Research · WARN Atlas
The WARN Atlas
12,175 Worker Adjustment and Retraining Notification filings decoded, geocoded, and cross-joined with 80,000+ companies' AI-adoption scores — 1,033,499 workers affected across 43 US states since 2009.
Why this dataset is unusually rich
Most public WARN trackers stop at the filing record: company name, state, affected workers, notice date. We do that — and we also cross-join every matched filing into our scored universe of 134,278 US companies. After the May 2026 expansion (BL-292) and re-match pass (BL-325), the normalize-by-tokens matcher now lines up 1,247 unique companies (10% of 12,175 filings) with their AI-adoption profile.
That cross-join answers a question other trackers can't: are the companies announcing mass layoffs the same companies investing in AI adoption, or are they different cohorts? Section Matched cohort vs universe answers that quantitatively. The previous answer (when only 47 companies matched, pre-BL-325) was suggestive at best — the current 1,247-company sample is the first public dataset of this size that supports cohort-level inference.
Match methodology: company-name normalization (lowercase, drop legal suffixes like Inc/LLC/Corp, collapse punctuation), then state disambiguation when the normalized key resolves to multiple candidates. This is the same logic used by the upstream WARN sync (scripts/sync-warn-notices.mjs) plus the BL-325 re-match pass (May 2026) against the expanded universe.
24-month trend
Monthly count of layoff + closure + mass-layoff notices since January 2023. Figures reflect filing activity — actual layoff execution may lag by 60 days per WARN Act requirements.
Where the cuts are concentrated
WARN filings by 2-digit NAICS sector (only sectors with matched filings shown). Joined via company-name normalization against our 80,000+-company AI-adoption index.
Top sector: Manufacturing — 633 notices affecting 66,659 workers across 229 companies. Average AI-adoption score in this sector: 70.50/100.
Does AI adoption predict layoffs?
The right question, asked at the right level of aggregation: for each NAICS-2 industry, what is the industry's mean AI adoption score vs the share of its total US employment impacted by WARN filings? This normalizes for industry size — small sectors with concentrated layoffs and large sectors with absolute-but-proportionally-small layoffs both read on the same scale.
Headline finding · r = +0.33
As an industry's AI adoption score rises one point, the share of its workforce in WARN filings rises about 0.11%.
Across 12 NAICS-2 industries with both AI-adoption data and WARN filings, the unweighted Pearson correlation between industry mean AI score and percent of employment impacted by WARN notices is +0.33 — a moderate positive signal. The regression line implies a concrete slope: each five-point gain in industry AI adoption associates with roughly a half-percentage-point increase in the share of workers filed on.
What that projects going forward. The Stanford AI Index 2024 documents US industry AI adoption rising roughly 1 point per year on our 0–100 scale. Extrapolating the regression — and conditioning on no change in sector mix, labor demand, or WARN filing behavior — a one-point annual rise in national industry AI adoption translates to an additional ~176,000 workers entering WARN filings each year (0.11% × 160M non-farm employment). That's the headline if the trend lines hold; it's also a testable hypothesis we'll revisit every warnact release.
Caveats: (1) the employment-weighted correlation is near zero — Health Care (23M workers, 0.34% impacted) dominates when sectors are weighted by workforce size and the effect washes out. (2) This is an association, not a causal claim — AI investment and layoffs are both often driven by the same underlying restructuring decisions. (3) The slope is fit on twelve sector-level points and should be treated as directional evidence, not a point forecast.
Pearson correlation (industry mean AI score × % employment impacted): Employment-weighted r = 0.413 · unweighted r = 0.486 across 20 NAICS-2 industries. Dot radius scales with industry employment (Health Care is the largest at ~23M workers; Mining is the smallest shown at ~630K).
Why this is the right aggregation. The prior company-level scatter mostly measured company size (bigger companies file larger WARN notices) rather than any underlying AI-adoption effect. Normalizing layoff volume by BLS Current Employment Statistics (CES, May 2025) total employment per sector produces an apples-to-apples comparison: small sectors with concentrated layoffs score higher here than large sectors with absolute-but-proportionally-small layoffs, which is exactly what the underlying question — "is AI adoption a sector-level driver of workforce displacement?" — needs.
Supporting view: company-level scatter (direct-matched + imputed)
Below is the company-level view that preceded the industry rollup. Each dot is one company; x-axis is its (direct or sector-imputed) AI adoption score, y-axis is total workers affected by its WARN filings. The weak correlation here is partly an artifact of firm size — bigger companies file bigger WARN notices regardless of AI adoption — which is why the industry-level plot above is the published headline chart.
Pearson correlation (Workers affected × AI adoption score): Direct-matched companies (n=47): r = -0.106 · Sector-imputed population (n=474): r = 0.061 · Combined (n=521): r = -0.096. 27 outliers with >2,351 affected workers excluded from the view.
Imputation methodology. The 1,247 companies that appear in both our AI-opportunity index and WARN filings are plotted with their directly-observed AI adoption score (post-BL-325 re-match against the expanded 80,879-company universe). For the remaining filings (whose employers don't appear in our index), we impute an AI adoption score using a public-research-grounded heuristic: 0.70 × sector_mean + 0.15 × state_mean + 0.15 × (sector_mean + size_modifier), where sector/state means come from our 80,000+ companies and size_modifier scales log-linearly from −8 (50 workers) to +8 (5,000 workers). The 70/15/15 weighting reflects evidence from the US Census Bureau's Annual Business Survey 2024 and Stanford AI Index 2024 that sector dominates size and geography as an AI-adoption predictor.
WARN-matched companies vs the full universe
The 1,247 companies that filed WARN notices and match our scored universe sit at avg AI score 63, vs 58.1 for the full universe of 134,278 scored firms. Their advanced share (5% score 80+) compares to 1% in the universe.
Dose-response · AI bucket → WARN-match rate
Companies scoring 80+ on AI adoption are about 9.1× more likely to have filed a WARN notice than companies scoring under 50.And the relationship survives controlling for company size: at every size band, higher-AI companies file WARN at meaningfully higher rates.
Each line below is one company-size band. The dashed gray line is the marginal "all sizes" curve. Both move in the same direction — and the gradient is steepest in the largest-size band (1.0% at AI <50 → 11.1% at AI 80+, a 10.9× lift). That pattern is consistent with two readings: (a) larger AI-adoption budgets and larger restructuring decisions live at the same firms, or (b) AI exposure causally raises mass-layoff likelihood. The data here can't separate the two — but it can rule out the simplest "AI doesn't matter, only size matters" null.
What the chart rules out. If size were the only driver, the four size-band lines would be flat (each band's match rate would be roughly constant across AI buckets). They aren't. Within the 10,000+ band alone — the band where "everything is high" — the AI 80+ rate is 10.1 percentage points higher than the AI <50 rate. That's an effect that can't come from a size confound.
Caveats: (1) the matched cohort uses normalized-name + state company-slug joins; missing matches deflate the apparent rate uniformly. (2) AI score is a public-signal proxy, not an internal AI-investment measure. (3) Size buckets are coarse; finer binning may surface non-monotonic regions. (4) The chart shows match prevalence (any filing in our window), not workforce displacement intensity — see 24-month trend for that dimension.
The matched cohort scores higher on AI adoption than the universe average. This is the opposite of what the simple "AI displaces workers" narrative would predict — but consistent with the larger-companies-do-everything-more reading: bigger, better-resourced firms file more WARN notices and adopt more AI. The AI-washing inquiry section dissects which interpretation the data supports.
Geography — per-capita burden
Top 15 states ranked by workers affected per million civilian labor force (2024 BLS baseline). Ranking corrects for absolute population size — small states with concentrated displacement rise to the top.
| Rank | State | Notices | Workers affected | Per million workforce |
|---|---|---|---|---|
| 0 | AK | 7 | 311 | 0.0 |
| 0 | AL | 98 | 11,608 | 0.0 |
| 0 | AZ | 187 | 27,823 | 0.0 |
| 0 | CA | 5,245 | 301,756 | 0.0 |
| 0 | CO | 238 | 19,176 | 0.0 |
| 0 | CT | 90 | 27,334 | 0.0 |
| 0 | DC | 55 | 6,857 | 0.0 |
| 0 | DE | 4 | 766 | 0.0 |
| 0 | FL | 354 | 22,623 | 0.0 |
| 0 | GA | 240 | 31,700 | 0.0 |
| 0 | IA | 410 | 18,288 | 0.0 |
| 0 | ID | 42 | 26,244 | 0.0 |
| 0 | IL | 475 | 55,478 | 0.0 |
| 0 | IN | 129 | 14,258 | 0.0 |
| 0 | KY | 163 | 15,455 | 0.0 |
Occupation forecast
12-month projected workforce displacement by 6-digit SOC occupation, computed by allocating each WARN filing's affected workers across that NAICS sector's BLS OEWS 2024 occupation distribution. Each row is cross-joined with our AI Replaceability index so you can see which displaced occupations are most exposed to AI substitution.
| # | Occupation (SOC) | Major group | Projected 12-mo displacement | AI replaceability | Median wage |
|---|---|---|---|---|---|
| 1 | Managers, All Other11-9199 | Management Occupations | 69,587±10,438 | 60.2 | $137k |
| 2 | Recycling and Reclamation Workers53-7062 | Transportation and Material Moving Occupations | 44,966±6,745 | 49.4 | $39k |
| 3 | Acute Care Nurses29-1141 | Healthcare Practitioners and Technical Occupations | 32,805±4,921 | 44.6 | $94k |
| 4 | Customer Service Representatives43-4051 | Office and Administrative Support Occupations | 23,490±3,524 | 90.6 | $43k |
| 5 | Web Administrators15-1299 | Computer and Mathematical Occupations | 22,440±3,366 | 68.5 | $109k |
| 6 | Shipping, Receiving, and Inventory Clerks43-5071 | Office and Administrative Support Occupations | 18,538±2,781 | 79.0 | $43k |
| 7 | Robotics Engineers17-2199 | Architecture and Engineering Occupations | 17,451±2,618 | 53.7 | $118k |
| 8 | Inspectors, Testers, Sorters, Samplers, and Weighers51-9061 | Production Occupations | 13,661±2,049 | 57.6 | $47k |
| 9 | Software Developers15-1252 | Computer and Mathematical Occupations | 11,054±1,658 | 68.0 | $133k |
| 10 | Team Assemblers51-2092 | Production Occupations | 6,995±1,049 | 54.7 | — |
| 11 | Fuel Cell Engineers17-2141 | Architecture and Engineering Occupations | 6,966±1,045 | 68.2 | $102k |
| 12 | Secretaries and Administrative Assistants, Except Legal, Medical, and Executive43-6014 | Office and Administrative Support Occupations | 6,828±1,024 | 91.8 | $46k |
| 13 | Retail Salespersons41-2031 | Sales and Related Occupations | 6,278±942 | 66.4 | $35k |
| 14 | Production Workers, All Other51-9199 | Production Occupations | 6,210±932 | 49.7 | $39k |
| 15 | Biologists19-1029 | Life, Physical, and Social Science Occupations | 6,100±915 | 53.5 | $93k |
| 16 | Construction Managers11-9021 | Management Occupations | 5,681±852 | 57.6 | $107k |
| 17 | Potters, Manufacturing51-9195 | Production Occupations | 5,452±818 | 52.2 | $46k |
| 18 | Biomass Power Plant Managers11-3051 | Management Occupations | 5,238±786 | 57.6 | $121k |
| 19 | Cooks, Restaurant35-2014 | Food Preparation and Serving Related Occupations | 5,207±781 | 35.2 | $37k |
| 20 | Office Clerks, General43-9061 | Office and Administrative Support Occupations | 5,166±775 | 90.9 | $44k |
| 21 | Heavy and Tractor-Trailer Truck Drivers53-3032 | Transportation and Material Moving Occupations | 5,063±759 | 52.9 | $57k |
| 22 | Architectural and Engineering Managers11-9041 | Management Occupations | 5,036±755 | 58.8 | $168k |
| 23 | Natural Sciences Managers11-9121 | Management Occupations | 5,028±754 | 60.9 | $161k |
| 24 | Medical and Health Services Managers11-9111 | Management Occupations | 4,779±717 | 88.7 | $118k |
| 25 | Maintenance and Repair Workers, General49-9071 | Installation, Maintenance, and Repair Occupations | 4,518±678 | 33.7 | $49k |
| 26 | Licensed Practical and Licensed Vocational Nurses29-2061 | Healthcare Practitioners and Technical Occupations | 4,516±677 | 42.0 | $62k |
| 27 | Janitors and Cleaners, Except Maids and Housekeeping Cleaners37-2011 | Building and Grounds Cleaning and Maintenance Occupations | 4,408±661 | 30.1 | $36k |
| 28 | Art Therapists29-1129 | Healthcare Practitioners and Technical Occupations | 4,029±604 | 43.9 | $65k |
| 29 | Social and Human Service Assistants21-1093 | Community and Social Service Occupations | 3,951±593 | 41.3 | $45k |
| 30 | Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders51-9021 | Production Occupations | 3,904±586 | 52.9 | $47k |
Methodology (hybrid LLM + NAICS). Each WARN notice's affected workers are allocated across ≤8 SOC occupations. For 11,865 of 12,003 notices (98.9%) the allocation comes from xiaomi/mimo-v2.5-pro via OpenRouter, which inspects company name + city/state + NAICS + worker count and returns a per-employer SOC distribution (so David's Bridal layoffs map to Retail Salespersons + Receptionists, not the generic "Retail Trade" NAICS-44 average). The remaining 138 notices fall back to BLS OEWS 2024 NAICS-SOC proportional allocation. Allocations are summed per SOC across the past 12 months. Confidence bounds are ±15% on the total.
Are layoffs drifting toward AI-exposed occupations?
For each quarter, the weighted-mean AI replaceability score of displaced workers (using the same per-notice LLM allocation as the forecast above). A rising line would mean layoffs are concentrating on more-AI-substitutable roles over time; a flat line means the occupation mix of layoffs has been stable.
Trend: slope -0.092 AI-points per year · Pearson r = -0.071 across 14 quarters. The mix of layoff occupations has been approximately stable — no clear secular drift toward higher- or lower-AI-exposure roles.
Share of layoffs by AI-exposure tier
Stacked view: what percent of each quarter's displaced workers fall into the high (AI ≥ 75), moderate (50–74), and low (< 50) exposure bands.
Share of affected workers by AI-replaceability tier per quarter. Growing red band over time = layoffs increasingly hitting AI-substitutable roles; stable bands = no tier shift.
Why this analysis requires the LLM allocation. NAICS-proportional occupation distributions are static per sector, so a pure BLS approach would produce a dead-flat quarterly AI exposure line regardless of what's happening in the labor market. Per-notice LLM allocation (BL-246) produces company-specific occupation mixes, which is what lets quarterly signal surface at all.
Top 25 companies by predicted 12-month layoff risk
Our cohort prediction model in content.warn_company_prediction scores 9,691 companies on their estimated probability of filing a WARN notice in the next 12 months, given prior-24-month layoff history, AI adoption posture, and sector base rates. The top 25 by predicted probability:
Predictions from content.warn_company_prediction — illustrative cohort modeling, not a deterministic forecast. Read the methodology section before citing.
Methodology
Source. Each filing originates from the public WARN roster published by its state Department of Labor. We ingest 61 state-level rosters across 44 US jurisdictions, normalize the 140+ event-type variants into five canonical buckets, geocode to state, and store the canonical record in content.warn_notices. The cleaned, self-hosted CSVs are available at /api/warn/export.
Coverage. 44 of 51 US jurisdictions have release-ready sources. AR, HI, KS, LA, NH, WY, and territories are either statutorily private (AR) or have no published public list. All rates should be interpreted as undercounts in non-release-ready states.
NAICS assignment. The source data contains no industry classification, so NAICS is derived by fuzzy-matching company_name_normalized against our 80,000+-company index (which has 100% NAICS coverage). Direct match rate is 0.4%; unmatched filings receive a sector-imputed AI adoption score per the scatter methodology above and are retained in all rollups.
Event classification. 140+ raw event-type strings are bucketized into five classes: layoff, closure, mass_layoff, amendment, other. Only the first three are counted in trend/sector rollups.
Rate model. The per-company layoff probability combines a sector- empirical base rate (12-month filings per matched company in each NAICS-2) with an AI-adoption coefficient fit on the matched subset via Poisson GLM (log link, IRLS solver, 6/6 unit tests). After the May 2026 BL-325 re-match against the expanded 80,879-company universe, the matched subset is now n = 1,247 companies (up from 47), making coefficient estimates substantially more stable. The model outputs flow into content.warn_company_prediction and surface in the Top 25 predicted layoff risk section.
Legal + ethical note. Company-specific risk bands are published only for firms we already list in our public AI-opportunity index. Bands are framed as statistical associations with historical filing behavior, not predictions of future actions. Corrections and takedowns welcome at our contact page.
Does the data support the "AI-washing" narrative?
Public discussion in 2026 has converged on a thesis: companies cite AI as a cover for layoffs that are really about cost-cutting. This is the "AI-washing" claim, raised by CNBC, Built In, and the top r/Economics threads of the past 30 days. Three sub-claims need testing against the data:
- Are AI-advanced companies overrepresented in WARN filings? The matched cohort comparison (Section Matched cohort vs universe) shows the matched cohort's avg AI score is 63 vs 58.1 for the universe (delta +4.9 points). The data says: yes, somewhat — but the difference is small enough that the cohort is dominated by larger firms with both more layoffs and more AI investment, not specifically by AI-leading firms cutting more.
- Do AI-advanced companies cut smaller numbers per filing? The company-level scatter (collapsed in Section: Does AI adoption predict layoffs?) shows no clear correlation between AI adoption score and total workers affected per company. Higher-score companies are spread across the full Y-range. The "AI-leading firms cut leaner" hypothesis gets no support.
- Are affected occupations more AI-exposed in advanced-firm filings? The AI exposure trend chart (Section: Are layoffs drifting toward AI-exposed occupations?) tracks the weighted-mean AI exposure score of all WARN-affected workers over time. If AI-washing were the dominant pattern, we'd expect rising weighted exposure scores as AI-advanced firms file more notices over advanced occupations. The trend is flat-to-mild, suggesting the dominant story is cyclical sector pressure, not AI-driven role substitution.
Bottom line: the data refutes the simplest "AI is causing the layoffs" framing at the cohort level. AI-advanced firms aren't strongly overrepresented, aren't cutting smaller, and aren't disproportionately cutting AI-exposed roles. That doesn't mean AI plays no role — it means the causal arrow needs better instrumentation than current public WARN data supports. The honest synthesis: AI is one input, not the primary driver, of the 2024–2026 layoff cycle visible in these filings. The headline finding (industry-level r = +0.33) is best read as "AI investment and labor restructuring are simultaneous responses to the same structural pressures" — not as AI substituting for workers in real time.
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