply, because the instruments barely overlap. A page-view script cannot see the fetch side of GEO: agent fetches run no JavaScript and fire no beacon, so that traffic is structurally invisible to the tool most teams report from. The one GEO number a script can carry is the referred half, AI-referred human sessions, which the free Rebilder Tag counts from a single pasted line. Every fetch-side KPI below comes from the server.
| SEO KPI | GEO equivalent | Where it comes from |
|---|---|---|
| Impressions | Agent fetches, by platform | Your own request log |
| Click-through rate | Answer rate: fetches you served vs missed | The gateway’s miss log |
| Average position | No equivalent exists | There is no position to hold |
| Crawl errors | Blocked or challenged agent requests | Bot rules, CDN and WAF logs |
| Page speed | Response size in tokens, first-fact offset | A readability score against a published spec |
| Sessions and conversions | AI-referred sessions and their conversion | Referrer analysis on human follow-through |
The row worth staring at is the third. There is no average position in a generated answer, and any dashboard that shows you one has invented it. The nearest honest substitute is the miss log, which names the URLs agents asked for and did not get, in the order the market is asking for them.
- Conversion lift, AI-referred visitors vs search
- ~38%
- Cyber Week 2025 retail data
- Content negotiation vs llms.txt, accurate retrieval
- ~4.2x
- 300k-domain study, mid-2026
What the GEO half is worth, measured
Two reference captures, both deterministic and rebuilt from committed fixtures on every build. They are quoted separately rather than averaged, because a product page and a services page carry different amounts of chrome.
| Capture | Before | After | First fact moves |
|---|---|---|---|
| Commerce product page | 91,226 bytes | 1,315 (−98.6%) | Character 23,185 → line 4 |
| Clinic services page | 13,581 bytes | 1,412 (−89.6%) | Character 10,152 → line 8 |
Roughly 69× less context on the commerce capture and 9.7× fewer tokens on the document one, for identical facts. Nothing in either capture changes what a search crawler receives, which is the concrete version of the claim that the two disciplines do not trade against each other.
How to split effort between them
The GEO side is unusually front-loaded: most of its value comes from a small amount of serving-layer work that is done once, not from an ongoing content programme. That makes the split easier than the framing suggests.
- Score a baseline first. One page per template, against a published spec, before you change anything. Without it you cannot attribute a later result to either discipline.
- Do the serving-layer work once. Content negotiation, bot rules, first-fact position, a generated index. This is the whole of GEO’s expensive part and it does not recur.
- Keep the SEO programme running unchanged. It still sends most of the traffic on most sites, and none of the above competes with it.
- Report the two separately. Search console for one, your request log for the other. Merging them into a single traffic number hides which one is working.
- Let the miss log set the content queue. URLs agents asked for and did not get are the only list that serves both disciplines at once.