Why Don't Scientist Trust Atoms?

A joke request is a maximally underspecified prompt: the model chooses format, topic, register, and delivery entirely on its own, which makes "Tell me a joke." a cheap, repeatable probe of default persona. We preregistered a comparison of two model families (Anthropic Claude, OpenAI GPT) across three commercial capability tiers each, under three request phrasings forming a content-pressure gradient (baseline / clean / dirty), with N = 200 independent single-turn calls per cell (3,600 calls, temperature 1.0, no system prompt). The unconstrained result is a monoculture with family signatures inside it: every one of 1,200 baseline responses was a question–answer pun, drawn almost entirely from a three-joke canon. All 600 Claude baseline responses were the atoms joke — for Claude Sonnet 4.6, 200 of 200 responses were character-identical, wrapper and emoji included, at temperature 1.0 — while GPT tiers spread across the skeleton and scarecrow jokes at lower concentrations. Under the "dirty" probe the families diverge structurally: two Claude tiers shift overwhelmingly to narrative joke forms while GPT stays in question–answer, and the only outright refusals in the study come from GPT's smallest tier. Of four confirmatory hypotheses surviving to analysis, one was supported and three were falsified, one of them — the misattribution of the atoms joke to GPT — in unusually instructive fashion. Three instrument failures (a judge-model output collapse, a blinding defect, and a compliance construct that three coders read three ways) are reported as findings. The study is also, reflexively, an artifact of its subject matter: Claude models are subjects, judge, apparatus co-authors, and reporting analyst, under human direction and validation.