About PlainWorkforce

Our Mission

We believe that every worker deserves clear, data-driven insight into how technology is reshaping their profession. PlainWorkforce exists to make workforce intelligence accessible — combining official government employment data with AI exposure analysis to help workers, students, career counselors, and policymakers understand the evolving labor market.

Why we built this: artificial intelligence is transforming the workforce at an unprecedented pace, but most public discussion relies on speculation rather than data. Meanwhile, the Bureau of Labor Statistics publishes detailed employment projections and the Department of Labor maintains comprehensive task-level occupation data through O*NET. PlainWorkforce brings these official sources together into a single, searchable platform that makes workforce intelligence available to everyone, not just economists and policy analysts.

Our philosophy is to present data faithfully and let users draw their own conclusions. We do not predict which jobs will disappear or recommend career paths. We provide the evidence — projected growth, wages, education requirements, and AI exposure — so individuals can make their own informed decisions.

Data Sources

All data comes directly from two official federal sources:

  • Bureau of Labor Statistics (BLS) Employment Projections 2024-2034: Official 10-year employment projections covering 832 occupations and 291 industries. Includes projected employment levels, growth rates, job change numbers, median wages, and education requirements.
  • O*NET Database 30.0: The U.S. Department of Labor's Occupational Information Network, which documents the tasks, skills, knowledge, and abilities required for each occupation. We use this to calculate AI exposure scores based on task-level analysis.

We also incorporate BLS industry employment data to provide industry-level workforce outlooks, and O*NET job zone classifications to map education and training requirements. All source data is downloaded directly from bls.gov and onetcenter.org.

Methodology

Our approach combines BLS Employment Projections with O*NET task-level data to calculate AI exposure scores for each occupation. The AI exposure methodology evaluates each occupation's tasks against criteria for AI automability: routine data processing, pattern recognition from structured inputs, rule-based decision making, and predictable physical actions.

Each occupation's AI exposure score represents the weighted proportion of its task content that is susceptible to AI automation or augmentation based on current and near-term capabilities. Tasks are individually assessed and weighted by their importance to the occupation, producing a composite score that reflects the overall AI exposure profile.

Employment projections, wages, and growth rates come directly from BLS and are presented without modification. Industry data is matched to occupations through BLS staffing pattern crosswalks. No data is interpolated, extrapolated, or editorialized.

Data Freshness and Update Schedule

BLS publishes new Employment Projections every two years, with the most recent release covering the 2024-2034 projection period. O*NET is updated continuously by the Department of Labor, with major version releases approximately annually. Our current database reflects BLS projections released in 2024 and O*NET Database version 30.0.

We update our database when significant new data is released from either source. The last updated date and data vintage for each data dimension are tracked in our ETL pipeline metadata to ensure transparency about data currency. AI exposure scores are recalculated when new O*NET task data becomes available.

Editorial Independence

Content on PlainWorkforce is compiled by our editorial team from official source data. Raw data from the U.S. Bureau of Labor Statistics (BLS Employment Projections 2024–2034), the Department of Labor's O*NET 30.0 occupational database, and SEC EDGAR filings for publicly traded employers is reformatted into readable occupation, industry, and employer profiles by our editorial team and verified against the source. before publication. AI exposure scores are computed from O*NET task descriptions using a documented methodology. The PlainWorkforce editorial team, operating under Kiznis Studio, is responsible for editorial standards, methodology, and corrections.

We do not accept payment, sponsorship, or promoted placement from employers, staffing firms, educational institutions, or any covered entity. Our only revenue source is contextual display advertising served by Google AdSense — advertisers do not influence which occupations, industries, or employers we cover or how we present data, and they do not receive preferential placement.

Limitations and Disclaimer

This site is for informational purposes only and does not provide career, financial, or employment advice. Important caveats to understand when using this data:

  • AI exposure scores are analytical estimates based on task analysis methodology — they are not predictions of employment outcomes
  • Employment projections are official BLS figures but are inherently uncertain, especially over a 10-year horizon
  • Actual workforce impacts depend on many factors beyond task automability, including adoption speed, economic conditions, regulatory environment, and the emergence of augmentation models
  • Wage data reflects national medians and varies significantly by geography, experience, and employer
  • Not all occupations in the economy are covered by BLS projections or O*NET task data

PlainWorkforce is not affiliated with the Bureau of Labor Statistics, the U.S. Department of Labor, or any government agency. Do not rely solely on this data for career decisions — consult career counselors, industry professionals, and other official sources.

Editorial Team

PlainWorkforce is published by PlainWorkforce Editorial, a small independent team that builds public-data portals so that government workforce records remain accessible to the people they describe. Our editorial work on PlainWorkforce is led by editors with backgrounds in labor-economics analysis, public-records journalism, and occupational research — disciplines that combine to vet what BLS, the Department of Labor, and O*NET publish, surface their limitations, and translate technical taxonomies (BLS Employment Projections methodology, O*NET job zone classifications, NAICS industry codes, SOC occupation codes) into language a worker, journalist, or policy researcher can actually use.

We do not employ practicing labor economists and do not publish original career advice. Instead, our editorial standard is verification, citation, and transparency: every data field we surface is traceable to a BLS or O*NET publication, every caveat (projection horizon, automation-exposure inference, employment-mix changes) is disclosed at the page level, and every methodology decision is documented at /methodology. When source data has known shortcomings — for example, BLS Employment Projections are 10-year forward estimates and actual outcomes can diverge from projected paths — we say so on the page where the data appears, not buried in a footer.

Editorial questions, fact corrections, and source-attribution issues should go to hello@plainworkforce.com. We are accountable for what we publish: every correction we make is reflected in the next data refresh, and our update schedule is documented at the top of this page so readers can see how recent the underlying figures are. PlainWorkforce does not accept paid placement, sponsored listings, or any incentive that would compromise the neutrality of how occupations and employment trends appear on the site.

Contact

For questions, feedback, or data correction requests, email us at hello@plainworkforce.com. We welcome reports of data discrepancies and suggestions for improving the site. Our goal is to make workforce intelligence accessible to everyone navigating the changing labor market.