Generative engine optimization (GEO) is sold with confident numbers. A July 2026 survey of the research behind it is more cautious. The strongest evidence applies to content an AI engine has already retrieved, not to whether it finds you in the first place.
- The original GEO paper, accepted to KDD 2024, reported visibility gains of up to 40% on its GEO-bench benchmark.
- A July 2026 survey by Olivier Martinez, covering 45 studies, found those gains hold in a fixed-context setting and do not show organic discoverability or lasting traffic effects.
- The survey found topical relevance and context position to be the most reproducible levers, while generic heuristics transfer poorly.
- Treat any GEO promise of guaranteed citations as unproven, and ask for repeated, controlled measurement.
Where the 40% figure comes from
GEO (generative engine optimization): the practice of increasing how often and how prominently your content appears in answers from generative search engines. The term was introduced by Pranjal Aggarwal and colleagues in a paper accepted to KDD 2024, a major data-mining conference.
The authors built GEO-bench, a collection of varied user queries across several domains, each paired with web sources able to answer them. Their abstract states that GEO methods can boost visibility in generative engine responses by up to 40%, and that effectiveness varies by domain.
Vendors often quote the 40% without the qualifier. The phrase is “up to,” the setting is a benchmark and the measure is visibility inside a generated response. That is a real result, but it is narrower than the way it is usually repeated.
What the 2026 survey concluded
On July 15, 2026, Olivier Martinez posted a critical survey to arXiv covering 45 studies published between November 2023 and July 2026. It frames GEO as a stochastic, partially observable pipeline: search activation, crawling, retrieval, reranking, citation and user behavior, each of which can change the outcome.
The central finding is a distinction. The original gains hold when a source is already in the engine’s context, so they do not show that a page will be found or that traffic will follow. The survey reports that no reviewed technique showed a stable, longitudinal, cross-platform causal effect on organic discoverability or downstream behavior, and it calls the overall evidence narrow.
This summary draws on the paper’s abstract and landing page, so per-technique effect sizes are not repeated here. Readers who rely on a specific number should consult the full text.
Which levers hold up
The survey does not say GEO is worthless. It says which findings travel and which do not. The table separates them in plain terms.
| Finding in the survey | What it supports | What it does not support |
|---|---|---|
| Topical relevance and context position are the most reproducible levers | Writing tightly on-topic pages with the key answer early | Any claim that formatting alone creates citations |
| Generic heuristics transfer poorly across settings | Testing on your own queries and engines | Universal checklists that promise results |
| Competition can erode individual gains | Expecting returns to shrink as rivals adopt the same tactics | Permanent advantage from a one-time rewrite |
| Citation-oriented rewrites can impair retrieval | Caution before rewriting for style alone | Assuming every rewrite helps |
The survey also reports high run-to-run variability in commercial audits, which is why our guide on measuring AI search visibility recommends repeated checks, not single screenshots.
What this means for your budget
The practical reading is to spend on what is both durable and testable. Clear, well-sourced pages that answer the question early serve classic search and AI engines alike, which is the argument made in our comparison of GEO, AEO and SEO.
Be more skeptical of tactics sold on a single mechanism. Our reviews of llms.txt and content freshness apply the same test: what was measured, on what sample, and does it show a causal effect.
When a vendor presents GEO results, ask four things.
- Which engines and queries were tested, and how many repeated runs were used?
- Were paraphrased queries and a control group included?
- Does the result measure citation, or traffic and revenue?
- Would the work still be justified if citation rates did not change?
Frequently Asked Questions
Does GEO work?
It works in the narrow sense that changes to already-retrieved content can change how it is cited. The 2026 survey found no technique with a proven lasting effect on being discovered or on traffic.
Is the 40% visibility gain a reliable benchmark?
It is the upper bound reported in the original paper’s benchmark tests. The authors also found effectiveness varied by domain, so it should not be read as an expected result for a given site.
What should a business do while the evidence is thin?
Invest in accurate, specific, well-structured content that helps readers, and measure citations and traffic over repeated runs. Avoid paying for guaranteed outcomes that no published study supports.
Thin evidence is a reason for disciplined testing, not for ignoring AI search. At Grow with Hafsa we help teams plan GEO and AI marketing programs around measurable pilots, and we are happy to look at which of your current claims the research actually backs.