Find where your work sits as AI changes work
Type your job to find its approximate point on the AI exposure gradient — then see what the work is actually made of: tasks, pay, preparation, tools, industries, and what people already delegate to AI.
Approximate — your point is AI task overlap (how much of the work today's AI can attempt), not a forecast, not automation, not jobs lost.
e.g. registered nurse · software developer · electrician · marketing manager
Exposure is how much of the work today's AI can already do — task overlap, not a prediction of automation or jobs lost. How this is measured →
Find your coordinate at any scale
Three honest doors into the same map — pick the scale you think in.
Land on your occupation’s coordinate — its tasks, pay, preparation, and AI-exposure band.
See your industry’s workforce shape and its employment-weighted AI-exposure band.
Find a single skill or tool — the jobs that rely on it and how exposed that work is.
“Business” here means your industry’s shape, not a per-company score — AI exposure is task overlap drawn from public datasets, never a verdict on any single employer or person.
Not “will AI take my job” — how much of the work it can already do. See where every occupation sits on a six-band gradient of generative-AI exposure, and how it moved between 2023 and 2025.
Most AI-exposed occupations
News Analysts, Reporters, and Journalists · Mathematicians · Market Research Analysts and Marketing Specialists · Search Marketing Strategists · Data Scientists
Least AI-exposed occupations
Helpers--Roofers · Helpers--Painters, Paperhangers, Plasterers, and Stucco Masons · Foundry Mold and Coremakers · Orderlies · Pile Driver Operators
Most-exposed industries
Insurance Agencies and Brokerages · Finance and Insurance · Direct Health and Medical Insurance Carriers
Least-exposed industries
Landscaping Services · Agriculture, Forestry, Fishing and Hunting · Poured Concrete Foundation and Structure Contractors
Exposure is how much of the work today's AI can already do — task overlap, not a prediction of automation. Occupations ranked by percentile; industries employment-weighted.
The 2013 reversal →Before large language models, the famous 2013 Frey–Osborne study put the automation risk on routine manual and clerical work. A decade later, AI task-overlap points the other way — at the cognitive, white-collar work that study rated safe: General and Operations Managers · Customer Service Representatives · First-Line Supervisors of Office and Administrative Support Workers · Elementary School Teachers, Except Special Education.
Two different estimates a decade apart — a contrast, not a forecast. See how every occupation flipped, held, or agreed across the two eras.
Browse the encyclopedia
- Roles
Every occupation — tasks, skills, pay, employment outlook, and AI exposure.
- Skills
Skills work requires, and the occupations that rely on each one.
- Knowledge
Bodies of knowledge jobs draw on, ranked by how broadly they're needed.
- Abilities
Enduring human abilities and the work that depends on them.
- Tools & technology
Software and equipment categories, and who uses them.
- Work activities
Generalized work activities and how often AI is applied to each.
- Industries
Sectors, their occupations, wages, and skill & tool metabolism.
- What people ask AI
The demand side of the AI economy — what people actually ask AI to do, from the Anthropic Economic Index.
- AI by state
Where AI use and specific requests over-index across the U.S.
Big-picture views
- The AI exposure gradient
Where the world's work sits on a six-band gradient of generative-AI exposure — and how it shifted 2023 → 2025.
- AI exposure across work
The most and least AI-exposed slices of the economy — every occupation, every industry, and six ways of grouping work, ranked on one comparable scale.
- Where work is changing
The occupations picking up new and revised tasks — where the day-to-day is visibly shifting.
- Job outlook
BLS employment projections — which occupations are growing, shrinking, and hiring.
- Jobs you can apprentice into
Careers you can enter through a paid, registered, earn-while-you-learn pathway.
What is Singulariki?
Singulariki turns public labor data into a readable page for every occupation. Each page draws on O*NET (tasks, skills, knowledge, abilities, tools, work context, preparation), BLS (wages and employment), published research on AI exposure, and the Anthropic Economic Index (how AI is actually being used) — and names the dataset and version each figure came from.
The whole graph is cross-linked: occupation → tasks → skills, knowledge & abilities → tools → industries → wages → employment → AI exposure → actual AI usage → request demand. Search for a role above, or browse the encyclopedia.
Not a prediction machine. A work map.
Singulariki names its sources and shows the dataset version behind every number. It separates AI exposure from replacement, and labor-market forecasts from AI-impact claims. See the methodology & sources →