Do GEO Tactics Stop Working When Everyone Uses Them?

Every ranking of GEO tactics you have read shares an assumption: your competitors' pages hold still while you optimize yours. A preprint posted on 27 August 2026 by Vaibhav Sourirajan and colleagues at Capital One AI Foundations drops that assumption and re-runs the question. When the share of competing documents that are also optimized rises to 80%, every fixed single tactic they tested scores below the baseline of never having optimized at all.

Key takeaways

Since the KDD 2024 GEO paper reported that optimization "can boost visibility by up to 40%," the field has treated its tactic list — add statistics, cite sources, add quotations, improve fluency — as a ranked menu. The ranking came from a specific experimental shape: one document in a retrieval pool gets rewritten, the others stay as they were, and the change in the engine's answer is measured.

That was a reasonable design in 2023, when almost nobody was optimizing. It describes a land grab. The Capital One paper asks what the same tactics are worth once the land is taken, and its answer is that the ranking itself is unstable: the value of any edit depends on how many competing documents already carry it.

The intuition is familiar from every prior optimization discipline. If one page in five has extractable statistics and yours is it, the engine's preference for statistics is your advantage. If four in five do, the same edit buys parity at best. What the paper adds is machinery for measuring this and a demonstration of how sharp the falloff gets.

The method has two phases. First, for each query and document set, it runs Bayesian Optimization of Combinatorial Structures — BOCS — over the 2^15 = 32,768 possible on/off combinations of 15 rewriting strategies drawn from the published GEO literature, using 50 evaluations per search. The 15 include the KDD 2024 tactics (statistics addition, citing sources, quotation addition, fluency, authoritative tone, keyword stuffing) alongside later additions like structured formatting and LLM guidance from C-SEO Bench.

Second, because running BOCS per page is impractical, the observations from those searches become training data: preference pairs and reasoning traces that fine-tune a language model to look at a document corpus and propose a strategy combination directly.

Visibility is scored with PAWC — Position-Adjusted Word Count — the standard GEO-research metric that credits a document for the words the answer draws from it, discounted by position. On GEO-Bench with gpt-oss-120b as the engine:

The gap worth noticing is not the headline one. It is that the best single tactic — the one at the top of most practitioner checklists — sits five points below an agentic rewriter and nearly ten below combination search. The checklist's unit of action, the individual tactic, is the wrong granularity even before competition enters.

Then competition enters. The authors build geo-bench_comp, a variant in which a fraction α of the competing documents are themselves rewritten by simulated optimizers, and test α from 0 to 0.8 — from an unoptimized field to one where four of five competitors are running GEO.

Two results carry the paper. Averaged across adoption rates, the competitor-aware method scores 29.93 PAWC against 25.20 for the strongest baseline — an 18% edge. And at the high end, the fixed approaches do not merely lag: "single-strategy methods at full competitive optimization (α=0.8) drop below the unoptimized (α=0) baseline." Running the standard playbook in a saturated category simulated as worse than never having run it.

The competitor-aware selector avoids this by doing the thing its name says: it infers the adoption level from the documents it can see and shifts "toward less saturated alternatives as the competitive landscape is increasingly optimized." Its total degradation across the full adoption range is 11.4% — it bends where the fixed tactics break. The method also transferred out of distribution, scoring 37.78 on an e-commerce set and 33.48 on Researchy-GEO, more than 8% above the strongest baseline on each.

For anyone doing answer engine optimization, the mechanism matters more than the scores. Nothing about a statistics block or an FAQ became less legible to the engine at high α. What changed is that the edit stopped being a difference. Engines cite what stands out from the pool; an optimization everyone applies is, by definition, no longer what stands out.

The paper does not say, because it cannot — its competitors are synthetic. But the measurement exists elsewhere, and reconciling the two is where this becomes usable.

The CISPA-led GEO-Flag study, posted ten days earlier, put the first number on real-world adoption: 8.90% of pages retrieved for 1,000 real queries were GEO-optimized, rising to 16.36% among pages modified in 2026 — we covered it in our analysis of GEO prevalence. Against the simulation's axis, the measured web sits near α = 0.1, one page in six for recent content. The regime where fixed tactics go negative starts around α = 0.8. On the web average, the 2024 playbook is still operating in something like the vacuum it was measured in.

But the average is doing heavy lifting. The same study found 0% detected optimization on Wikipedia and major medical sites and 20% on sampled Amazon pages, and its year-over-year trend — 7% of 2024-modified pages, 12.8% for 2025, 16.4% for 2026 — points one direction. A competitive commercial category is plausibly years ahead of the mean, and it is precisely the categories where AI answers drive purchases that fill fastest.

That yields a decision rule the paper stops short of stating. The question "do GEO tactics work" has no fixed answer; the question "how optimized are the pages cited for my prompts" does, and it determines the first one. Pull the citations for your tracked prompts, assess how many are visibly running the playbook, and read your position off the curve: a quiet pool means the standard tactics still buy differentiation, a crowded one means they buy parity, and the trend line tells you how long the first state lasts.

The caveats here are structural, not fine print.

The competitors are simulated — three optimizer functions applied on a fixed rotation — and the engines are proxies: gpt-oss-120b and Llama-3.3-70B-Instruct, not the production systems answering buyers' questions. PAWC measures words drawn into an answer, which is an impression proxy, not a mention, a recommendation, or a sale. And the competitor-aware method holds what the authors concede is "an information advantage": it reads the entire competitive corpus before choosing a strategy. No publisher sees the retrieval pool the way this selector does — though tracking which pages engines actually cite for your prompts is the closest available approximation.

The largest omission sits in the faithfulness table. The winning method's rewrites scored 4.90 against 8.29 for unrewritten documents on the paper's LLM-judged scale — the visibility-optimal version of a page drifted furthest from what the page originally said. The authors log it as a trade-off; for a brand it is the whole risk. A rewrite that wins the citation by saying something your source material does not support becomes an engine confidently attributing that claim to you. This is the same failure mode the mechanism-design work on citation wars found accumulating over repeated optimization rounds, and the same decoration-grade citations the GEO-Flag audit found on 69% of detected pages. Three independent groups, three methods, one convergent result: pushed hard enough, visibility optimization manufactures unsupported claims.

There is also a quieter irony worth holding: a paper about competitors adapting to each other is itself now public. A tactic selector that wins by finding "less saturated alternatives" describes an equilibrium that moves again the moment enough parties run one.

The honest summary is that GEO tactics work the way arbitrage works: the return is real, and it is a function of how many others have found the trade. The KDD 2024 measurements described an empty field, the CISPA measurements say the field is one-sixth full and filling, and this paper simulates the far end where the standard moves pay less than nothing. The practitioner's job shifts accordingly — from running a checklist to knowing where on that curve their category sits, which is a measurement question before it is a tactics question.

Elmo is an open-source, self-hosted AI visibility platform that runs your prompt sets across ChatGPT, Claude, Gemini, Perplexity, and Google's AI surfaces and records every answer with its citations and competitor mentions. Because the cited pages for each prompt are in your own database, the adoption-rate check this research calls for is a query, not a project — and re-running it each month tells you when your category's curve starts to bend.

For the fundamentals, start with what AI citations are and where they come from. For the adjacent research, see how much of the web is GEO-optimized and what happens when platforms fight back. For the practice, the AEO guide and how to track your brand in AI search. For the vocabulary, the AI search glossary.