What is GEO, and how much of it is a rebranded SEO upsell?

The term came from a paper, not from an agency

Generative engine optimization is the practice of making a page more likely to be quoted inside an AI-generated answer.

The name comes from a 2024 paper. Six researchers led by Pranjal Aggarwal presented GEO: Generative Engine Optimization at KDD, the ACM's data mining conference.

They built a benchmark of about 10,000 queries. Then they rewrote source pages seven different ways and measured which versions the engines quoted.

The strongest methods were unglamorous. Adding citations, direct quotations, and statistics lifted visibility in generated answers, with the best results reaching about 40%.

Keyword stuffing performed worse than doing nothing.

Two details rarely survive the retelling. That 40% is a ceiling rather than an average. And low-ranked sources gained the most, which is a finding about equalization rather than a promise to everyone.

Still, the paper named something true. A page carrying verifiable facts is easier for a machine to quote than a page carrying adjectives.

The corrective arrived a year later

In 2025 a second team tested the same idea more harshly. Haritz Puerto and four co-authors built C-SEO Bench and presented it at NeurIPS.

They ran nine of these optimization methods across six domains and two tasks. Most were largely ineffective. Several pushed a document down rather than up.

What dominated instead was retrieval ranking. The pages the search layer surfaced first were the pages the model leaned on. That is the same lever search professionals have worked for two decades.

Then comes the finding that matters most for anyone holding a proposal. The benchmark modeled what happens as more competitors adopt these tactics. The advantage shrinks toward zero as adoption rises.

A tactic whose benefit disappears when everyone uses it is a poor thing to buy on a subscription.

Three layers of what gets sold

The first layer is evidenced and mostly free. Your pages have to be crawlable, indexed, and readable to the assistants.

They also have to carry facts a machine can lift and check. Other credible sites have to mention you. That is the whole evidenced core.

The second layer is plausible and unproven. Answer-first page structure, quarterly refreshes of aging pages, schema markup beyond what earns rich results.

None of it will hurt you, and none of it has a controlled study behind it. Do the cheap items and wait on the expensive ones.

The third layer is where the invoice grows. Proprietary readiness scores, promised citations, and machine-readable files invented for the purpose.

The clearest exhibit in that third layer is llms.txt. It is a file you publish to tell AI systems what your site contains.

Ahrefs checked the server logs of 137,210 domains. It found that 97% of published files received no requests in a month.

The detail underneath is better. Of the requests that did arrive, a slice came from GEO audit tools checking whether the file existed.

Part of the market for llms.txt is the industry inspecting its own compliance with a standard almost nothing reads.

Google's John Mueller has compared the file to the keywords meta tag. That was the self-reported signal search engines stopped trusting fifteen years ago.

Google's own guidance says no machine-readable file is needed to appear in its AI features.

The acronym has three other names

You will also see answer engine optimization, large language model optimization, and AI search optimization.

No settled taxonomy separates them. In practice the terms describe the same approach. All of them mean structuring content so an AI system can find it, trust it, and quote it.

A category with four names and no settled definitions is still deciding what it sells. It is also why two proposals using the same word can describe entirely different work.

The measurement layer is where the money hides

The dashboard is usually the most expensive part of the package. It measures something that moves on its own.

SE Ranking ran 10,000 keywords through Google's AI Mode three times in one day. The average overlap of exact URLs across the three runs was 9.2%.

For about a fifth of the keywords, not one URL repeated.

That measurement dates from August 2025 and the surfaces have changed since. The instability has not.

So a visibility score built on one run of a prompt set is reporting noise as progress. Two tools quoting the same brand on the same day can disagree widely, and both can be honestly built.

Google adds its own warning. No third-party tool has access to its ranking or AI systems, whatever a dashboard suggests.

The numbers that survive all this are the ones attached to people. Who arrived, who asked, and who mentioned where they first heard your name.

What to ask before you pay for it

Three questions sort most proposals. They belong in the same conversation as everything else you ask a provider before signing.

Ask which layer each deliverable sits in, and what evidence puts it there. A vendor who cannot answer has not read the research they are invoking.

Ask how many runs sit behind each number in the report. One run of a prompt is a coin toss with the result hidden.

Ask what happens to your advantage when competitors buy the same package. The answer is that it shrinks. A vendor who says otherwise is describing a world the benchmarks do not find.

What the label gets right

The term is not empty, and treating it as pure marketing would be its own error.

Writing for rank and writing to be quoted are different jobs. A page can rank well and still give a model nothing to lift.

It carries no figure, no attributable line, and no statement of what the writer knows. A machine reading it finds nothing it can stand behind.

For an expert practice, closing that gap means putting your own evidence on the page.

That means the number from your files, and the year you started doing this. It means the thing you tell clients that contradicts the textbook.

GEO named that gap while much of the industry was still arguing about whether AI answers mattered. That is worth crediting, and it will outlast the acronym.

The rest of the case for a separate program rests on the work diverging. That is a claim you can check line by line.

What the evidence supports, stated once

The core of GEO is small. Be readable to the crawlers. Publish pages carrying facts and figures a machine can verify.

Be mentioned by other people who are credible.

Every one of those is free or close to it. Every one is work you can do or direct yourself.

The expensive layer sits on top, and the published research has not shown that it moves anything. When somebody quotes a monthly figure for it, they are pricing the acronym rather than the evidence.