Google’s Mueller Calls Markdown-For-Bots Idea ‘A Stupid Idea’ via @sejournal, @MattGSouthern

Some developers have been experimenting with bot-specific Markdown delivery as a way to reduce token usage for AI crawlers.

Google Search Advocate John Mueller pushed back on the idea of serving raw Markdown files to LLM crawlers, raising technical concerns on Reddit and calling the concept “a stupid idea” on Bluesky.

What’s Happening

A developer posted on r/TechSEO, describing plans to use Next.js middleware to detect AI user agents such as GPTBot and ClaudeBot. When those bots hit a page, the middleware intercepts the request and serves a raw Markdown file instead of the full React/HTML payload.

The developer claimed early benchmarks showed a 95% reduction in token usage per page, which they argued should increase the site’s ingestion capacity for retrieval-augmented generation (RAG) bots.

Mueller responded with a series of questions.

“Are you sure they can even recognize MD on a website as anything other than a text file? Can they parse & follow the links? What will happen to your site’s internal linking, header, footer, sidebar, navigation? It’s one thing to give it a MD file manually, it seems very different to serve it a text file when they’re looking for a HTML page.”

On Bluesky, Mueller was more direct. Responding to technical SEO consultant Jono Alderson, who argued that flattening pages into Markdown strips out meaning and structure,

Mueller wrote:

“Converting pages to markdown is such a stupid idea. Did you know LLMs can read images? WHY NOT TURN YOUR WHOLE SITE INTO AN IMAGE?”

Alderson argued that collapsing a page into Markdown removes important context and structure, and framed Markdown-fetching as a convenience play rather than a lasting strategy.

Other voices in the Reddit thread echoed the concerns. One commenter questioned whether the effort could limit crawling rather than enhance it. They noted that there’s no evidence that LLMs are trained to favor documents that are less resource-intensive to parse.

The original poster defended the theory, arguing LLMs are better at parsing Markdown than HTML because they’re heavily trained on code repositories. That claim is untested.

Why This Matters

Mueller has been consistent on this. In a previous exchange, he responded to a question from Lily Rayabout creating separate Markdown or JSON pages for LLMs. His position then was the same. He said to focus on clean HTML and structured data rather than building bot-only content copies.

That response followed SE Ranking’s analysis of 300,000 domains, which found no connection between having an llms.txt file and how often a domain gets cited in LLM answers. Additionally, Mueller has compared llms.txt to the keywords meta tag, a format major platforms haven’t documented as something they use for ranking or citations.

So far, public platform documentation hasn’t shown that bot-only formats, such as Markdown versions of pages, improve ranking or citations. Mueller raised the same objections across multiple discussions, and SE Ranking’s data found nothing to suggest otherwise.

Looking Ahead

Until an AI platform publishes a spec requesting Markdown versions of web pages, the best practice remains as it is. Keep HTML clean, reduce unnecessary JavaScript that blocks content parsing, and use structured data where platforms have documented schemas.

AI Recommendations Change With Nearly Every Query: Sparktoro via @sejournal, @MattGSouthern

AI tools produce different brand recommendation lists nearly every time they answer the same question, according to a new report from SparkToro.

The data showed a <1-in-100 chance that ChatGPT or Google>

Rand Fishkin, SparkToro co-founder, conducted the research with Patrick O’Donnell from Gumshoe.ai, an AI tracking startup. The team ran 2,961 prompts across ChatGPT, Claude, and Google Search AI Overviews (with AI Mode used when Overviews didn’t appear) using hundreds of volunteers over November and December.

What The Data Found

The authors tested 12 prompts requesting brand recommendations across categories, including chef’s knives, headphones, cancer care hospitals, digital marketing consultants, and science fiction novels.

Each prompt was run 60-100 times per platform. Nearly every response was unique in three ways: the list of brands presented, the order of recommendations, and the number of items returned.

Fishkin summarized the core finding:

“If you ask an AI tool for brand/product recommendations a hundred times nearly every response will be unique.”

Claude showed slightly higher consistency in producing the same list twice, but was less likely to produce the same ordering. None of the platforms came close to the authors’ definition of reliable repeatability.

The Prompt Variability Problem

The authors also examined how real users write prompts. When 142 participants were asked to write their own prompts about headphones for a traveling family member, almost no two prompts looked similar.

The semantic similarity score across those human-written prompts was 0.081. Fishkin compared the relationship to:

“Kung Pao Chicken and Peanut Butter.”

The prompts shared a core intent but little else.

Despite the prompt diversity, the AI tools returned brands from a relatively consistent consideration set. Bose, Sony, Sennheiser, and Apple appeared in 55-77% of the 994 responses to those varied headphone prompts.

What This Means For AI Visibility Tracking

The findings question the value of “AI ranking position” as a metric. Fishkin wrote: “any tool that gives a ‘ranking position in AI’ is full of baloney.”

However, the data suggests that how often a brand appears across many runs of similar prompts is more consistent. In tight categories like cloud computing providers, top brands appeared in most responses. In broader categories like science fiction novels, the results were more scattered.

This aligns with other reports we’ve covered. In December, Ahrefs published data showing that Google’s AI Mode and AI Overviews cite different sources 87% of the time for the same query. That report focused on a different question: the same platform but with different features. This SparkToro data examines the same platform and prompt, but with different runs.

The pattern across these studies points in the same direction. AI recommendations appear to vary at every level, whether you’re comparing across platforms, across features within a platform, or across repeated queries to the same feature.

Methodology Notes

The research was conducted in partnership with Gumshoe.ai, which sells AI tracking tools. Fishkin disclosed this and noted that his starting hypothesis was that AI tracking would prove “pointless.”

The team published the full methodology and raw data on a public mini-site. Survey respondents used their normal AI tool settings without standardization, which the authors said was intentional to capture real-world variation.

The report is not peer-reviewed academic research. Fishkin acknowledged methodological limitations and called for larger-scale follow-up work.

Looking Ahead

The authors left open questions about how many prompt runs are needed to obtain reliable visibility data and whether API calls yield the same variation as manual prompts.

When assessing AI tracking tools, the findings suggest you should ask providers to demonstrate their methodology. Fishkin wrote:

“Before you spend a dime tracking AI visibility, make sure your provider answers the questions we’ve surfaced here and shows their math.”

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