Methodology.
How AIREAPER calculates AI displacement risk — and what we don't claim.
Data layers
Every score combines five independent data sources, each weighted by how directly it measures displacement risk:
Source confidence
Not all sources carry equal weight. We assign confidence ranges based on how directly verifiable a signal is:
What counts as verified
Government filings (WARN Act, SEC EDGAR), official company press releases, and reporting from major financial news outlets. These appear on AIREAPER as confirmed events.
What counts as a signal
Hiring trend changes, job posting volume shifts, GDELT news sentiment, and industry-level macro data (BLS JOLTS). These move your score but are presented as trends, not certainties.
What counts as chatter
Anonymous employee posts and community reports. These are shown as "employee chatter" or "rumor heat" — never presented as confirmed fact, and never the sole basis for a high-risk score.
How often scores update
Risk calculations pull live data on every assessment — BLS JOLTS, GDELT news, and SEC filings are queried in real time. Sector-level baselines (Oxford, WEF) update as new reports are published, typically annually.
What we do not claim
Privacy / anonymity model
We separate your identity from public posts, minimize stored verification data, and never expose your email or verification data through client-facing APIs. Your company board identity is separated from your public posts.