The article examines the impact of Large Language Models (LLMs) on search engines and their implications for Search Engine Optimization (SEO). A heated debate in the SEO community revolves around whether new terminology is needed to describe techniques for optimizing visibility in AI-generated answers. Advocates for new terms like "Generative Engine Optimization" argue that the introduction of LLMs marks a significant shift, while others maintain that traditional SEO practices remain relevant. The author, a machine learning engineer, asserts that LLM-based search fundamentally alters the optimization landscape. Unlike classical search methods that return ranked documents based on relevance scores, LLM search generates answers by pulling relevant information from a broader dataset. This shift leads to different mathematical objectives in optimization. Classical SEO focused on maximizing click-through rates, while LLM search requires content to influence the generated answers without necessarily driving clicks. The author emphasizes that even though the underlying methodologies may not be entirely new, the strategies for SEO must adapt to this new context. Classical SEO’s objective function doesn't translate directly to the generative nature of LLMs. This change requires a reevaluation of how content is created and optimized to ensure visibility in LLM outputs. The article aims to clarify these distinctions and provide insight into how SEO practitioners can navigate this evolving landscape.