AI Job Market Analysis

Methodology

How we collect, verify, and present worldwide AI workforce impact data

Data Sources

Bureau of Labor Statistics (BLS)

Employment time series from the BLS public API (v1): total nonfarm employment (CES0000000001), information sector (CES5000000001), professional & technical services (CES6054000001), and unemployment rate (LNS14000000). Updated monthly.

Company Announcements & SEC Filings

Primary layoff data from official press releases, SEC 8-K filings, WARN Act notices, and earnings call transcripts where executives discuss workforce changes.

33 Verified News Sources

Cross-referenced reporting from Reuters, Bloomberg, CNBC, Financial Times, The Verge, TechCrunch, Wall Street Journal, BBC, NPR, Variety, Business Insider, Forbes, and 21 others. Each event requires at least two independent sources.

Research & Surveys

AI adoption rates from McKinsey Global AI Survey. Job market trends from LinkedIn Economic Graph and Indeed Hiring Lab. Salary data from Glassdoor and BLS OES. AI spending data from IDC and Gartner.

Collection & Update Process

Data collection combines automated scripts and manual curation:

  • Automated BLS pulls: Weekly workflow fetching latest BLS employment data via their public API.
  • Curated layoff events: Each of 167 events is manually verified and tagged with AI attribution based on company statements, analyst assessments, and contextual evidence.
  • Derived statistics: Industry breakdowns, geographic distributions, salary impacts, and skills demand data are regenerated from source data after each update.

AI Attribution Criteria

We classify a layoff as "AI-related" when one or more criteria are met:

  1. Company executives explicitly cite AI, automation, or ML as a driver of workforce reduction.
  2. Layoffs coincide with announced AI investment or product pivots (e.g., Microsoft investing $10B in OpenAI while cutting 10,000 jobs).
  3. Specific roles being eliminated are demonstrably automatable by current AI systems (e.g., customer service agents replaced by AI chatbots).
  4. Industry analysts and reporting directly link cuts to AI competitive pressures or transformation.

Each company is assigned an AI Investment rating (High/Medium/Low) based on publicly stated AI spending and strategic commitments.

Limitations & Caveats

  • Attribution complexity: Layoffs rarely have a single cause. Events attributed to AI may also involve economic downturns, over-hiring corrections, and unrelated strategic pivots.
  • Reporting bias: We primarily track large, publicly reported layoffs. Smaller companies and gradual attrition are underrepresented.
  • Projection uncertainty: Future projections are estimates, not predictions. AI development pace, regulation, and economics could significantly alter outcomes.
  • Geographic gaps: Coverage skews toward North America and Europe. Asian and developing market data is less comprehensive, though we now track companies like Samsung, Alibaba, Baidu, and ByteDance.
  • Job creation data: New AI job figures are based on industry estimates and job posting data, which may not reflect actual hires or long-term retention.
  • Salary impact: Salary data represents industry averages and may not capture company-specific compensation changes.