Semrush put out an infographic last week. The kind built to be screenshotted into LinkedIn carousels and pasted into webinar decks. Four pillars. The fourth one is called “Technical GEO”: schema, structured data, clean architecture. The line that justifies it: “Ensures AI engines can parse and connect your content.”
Ensures.
That is the entire piece in one word. The architecture of large language models is, by design, the opposite of ensured. And schema has nothing to do with whether an LLM can parse text. LLMs parse text by reading text.
Semrush is far from alone. Every SaaS vendor with skin in this game is running variations of the same play. SEO-era controllability, repackaged under a new acronym. The same percentages, pillars, and pyramids. All dressed for a system that was built specifically not to work this way.
I have made the strategic version of this case before, in “Your AI Strategy Isn’t a Strategy.” This piece is the technical floor underneath it.
Built To Read Whatever’s There
Language models exist because the web is a mess. Forums, Wikipedia stubs, blog posts written at 2 A.M., scraped product copy, machine-translated junk, code comments, half-formed sentences, typos, contradictions, every register from journal article to subreddit shitpost. Pre-training data is the public web, and the public web has never been structured.
The transformer architecture handles this by treating language as sequences of tokens. There is no parser inside the model looking for
At inference time, the model generates more tokens conditioned on the input. None of that pipeline is reading microdata.
Schema.org has real jobs. It feeds rich results in classical search. It supports entity disambiguation in the knowledge graph. It helps voice assistants pull structured fields. These are well-defined functions inside specific systems. They are not the mechanism by which an LLM understands a sentence.
So when a vendor claims structured data “ensures AI engines can parse and connect your content,” there is nothing to ensure. The parsing layer they are imagining is not there. The model already parsed your sentence. It did so by reading the sentence.
One Trick, Three Brand Colors
Look at the biggest GEO and AEO explainers in the market right now, and you find the same SEO-era playbook with the acronym swapped.
Semrush is already covered. The fourth pillar of its “Technical GEO” presents schema and structured data as ensuring something that the architecture cannot ensure.
AirOps published a graphic titled “15 Ways to Get Cited by ChatGPT, Perplexity, & Google.” It is the most numbers-heavy specimen of the genre I have seen this year. Schema markup increases citation likelihood by 13%. Sequential H2 to H4 tags double your chances. Short paragraphs make content 49% more likely to appear in AI…
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