Google Reveals the Core Problem with LLMs.txt

If you’ve spent any time in SEO or content circles over the past year, you’ve almost certainly heard about LLMs.txt — a proposed standard file meant to help AI systems understand a website’s content more easily. Plenty of site owners have rushed to implement it, treating it as a shortcut to better visibility in AI-generated answers. According to Google, that rush is based on a fundamental misunderstanding of what the file was ever meant to do.

In a recent episode of Google’s Search Off The Record podcast, Search Advocate John Mueller and Martin Splitt addressed LLMs.txt directly — and the explanation exposes a gap between what the SEO industry assumed about the standard and what its own creator intended.

What LLMs.txt Was Actually Built For?

Mueller revealed that he’d spoken directly with one of the people behind the original LLMs.txt proposal, and the explanation contradicts how most of the industry has been using it. According to that conversation, LLMs.txt was never designed to make a site easier for search engines or LLM systems to discover — it was conceived more as a reference that an LLM could consult if it already knew about a site and wanted to learn more about what else was there.

That distinction matters more than it might first appear. Mueller was blunt that using LLMs.txt as a discovery-optimisation tactic for AI systems or search systems doesn’t make sense at all. In other words, the exact use case most publishers have been pursuing — getting found and cited by AI — was never the problem LLMs.txt was built to solve.

Why Discovery Doesn’t Work the Way People Assume?

To understand why this distinction matters, it helps to understand how search engines actually find and rank content in the first place. The process runs through five stages: discovery, crawling, indexing, ranking, and serving. Discovery — the step where a search engine first becomes aware a page exists — happens at the very beginning of that pipeline, and it happens through standard, crawlable HTML.

LLMs.txt doesn’t participate in that pipeline at all. A file sitting on your server telling an AI system what your site contains doesn’t get crawled the way your actual pages do, and it isn’t part of how discovery has ever worked. This is the same foundational principle that underpins technical SEO — visibility starts with making sure search systems can actually find and parse your content through the mechanisms they already use, not through a separate file hoping to shortcut that process.

The Trust Problem Nobody Talks About

There’s a second, arguably bigger issue Mueller raised: even if LLMs.txt did influence discovery, AI systems would have good reason not to trust it. He pointed out that an LLMs.txt file is fundamentally just a site owner telling AI systems how great their own website is and which pages everyone should visit — which means, by design, an AI system has no reliable way to use that file to differentiate one website’s claims from another’s.

This is a problem self-declared metadata has always faced in SEO. Keyword stuffing in meta descriptions, exaggerated claims in title tags, and manipulated schema markup all ran into the same wall years ago: a system can’t safely rank or cite content based on what a site says about itself, only on what it can independently verify. LLMs.txt, as currently proposed, asks AI systems to take that leap of faith — and they’re not going to.

Where LLMs.txt (and Similar Standards) Might Actually Help?

Mueller didn’t dismiss the broader category of machine-readable site standards entirely — he just drew a clear line around where they might be useful. He suggested that once a user or AI agent is already on a specific website, having some kind of automated guidance available could genuinely help — his example was an AI agent navigating a photography website and finding agent-specific guidelines for completing a purchase.

That’s a meaningfully different use case from discovery. Mueller connected this to a different and likely more promising standard, the Web Model Context Protocol (WebMCP), which appears better suited to that kind of in-session, agentic interaction than LLMs.txt does. The distinction is between being found (which still runs through HTML) and being usable once an AI agent has already arrived — two very different problems with two very different solutions.

What This Means for Your SEO Strategy

If you’ve already implemented LLMs.txt hoping it would boost your visibility in AI search results, this isn’t a reason to panic — but it is a reason to recalibrate expectations. The file itself isn’t harmful to have, but it shouldn’t be treated as a substitute for the fundamentals that actually drive discoverability:

Strong on-page fundamentals still come first. Clear headings, well-structured content, and genuinely useful information remain what both traditional search engines and AI systems rely on to understand and surface your content. Our on-page SEO work focuses on exactly this layer.

Technical crawlability matters more than novel file types. If a search engine or AI crawler can’t efficiently access and parse your actual HTML, no auxiliary file is going to compensate for that. This is core to the technical SEO audits we run for clients.

Content quality is what earns citations, not self-description. Since AI systems can’t trust what a site claims about itself, the only lever left is producing content that’s genuinely comprehensive, accurate, and well-organised enough to be cited on its own merits. This is the foundation of effective AI SEO work, and it’s also why strong content writing remains as relevant in the AI search era as it ever was in traditional search.

Keep an eye on agentic standards, but don’t over-invest yet. As Mueller noted, none of the proposed standards for AI-agent interaction have settled into a single dominant approach. It’s reasonable to monitor developments like WebMCP, but building your entire AI-visibility strategy around an unsettled standard is premature.

Conclusion

Google’s comments on LLMs.txt aren’t an attack on AI-readiness efforts — they’re a correction of a widespread misunderstanding. Discovery has always run through HTML, and that hasn’t changed just because AI systems are now part of the search landscape. The sites that succeed in AI-driven search will be the ones that double down on the fundamentals: crawlable technical architecture, genuinely strong content, and clear on-page structure — not the ones hoping a self-authored text file does the work for them.