Methodology
AI-Risk.ai separates evidence from interpretation. We track what changed, explain why it may matter, show who could be exposed, state confidence, and identify the next signal that could change the picture.
Signal first. Score only when justified.
We do not publish a synthetic all-purpose AI risk number simply because one looks good in a dashboard. Each dimension becomes quantitative only when source quality, update cadence and historical comparability support it.
Detect meaningful movement
Compare current evidence with a relevant baseline or prior release instead of treating every headline as a signal.
Translate evidence into context
Explain the transmission path: how a change in AI capability, adoption, labor markets, financing or geopolitics could affect real decisions.
Make uncertainty useful
State confidence and name the next data point that would confirm, weaken or materially change the current interpretation.
AI Jobs & Work Monitor
The U.S.-first labor-market monitor deliberately separates technical exposure from observed AI use and realized labor-market outcomes.
What can AI do?
Occupation tasks and skills are anchored in O*NET. External task-level exposure research, including the AI Risk Index, can be used as one versioned input with attribution.
What is actually being used?
Anthropic Economic Index, Census BTOS and other published adoption evidence help distinguish technical capability from real deployment.
What is changing in work?
BLS, Stanford and future labor-market feeds track hiring, entry-level access, wages and skill demand. A high exposure score alone is never treated as a forecast of unemployment.
Global Risk Pulse
Global Risk Pulse is a separate macro context model. Version 1.1 keeps the original 30/40/30 weighting while replacing two fragile news-coverage proxies with dedicated, reproducible research datasets.
Brent price movement
Daily Brent Europe spot-price observations are retrieved from FRED, sourced from the U.S. Energy Information Administration. The latest daily percentage move is translated into the energy stress signal.
Source: FRED / EIAAI-GPR geopolitical pressure
The daily AI-GPR Index by Matteo Iacoviello and Jonathan Tong measures geopolitical risk using LLM-scored newspaper coverage. AI-Risk.ai takes the current 7-day average and converts its position within the trailing five-year distribution into a 0–100 pressure score.
This is a geopolitical-risk intensity measure, not a probability of war and not independent event verification.
Source: Iacoviello & Tong AI-GPRNY Fed supply-chain pressure
The New York Fed Global Supply Chain Pressure Index combines transportation costs and manufacturing indicators into a monthly measure expressed in standard deviations from its historical average.
For the 0–100 context scale, zero standard deviations maps to 50 and each standard deviation shifts the signal by 20 points, capped at 0 and 100.
Source: Federal Reserve Bank of New York GSCPIGlobal Risk Pulse = Energy × 30% + Conflict × 40% + Supply × 30%
A composite is shown only when all three required inputs are backed by real observations. Source mode, provenance, model version and leading drivers remain visible; a source outage never becomes a fabricated live value.