Google’s Mueller Slams ‘Stupid’ Markdown-for-Bots Proposal
▼ Summary
– Some developers propose serving raw Markdown files to AI crawlers to drastically reduce token usage, but Google’s John Mueller strongly opposes this idea.
– Mueller raised technical concerns, questioning if crawlers can properly parse Markdown files and follow links, and warned it could harm a site’s internal linking structure.
– He and other experts argue that stripping a page down to Markdown removes important context and structure, and there’s no evidence LLMs favor such content.
– Data from an analysis of 300,000 domains shows no connection between using bot-specific formats (like llms.txt) and how often a site is cited by LLMs.
– The recommended best practice remains to provide clean HTML, minimize blocking JavaScript, and use documented structured data, not bot-only content copies.
The debate around creating specialized content for AI crawlers is heating up, with a recent proposal to serve raw Markdown files facing significant criticism. Google Search Advocate John Mueller has strongly opposed the concept, labeling it as a fundamentally flawed approach that could harm a website’s visibility and structure rather than help it. His comments highlight a growing tension between developers seeking to optimize for new technologies and the established principles of search engine optimization.
The discussion began when a developer shared a technical strategy on an online forum. The plan involved using a web framework’s middleware to identify visits from specific AI crawlers, like those from OpenAI or Anthropic. Upon detection, the system would intercept the request and deliver a stripped-down Markdown file instead of the standard HTML page. The developer reported preliminary data showing a massive reduction in computational tokens used per page, suggesting this could allow sites to be processed more efficiently by systems designed for retrieval-augmented generation.
Mueller responded with a series of pointed technical questions. He expressed doubt about whether these AI bots are even configured to interpret a Markdown file delivered via a website as meaningful content, as opposed to a simple text download. He questioned if the bots could properly parse and follow hyperlinks within the Markdown, and what the impact would be on essential site elements like navigation, headers, and footers. His core argument was that manually providing a Markdown file is entirely different from automatically serving one when a bot expects a fully structured HTML document.
In a separate conversation on another social platform, Mueller’s critique was even more blunt. Replying to an SEO expert who noted that flattening pages into Markdown strips away crucial semantic meaning and layout, Mueller sarcastically suggested taking the flawed logic to its extreme. He quipped that since large language models can interpret images, one might as well convert an entire website into a single picture. The SEO consultant, Jono Alderson, supported this view, arguing that the proposal sacrifices important context for mere convenience and does not represent a sustainable long-term strategy.
Other professionals in the initial forum thread raised similar alarms. One commenter pondered whether this tactic might actually restrict how thoroughly a site is crawled, rather than improving it. They emphasized a lack of any public evidence showing that AI models are trained to prioritize or reward content that is less resource-intensive to process. The original poster countered by stating that LLMs, trained extensively on code repositories like GitHub, are inherently better at parsing Markdown than HTML, a claim that remains unproven in this specific application.
This stance from Mueller is not a new one. He has consistently advised against creating separate content versions specifically for AI bots. In a prior exchange, when asked about building dedicated Markdown or JSON pages for large language models, he gave the same counsel. His recommendation remains to focus on producing clean, accessible HTML and implementing structured data using documented schemas, rather than maintaining duplicate content for different crawlers.
This advice is supported by independent research. An analysis of hundreds of thousands of domains found no correlation between the use of a specialized `llms.txt` file and how frequently a domain was cited in AI-generated answers. Mueller has previously compared such bot-specific files to the largely obsolete keywords meta tag, a format that major platforms never officially supported for ranking. To date, no leading AI company has published documentation requesting or recommending that websites provide Markdown versions of their pages to improve indexing or citation rates.
For website owners and developers, the path forward remains clear. The most reliable strategy is to adhere to foundational web best practices. This means ensuring HTML code is clean and semantic, minimizing unnecessary JavaScript that can block content from being parsed, and using structured data where its use is explicitly documented by platforms like Google. Chasing speculative optimizations for unproven crawlers often distracts from the core work of building a robust, universally accessible website. Until an AI platform formally specifies a need for alternative formats, creating them is likely an inefficient use of resources that could compromise a site’s integrity for all visitors, both human and bot.
(Source: Search Engine Journal)
