The Ordinary Meaning Bot: Simulating Human Surveys with LLMs

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, 2026), 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, training-data contamination, 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.