Eliminator: Semantic Merit Allocation for AI Compute

Large language models consume significant computational resources, which are increasingly recognized as a scarce global asset. Current AI systems allocate computational resources primarily based on infrastructure constraints, subscription tiers, or internal routing heuristics rather than the semantic value or societal relevance of user queries. This paper proposes a new framework called Semantic Merit Allocation (SMA). The system evaluates the logical clarity, informational density, and intended purpose of a query before allocating computational resources. Under this model, AI compute is distributed according to the semantic merit of requests, rather than purely economic or random demand. Educational contexts and novice users are treated as a special category, ensuring that learning processes are supported without penalization. The framework aims to reduce computational waste, improve fairness in access to advanced AI systems, and create a new paradigm for responsible AI resource management.