The landscape of guidance for search engine optimization (SEO) has historically relied on a collaborative framework among major search engines, allowing practitioners to optimize for one platform with the confidence that it would apply across others. This was exemplified by shared protocols like Sitemaps and Schema.org. However, the emergence of large language models (LLMs) such as OpenAI's ChatGPT and Google's Gemini has disrupted this model. Unlike SEO, LLMs operate on distinct training datasets and retrieval systems, resulting in a lack of standardization and portability. For instance, the proposed llms.txt file, designed to help LLMs identify crucial content, has not been adopted by any major provider, highlighting the absence of a shared standard. Furthermore, analyses reveal that Google’s AI surfaces do not align with its own SEO guidance, complicating the optimization landscape. As a result, content that performs well on one platform may not translate to others, necessitating a more nuanced approach for practitioners. They must now treat guidance from each LLM provider as a singular input rather than a comprehensive roadmap, adapting their strategies to the unique characteristics of each platform.