How We Calculate AI Risk Scores
Our methodology combines peer-reviewed research, real-world AI labor market studies, and government data to produce accurate, task-level automation risk scores. Last updated: March 2026.
Research Sources
Anthropic — AI Labor Market Impact Study (March 8, 2026)
Most current primary research. Found AI theoretical coverage exceeds 80% in several occupation groups. Computer/math and business/finance at 94.3% exposure. Documented 16% employment decline for workers ages 22–25 in AI-exposed roles.
Goldman Sachs Research (2023–2026)
March 2026: 6–7% of US workers (~11M jobs) projected for long-term AI displacement. AI-related job losses at ~20,000/month in 2026. 300M jobs globally exposed to automation. 25% of US work hours potentially automatable.
Oxford Martin School (Frey & Osborne, 2013–2026)
\"The Future of Employment\" — original framework for estimating computerization probability of 702 occupations. Updated continuously with AI-specific capability assessments.
McKinsey Global Institute — \"Agents, Robots, and Us\" (November 2025)
Current AI could automate 57% of US work hours. Roles with highest automation potential make up 40% of total jobs. AI fluency demand grown 7x since 2023. 32% of companies expect AI-driven workforce reduction.
World Economic Forum — Future of Jobs Report 2025
170M new roles created vs 92M displaced by 2030 (net +78M). 41% of organizations expect AI-driven workforce reduction. 40% of job skills will change by 2030. Companion paper \"Four Futures for Jobs\" released January 2026.
Federal Reserve Bank of Dallas (February 24, 2026)
AI-exposed sector wages up 16.7% vs 7.5% national avg since 2022. Computer systems design employment down 5%. Total US employment up 2.5% since ChatGPT, but AI-exposed sectors significantly lagging.
Bureau of Labor Statistics (February 2026 Jobs Report)
92,000 jobs shed in February 2026. Unemployment at 4.4%. OEWS wage data: May 2024 edition (latest available; May 2025 data due May 15, 2026).
Scoring Method
Each job is broken down into its constituent tasks. Every task is individually assessed for AI automation potential on a 0–100% scale based on:
- Current AI capability — Can today’s AI (LLMs, agents, computer vision, robotics) perform this task?
- Near-future trajectory — Based on research trends, how likely is automation within 5–10 years?
- Task complexity — Does it require physical dexterity, emotional intelligence, or creative judgment?
- Regulatory barriers — Do licensing, safety, or legal requirements slow AI adoption?
- Real-world adoption rate — Is AI actually being deployed in this area, or only theoretically capable? (per Anthropic 2026 findings)
The overall job risk score is the weighted average of all task scores, where tasks that consume more of the job’s time receive higher weight.
Risk Tiers
Very Safe (0–19%)
Roles requiring deep human judgment, physical presence, or emotional connection. Minimal AI threat in the foreseeable future.
Low Risk (20–39%)
Some tasks automatable, but core responsibilities require human skills. AI will augment, not replace.
Moderate Risk (40–59%)
Significant portions of the role can be automated. Workers should actively upskill and adapt.
High Risk (60–79%)
Most routine tasks automatable. Role likely to transform significantly. Career pivoting recommended.
Extreme Risk (80–100%)
Nearly all tasks automatable with current or near-future AI. These roles face the highest probability of significant reduction or elimination.
Important Caveats
Our scores represent technical automation potential, not guaranteed job loss. As Anthropic’s March 2026 study confirmed, actual AI adoption is just a fraction of what AI tools are feasibly capable of performing. Actual displacement depends on:
- Economic feasibility of deploying AI vs. human labor
- Regulatory and legal frameworks in each country
- Social acceptance and trust in AI systems
- Speed of AI technology advancement
- New roles and industries created by AI (1.3M new AI jobs created 2024–2026 per LinkedIn)
- Wage premium in AI-exposed sectors (16.7% above national average per Fed Dallas)
Scores are updated quarterly as AI capabilities evolve and new research becomes available. Last major update: March 2026.