When "4.3M Prompts" Isn't 4.3M Prompts (www.aivojournal.org)

A forensic analysis reveals that dashboards touting “millions of AI prompts” (e.g., exact counts like “4,302,441 prompts”) are not reporting observed usage logs from LLM vendors but modeled estimates built from small opt‑in panels. The process is three-step: vendors collect panel data via browser extensions, partner apps or voluntary shares (biased toward early adopters), apply demographic and geographic weighting to extrapolate to a population level, and then surface the result as precise integers with error margins buried or omitted. Weighting reduces some demographic skew but cannot correct behavioral differences (e.g., heavy early‑adopter usage), and there are no independent benchmarks or visible confidence intervals to validate the projections. That presentation of false precision creates tangible governance risks for boards and executives: financial waste (vendors estimate 20–40% potential overspend), strategic misprioritization, reputational harm, operational misalignment, and emerging regulatory scrutiny. The authors recommend four safeguards—full disclosure of panel sources and holdout validation, visible confidence intervals alongside any totals, prohibiting modeled counts in audited board packs, and adopting an auditable Prompt‑Space Occupancy Score (PSOS) that measures observed brand presence in AI assistants. In short: treat panel‑based prompt volumes as directional signals, not governance‑grade facts.