The Canned Conspirac-pea: Neutral Object Enumeration as Method for Detecting Search Drift and Calibrating AI Epistemic Postures

This paper documents a low-stakes, humor-driven observational experiment that began with an anomalous market statistic about canned peas and evolved into a cross-model epistemic calibration test for large language models (LLMs). Using a deliberately neutral object (“the pea”) and a bounded enumeration prompt (“provide a rough lower-bound estimate”), the experiment probes how different AI systems respond to ambiguity, abstraction, and scope definition. Responses from multiple models reveal a consistent divergence between compressive (enumerative, category-collapsing) and expansive (granularity-maximizing, ontology-exploding) reasoning styles when tasked with defining “distinct functional uses” of an everyday object. The experiment further identifies a real instance of aggregation drift in AI-generated search summaries, where a repeated but mis-scoped market figure ($8.5B) propagated as an apparent “fact” despite originating from a broader category. This drift was independently detected through cross-model comparison and subsequently acknowledged via feedback to a live search system. Importantly, the study demonstrates that playful framing and low symbolic loading can reduce defensive behaviors in AI systems, enabling clearer observation of epistemic posture, abstraction bias, and corrective mechanisms. The resulting methodology—proposed as the Neutral Object Enumeration Test (NOET)—offers a lightweight, reproducible approach for evaluating model behavior under ambiguity without invoking ideologically or socially loaded content. This work makes no claims about peas themselves; instead, it uses a mundane object as a calibration substrate to explore how knowledge compression, repetition, and abstraction boundaries shape AI-mediated “facts” in real-world information systems.