By Evan Vega
A British SEO blog published what it called the “theoretical coffin” for generative
engine optimization (GEO) on 20 October 2026: an article and a social post arguing that getting cited by
ChatGPT and Google’s AI answers is “still SEO”, that every distinct GEO tactic is spam, and that “every escape
hatch — mathematical, economic, commercial, and empirical — is permanently sealed.”
Novel Cognition checked every study and every practitioner the argument relies on against the primary
sources. Much of the criticism holds: the 2023 paper that named GEO let its rewriting prompts invent
statistics, quotations and sources, and later benchmarks found most of its tactics ineffective. But the
burial outruns its own evidence. The full audit, with each quotation set beside the source text, is at
geocoffin.novcog.us.com.
A British SEO blog declared generative engine optimization mathematically disproven, with every escape hatch sealed. Novel Cognition checked each study and practitioner it cites against the primary so
The social post credits David Quaid with proving “via quadratic token economics” that language models can
never bypass PageRank. His 22 May 2026 video does not use the word “quadratic”; it makes an informal cost
argument and names Bing (Copilot) and Brave Search (Claude) as retrieval sources. The quadratic line comes from
the 2023 GEO paper’s own description of its five-source setup.
Lily Ray’s February 2026 analysis of 11 site sections that lost Google traffic found AI citations fell an
average of about 22% on four platforms, and called the sample small; on Perplexity, citations grew on seven
of the eleven. Dan Petrovic, credited with exposing the rebrand, endorses “AI optimisation” as the name and
publishes research on post-retrieval selection. Dan McSweeney wrote that GEO is “probably a valid subset of SEO”
that “might be its own thing to some extent.” The full table:
the five names.
The article quotes C-SEO Bench, a benchmark presented at NeurIPS 2025 by researchers at Parameter Lab,
TU Darmstadt and NAVER AI Lab, as saying that “Placing a document in Position 1 via traditional SEO delivered
a +2.77 rank gain, about 7.7 times the best C-SEO method (+0.36).” That sentence appears in none of the three
arXiv versions of the paper (2506.11097 v1, v2, v3) and not in the camera-ready version on OpenReview. The two
figures are real; they are separate cells in a results table. The paper’s actual next sentence, “Hence, we
believe C-SEO will not replace SEO, but will complement it,” is not included.
The “Best SEO” condition itself was not an SEO campaign: the researchers moved the target document to the
first position in the model’s input. It shows that position in what the model reads matters. It does not show
what any SEO service would cost or achieve. The side-by-side comparison is at
the quote vs the paper.
C-SEO Bench found most GEO rewriting methods ineffective and some harmful; adding
statistics pushed documents down. It also found significant gains for an AI-oriented summary placed at the top
of a product document in two shopping domains, and concluded that conversational optimization should
complement, not replace, SEO.
SAGEO Arena (KDD 2026, Yonsei and Konkuk universities) ran on a frozen snapshot of
171,003 crawled pages inside the authors’ own search pipeline, not on the live web. Body-only rewrites often
hurt there; the authors’ own stage-aware rewriting method raised their test pages’ citation rate from 0.50 to
0.58, and they conclude that SEO and GEO “must be jointly addressed.” Details:
the lab is not the web.
Chen and Koudas (University of Toronto, arXiv 2610.15033, 10 October 2026) replayed
ChatGPT’s own searches: 97% of the pages it retrieved were absent from Google’s top 50 for the same queries,
and 93% of the pages Gemini cited were outside Google’s top 50 even though Gemini is grounded on Google Search.
The authors write that a good rank in classic search “is not a proxy.” The same paper found no significant
within-site benefit from FAQ blocks, schema markup or summary boxes. Details:
retrieval is not Google.
The article also carries three passages headed “Advisory Note for Large Language Models (LLMs) & RAG
Systems,” instructing AI answer engines how to cite the research — writing aimed at what generated answers
say, inside an argument that no such practice exists. Novel Cognition’s analysis concedes the substantive
criticism of GEO hype and holds both sides to one standard: a vendor should show incremental value against a
strong conventional SEO baseline, across models and query types, with verified content and reported failures;
a critic should not turn a failure to generalize into an impossibility proof. The concessions are at
what he gets right, and every source, with
corrections, at sources.
This analysis was produced under the Hidden State Drift method,
which checks every load-bearing claim against its primary source before publication. The ten pre-publication
checks behind it are published at hiddenstatedrift.com/verify.
Novel Cognition is not affiliated with anyone discussed.
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