LLM Guidance Doesn’t Transfer the Way SEO Guidance Did: the Operator's View

For the better part of two decades, those of us in the search industry operated under a quiet, comforting assumption: if you solved for the biggest player, you had effectively solved for everyone. It was a shortcut that worked. If Google signaled that sitemaps were a priority, you could be reasonably certain that Bing would value them too. If Bing emphasized the importance of structured data, Google was usually in agreement.

This portability wasn't a coincidence or a stroke of luck. It was the result of a massive, shared architectural layer that the major search engines spent twenty years building together. We lived in a world where the "rules of the road" were largely standardized across the industry. But as we move into the era of Large Language Models (LLMs), that world has vanished.

The danger now is that we are carrying old habits into a new environment. We are tempted to find one "authoritative" guide to LLM optimization and apply it universally. However, the structural reality of AI is fundamentally different from the structural reality of traditional search. Following one provider's guidance today is not a map of the territory; it is a single data point in a highly fragmented landscape.

The Collaborative Foundation of Traditional SEO

To understand why LLM optimization is so different, we have to look at how the "portability" of SEO was actually engineered. It wasn't just that engines happened to like the same things; they explicitly collaborated to create shared standards.

A prime example is the Sitemaps protocol. In November 2006, Google, Yahoo, and Microsoft formally agreed to support a common protocol (version 0.90), building upon an earlier version Google had released in 2005. This wasn't a competitive move; it was a utility move. By agreeing on how sitemaps worked, they reduced the friction for webmasters and improved the quality of data for the engines.

This pattern repeated in 2011 with the launch of Schema.org. Google, Bing, and Yahoo (and shortly after, Yandex) created a common vocabulary for structured data. The goal was simple: create a shared language so that a site owner didn't have to write three different versions of the same metadata to be understood by three different bots.

We saw this again with the long-standing convention of robots.txt, which eventually became a formalized standard (RFC 9309) in 2022. Even more recently, the IndexNow protocol—launched by Bing and Yandex—has been adopted by several other engines, though Google has remained in the testing phase without full adoption.

Expert Interpretation: The tradeoff here was a sacrifice of absolute competitive uniqueness in exchange for ecosystem efficiency. The search engines realized that a cleaner, more standardized web benefited everyone. For the practitioner, the decision was easy: follow the most stringent standard (usually Google's), and you were covered across the board. The "portability" was a feature of the system's design, not a fluke of the algorithms.

Where the LLM Stacks Diverge

LLMs do not have a shared substrate. There is no "Schema.org" for generative AI. The differences between OpenAI, Google, and Anthropic are not superficial tweaks; they are baked into the very foundation of how these models are constructed.

The most significant divergence begins with training data. The corpora used to feed these models are not identical. OpenAI has entered into high-profile, disclosed licensing agreements with News Corp (up to $250 million over five years), Axel Springer, and Reddit (estimated at $70 million per year), along with several other prestige publishers like the Financial Times and the Associated Press.

Google has its own distinct arrangements, including a Reddit deal estimated at $60 million per year that provides real-time API access. Meanwhile, Anthropic has not publicly disclosed equivalent publisher licensing deals. This means the "knowledge base" of each model is fundamentally different. One model may have a deep, licensed understanding of a specific news archive that another model simply does not possess.

The divergence extends to the infrastructure used to gather this data. We are no longer dealing with a few standardized crawlers. OpenAI utilizes GPTBot for training, OAI-SearchBot for indexing, and ChatGPT-User for retrieval. Anthropic employs ClaudeBot, Claude-SearchBot, and Claude-User. Perplexity uses its own set of bots, and Google uses Google-Extended to manage how its AI models interact with web content.

Expert Interpretation: This matters because the "black box" has grown. In traditional SEO, you could guess the logic because the standards were public. In LLM optimization, you are fighting against undisclosed licensing deals and proprietary retrieval pipelines. The tradeoff is that you can no longer optimize for "the web"; you have to optimize for specific, competing data pipelines. The decision you must make is whether to spread your efforts thin across all providers or double down on the one that drives your most valuable traffic.

The Failure of Community-Driven Guidance

Because we are used to portability, the SEO community often tries to create its own standards when the providers are silent. However, these community-led efforts often fail to port to the actual technology.

The most striking example is the llms.txt file. Proposed by Jeremy Howard of Answer.AI in September 2024, this was intended to be a markdown manifest placed at a site's root to guide LLMs toward the most critical content. The industry jumped on it. Agencies added it to their service lists, and plugins were built to generate these files automatically.

But here is the reality: as of mid-2026, no major LLM provider—not OpenAI, not Anthropic, and not Google—has confirmed that they actually consume this file. Server log data across hundreds of thousands of domains suggests that the major AI crawlers are not routinely requesting these files. The community built a "standard," but the providers didn't sign the agreement.

Expert Interpretation: This highlights a dangerous gap between "community consensus" and "provider reality." In the past, if the SEO community agreed a tactic worked, it usually did because the underlying engines were using the same shared standards. Now, a tactic can be "industry standard" and yet be completely ignored by the models. The lesson here is to prioritize provider-confirmed documentation over community-driven "best practices."

The Gemini Inversion

Perhaps the most confusing aspect of this new landscape is that divergence exists even within a single company. Google is the perfect example of this "inversion."

For twenty years, Google has published canonical guidance via Search Central. This documentation emphasizes E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness), content quality, and technical accessibility. This guidance remains highly effective for traditional Google Search results.

However, Google also produces Gemini, which powers AI Overviews and the standalone AI Mode. The evidence suggests that the citation behavior of Gemini does not strictly follow the same rules as Google Search. A page that is perfectly optimized for Search Central's guidelines may still be ignored by Gemini's generative output.

Expert Interpretation: This is a critical realization. If Google's own search guidance doesn't perfectly port to Google's own AI model, there is no hope that OpenAI's guidance will port to Gemini. The tradeoff is between "ranking" (traditional search) and "citation" (generative AI). You must inspect your visibility on AI surfaces separately from your traditional keyword rankings, as they are governed by different internal priorities.

The Shrinking Layer of Universality

To be clear, there are still some things that port across all LLMs, but this "universal layer" is much smaller than it used to be. It is no longer a comprehensive set of rules, but rather a few basic prerequisites.

First, crawler accessibility remains universal. If a bot cannot reach your content, it cannot cite it. Second, primary-source factual content continues to outperform aggregated restatements. LLMs are designed to find the "source of truth," and those who provide the original data are more likely to be cited.

Third, clean, retrievable structure still helps. While a specific llms.txt file might be ignored, a page that is logically organized is easier for any system to parse. Finally, presence on high-authority "force multipliers"—such as Wikipedia, Reddit, YouTube, and major news outlets—remains a universal win. Because almost all major LLMs draw heavily from these sources, visibility there increases your chances of surfacing across multiple platforms.

Expert Interpretation: The universal layer has shifted from "technical standards" (like Sitemaps) to "authority signals" (like Wikipedia). The tradeoff is that these authority signals are much harder to manipulate than a technical file. The decision for the practitioner is to stop looking for a "hack" and instead focus on earning mentions in the high-authority datasets that all models share.

Redefining the Workflow

The practical takeaway is that we must abandon the "Google-first" reflex. In the old world, you optimized for Google and trusted the portability. In the new world, treating any single provider's guidance as a universal map is a recipe for invisibility.

The new workflow requires a shift in mindset: treat divergence as the default and overlap as the exception. This means reading the documentation from every major provider—OpenAI, Google, Anthropic, and Perplexity—and recognizing that their advice may contradict one another.

More importantly, you must test your visibility across platforms. Do not assume that because you appear in a ChatGPT response, you will appear in a Gemini AI Overview. You have to verify your presence on each surface individually.

We are moving from an era of "set it and forget it" portability to an era of platform-specific calibration. It is more work, and it is less predictable, but it is the only way to ensure your content remains discoverable in a fragmented AI ecosystem.