Casmos: Optimizing for LLM Citations Instead of Rankings (yyyokel.com)

A new strategy called Casmos has been introduced, focusing on optimizing the citation behavior of Large Language Models (LLMs) rather than traditional ranking metrics. This approach emphasizes understanding how AI and competitors operate within specific niches, highlighting opportunities to exploit weaknesses in citation patterns and retrieval algorithms. Casmos suggests that LLMs, including models like Claude and Perplexity, exhibit distinct citation dynamics influenced more by query types than by underlying architecture. This indicates that controlling authoritative sources for certain queries could lead to significant advantages in citation capture. For the AI/ML community, this is significant because it shifts the focus from merely optimizing for search engine rankings to enhancing content for citation recognition by LLMs. By employing techniques like structured data and feedback loops, stakeholders can potentially accelerate traffic and establish credibility in AI-driven ecosystems. The implications are vast; using modular, extractive content architecture and strategically leveraging "parasite SEO" tactics may allow startups and content creators to gain rapid visibility and monetization—transforming AI search and citation strategies into a competitive edge in 2026 and beyond.