Confirmation Bias in Post-LLM Software Architecture: Are We Optimizing for the Wrong Reader?

This paper examines the impact of dot notation, the dominant naming convention in Event-Driven Architecture (EDA) frameworks, on the processing efficiency of Large Language Models (LLMs). We introduce the concept of architectural confirmation bias: the tendency of software architects to perpetuate human-readable design patterns without evaluating their optimality for AI agents that increasingly participate in the software development lifecycle. Through a theoretical analysis of Byte-Pair Encoding (BPE) tokenization mechanics applied to a corpus of 40 enterprise event identifiers, we demonstrate that dot notation produces an average of 1.67x more tokens than camelCase and 1.43x more than snake_case. We propose an actuarial cost model that quantifies the impact on API costs, context window consumption, and hallucination risk, projecting annual savings of up to $36,500 USD for high-volume enterprise systems switching from dot notation to camelCase notation. The paper concludes with a reproducible methodology for empirical validation.