Abstract:
This Comment shows how large language models (LLMs) can help courts discern the "ordinary meaning" of statutory terms. Instead of relying on expert-heavy corpus-linguistic techniques (Gries 2025), the author simulates a human survey with GPT-4o. Demographically realistic AI agents replicate the 2,835 participants in Tobia's 2020 study on vehicle and yield response distributions with no statistically significant difference from the human data (Kolmogorov-Smirnov p = 0.915). The paper addresses concerns about hallucinations, reproducibility, data leakage, and explainability, and introduces the locked-prompt "Ordinary Meaning Bot," arguing that LLM-based survey simulation is a practical, accurate alternative to dictionaries, intuition, or complex corpus analysis.