Job Displacement Tracker — Layoffs, Risk Data & Career Transitions

We try to track every publicly reported mass layoff event where AI, automation, or workforce restructuring was cited as a factor — we don't catch everything. Each event is classified across four tiers based on how the company described it: AI-Direct (company explicitly cited AI in a press release or filing), AI-Adjacent (restructuring framed in the context of AI adoption), Business Cycle (traditional economic reasons), or Mixed. Classification is done by LLM and flagged as unreviewed — so treat these as a useful signal, not a definitive determination. As of 2026, 9.9% of tracked events are AI-Direct; 72.3% are Business Cycle. WARN Act filings in the second tab are official government records, not media estimates.

These scores are our attempt to synthesize what multiple independent research teams have published about AI's impact on specific occupations — Anthropic's Economic Index, OpenAI's GPTs-are-GPTs study, Goldman Sachs, the WEF Future of Jobs report, and Oxford's Frey/Osborne analysis. Where studies agree, the range is tight. Where they disagree significantly (sometimes a 40-point spread), both ends are shown. We don't pick the "right" number. Scores span research published 2013–2025; the methodology section explains the vintage issue. Click any occupation to see salary data, task-level exposure, transferable skills, and transition pathways — treat these as a starting point for your own research, not a final answer.

For every occupation flagged as high-risk in the dashboard above, there are researched pathways to stable or growing roles. This section maps 27 career transitions across three categories: reskilling routes into tech and data roles, skilled trade apprenticeships that provide income during training, and entrepreneurship paths that leverage existing domain expertise. Each pathway shows the realistic salary delta, training cost, time to transition, and current job demand — not projections, but data drawn from BLS occupational projections, Glassdoor, and O*NET. Free and government-subsidized options are flagged. Use the compare tool to evaluate up to three pathways side-by-side.

AI isn't destroying the labor market uniformly — it's accelerating structural shifts that were already underway while creating entirely new categories of work. This section maps the specific roles actively losing headcount (with the automation mechanism that makes them vulnerable), the roles gaining workers despite AI pressure, and 10 job categories that barely existed three years ago. Salary transition data below shows what workers in declining roles can realistically earn after a switch — with actual figures drawn from BLS, Glassdoor, and LinkedIn salary data, not estimates.

Source: WEF Future of Jobs Report 2025 • BLS Employment Projections 2024-2034

Different industries are at different points in the AI adoption cycle — some are mid-restructuring right now, others haven't felt the impact yet. This section breaks down verified layoff volumes, AI attribution rates, and economic recovery timelines by sector so you can assess where your industry stands. Expand any card for the specific companies that have cut, which roles are most affected, what's actively growing within that sector, and whether the disruption is AI-driven or a standard business cycle event.

Tap any card for more detail • Source: MIT Sloan, Penn Wharton, WEF

Aggregate displacement figures obscure a sharper truth: the cost of AI-driven workforce restructuring falls unevenly, and it isn't random. This section identifies five demographic groups facing structurally elevated risk and explains the specific mechanisms behind their exposure — which roles they predominantly hold, why those roles are vulnerable, and what the data actually shows about realistic options.

Tap any card for more detail

AI's impact on labor markets isn't uniform — it's shaped by each region's mix of industries, digital infrastructure, regulatory framework, and workforce education levels. This section scores six global regions across four readiness dimensions to map the gap between AI exposure (how much risk exists) and preparedness (how equipped the workforce is to navigate it). The high-exposure, high-readiness regions face the sharpest near-term disruption but also have the most capacity to absorb it. US WARN Act filing data by state is embedded directly — legally documented mass layoffs at the sub-national level, not media estimates.

Source: IMF, UNDP, World Bank, Stanford warn-scraper (34 states)

Understanding the J-curve explains why current displacement figures don't tell the full story, and why the long-term economic outcome is genuinely uncertain. When companies adopt AI, productivity actually drops at first. Workers need training, systems need updating, and old processes break before new ones are ready. But after the rough patch, growth accelerates. Many economists describe this as the "J-curve" — it dips before it rises. This pattern is documented in MIT Sloan research on AI-adopting manufacturing firms and modeled by Brynjolfsson et al. in the NBER Productivity J-Curve framework (2019). Note: the magnitude and timeline of recovery are actively contested. Daron Acemoglu (MIT, Nobel 2024) argues many AI deployments target tasks with limited productivity upside, potentially reducing the long-run gain. Figures below reflect the Brynjolfsson/optimistic scenario; actual outcomes vary by deployment quality and sector.

Source: MIT Sloan, Penn Wharton, Challenger, Yale Budget Lab

If the rest of this tracker maps the scope of the problem, this section provides the practical response. Four categories of action — reskilling, skilled trades, entrepreneurship, and legal rights — each grounded in verified programs with real costs and timelines. Free options and government-backed resources are listed first. Everything here links to an original source: government agencies, official training directories, and legal frameworks. There are no affiliate links, sponsored placements, or marketing funnels — this is a public resource.

Every data point on this page has a traceable origin: risk scores are composites from multiple independent studies with the range shown when studies disagree; layoff events are verified against original press reporting and company filings; WARN Act filings are pulled directly from 34 state labor departments. The methodology accordion below explains how attribution works, why aggregator-tier sources are distinguished from peer-reviewed research, and what the historical baseline for layoff volumes looks like — so you can evaluate what you're looking at.

Last updated: May 2026 • Data refreshed via automated pipeline • GAIO v1.0 Integrity Lock active