BLS 2024–2034 Projections · O*NET task analysis
Will AI replace your job?
The average U.S. occupation has 37% of its tasks exposed to AI automation, according to BLS 2024-2034 projections and O*NET task analysis. Search 832 occupations, 291 industries, and 1,965 employers.
Across 832 occupations and 169,956,400 reported 2024 jobs, BLS projects 581 occupations growing through 2034 and 244 contracting. The average occupation has about 37% of its tasks exposed to AI automation.
Home health and personal care aides is #1 of 832 occupations by employment (4.3M, AI exposure rank #392 at 35%) vs. Bookkeeping, accounting, and auditing clerks #1 by AI task exposure (95%, employment rank #17 at 1.6M).
Entry credentials across the corpus
Exclusive cut of 832 occupations by BLS typical entry-level education. Orthogonal to the sector demand bars and the employment≠AI dual above, this is the credential inventory, not a growth or exposure ranking.
Projected workforce demand by sector
BLS Employment Projections (2024–2034) by major NAICS sector. Healthcare and professional services lead; retail and extraction contract.
Projected employment change by sector, 2024–2034: Healthcare+8.1%, Professional Services+7.8%, Information & Tech+7.2%, Arts & Recreation+5.7%, Construction+4.7%, Finance & Insurance+3.6%, Hospitality+2.9%, Utilities+2.4%, Transportation+2.4%, Agriculture+0.1%, Education+0.1%, Manufacturing0%, Retail-1.2%, Mining & Extraction-1.6%.
PlainWorkforce combines official BLS Employment Projections for 2024-2034 with O*NET task analysis to measure how exposed every US occupation is to AI automation. Each occupation receives an AI exposure score from 0-100% based on the proportion of its tasks that current or near-future AI systems can perform.
Whether you are a student choosing a career path, a worker considering a transition, or a policy researcher studying labor market disruption, PlainWorkforce gives you the data to make evidence-based decisions. We cover 832 occupations across 291 industries, with detailed profiles showing growth projections, median wages, education requirements, and task-level AI vulnerability.
Not sure where to start? Our guides break down the key questions: how many jobs are at risk from AI, which remote jobs are most vulnerable, how apprenticeships compare to college degrees on cost, earnings, and AI resilience, and which careers are most future-proof through 2034.
All projections come from the Bureau of Labor Statistics. AI exposure scores are derived from O*NET task importance data. This is informational analysis based on federal data sources, not career advice.
Exposure vs. growth, where the big jobs are heading
The 16 largest U.S. occupations by AI exposure (horizontal) and projected 2024–2034 growth (vertical). The bottom-right is the danger zone, high automation exposure and shrinking; the top-left is the future-proof corner.
How to read this
- Above-median exposure
- At/below-median exposure
Bubble size = current 2024 employment. Hover or focus any point for its exact figures.
Source: BLS Employment Projections 2024–2034; AI-exposure scores are PlainWorkforce's analysis of O*NET task data.
Most At-Risk from AI
Occupations with highest AI exposure scores
Fastest Growing Careers
BLS 2024–2034 projected growth leaders
Registered Apprenticeships
Earn while you learn, DOL FY2024 data for 844,900 active apprentices
How We Measure AI Risk
O*NET Task Analysis
We analyze every task within each occupation from the O*NET database, evaluating whether it can be automated by current or near-future AI systems.
BLS Projections
Official 10-year employment projections from the Bureau of Labor Statistics show which occupations are growing or contracting through 2034.
AI Exposure Score
Each occupation gets a 0–100% AI exposure score based on the proportion of its tasks that are susceptible to automation, weighted by importance.
Download the occupation AI-exposure extract cited on this page: occupation-ai-exposure-landscape.csv (CC0).
Guides & Analysis
Editorial research and plain-language explainers from our team. Every guide is written to help you read the underlying public data correctly.
Research
Original analysis from our editorial process, every statistic derived from our own database. See all research.
State-Level Apprenticeship Programs for High-Growth Trades in 2023
Apprenticeship data across 51 states, California leads with 89,000 active apprentices and Texas added 18,500 new enrollees, concentrated in trades like electricians and plumbers, with California's completion rate near 58%.
ResearchKey Industry Employment Shifts from 2024 to 2034 Projections
Employment shifts across 291 industries, crop production adds 17,300 jobs (+2.0%) while animal production sheds 14,800 (−3.3%), with each BLS trend linked to occupation-level AI risk.
What to do with this
Use the AI exposure and growth data above to plan a move, not just react to a headline.
- Check where your own occupation ranks on automation risk before assuming AI headlines apply to you specifically. Safest occupations from AI
- See which occupations BLS projects to add the most jobs through 2034. Fastest-growing occupations
- Compare two occupations side by side on wage, growth, and exposure before making a career decision. Workforce comparison tool
AI exposure scores estimate task automatability from O*NET data; they are not a certainty of job loss, and BLS growth projections are estimates, not guarantees.
About this data
How PlainWorkforce works, and why you can trust these projections
What this site is
PlainWorkforce tracks U.S. occupation and industry employment projections, wages, and AI automation exposure, built from BLS Employment Projections and O*NET task data, with employer profiles enriched from SEC EDGAR filings for publicly traded companies.
Editorial process
- Source. Download the BLS Employment Projections tables (occupation and industry, 2024-2034), the O*NET 30.0 database of task and skill descriptions, and SEC EDGAR company filings for employer profiles.
- Verify. Derive AI exposure scores from O*NET task importance combined with BLS employment projections, evaluating whether each task involves routine data processing, pattern recognition, or physical dexterity.
- Publish. Cross-validate wage figures against BLS OEWS data and publish per-occupation, per-industry, and per-employer pages linking back to the source.
Editorial independence & corrections
The PlainWorkforce is independent and is not affiliated with the BLS, the Department of Labor, or the SEC. Found an error? Get in touch via the contact page. See our methodology for full source attribution.
Frequently asked
Which jobs are most at risk from AI automation?
According to BLS projections and O*NET task analysis, clerical and routine data-processing roles face the highest AI exposure. Bookkeeping clerks, data entry workers, and telemarketers consistently rank highest in automation risk studies.
How is AI exposure score calculated?
AI exposure scores are derived from O*NET task importance data combined with BLS employment projections. Each task is evaluated for its susceptibility to AI automation based on whether it involves routine data processing, pattern recognition, or physical dexterity.
What time period do these employment projections cover?
PlainWorkforce uses BLS Employment Projections covering the 2024–2034 decade, released by the Bureau of Labor Statistics. These are official 10-year projections covering 832 occupations and 291 industries.