The Tool Desk
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This stack is free to access, but the numbers answer different questions. A defensible monitor records the employer, stated reason, headcount, date, geography, inclusion rule, confidence and the source’s “as of” date for every entry.
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Best free tools at a glance
| Tool | Best use | What it provides | Timing or update detail |
|---|---|---|---|
| AI Layoffs | Focused event monitoring | Monthly AI Layoffs Index, source-cited event register, open JSON API, methodology, evidence tiers and confidence bands | FAQ reports 110 source-cited events through August 2026 |
| BLS JOLTS, Table 5 | Macro baseline for U.S. layoffs | Layoffs and discharges level and rate | August 2026 provisional release: 1.641 million and a 1.0% rate |
| OECD.AI | Labor demand, supply and skills context | Live indicators for jobs, skills, migration and related measures | Methods and source coverage vary by indicator |
| AIExposure | Occupation, industry, state and company analysis | CC BY 4.0 downloads; fields include total layoffs, AI-related layoffs, WARN filings and SEC disclosures | Download page lists a 2026-07-01 update |
| AIimpacted | Independent event cross-check | Employer-attributed events with a source and date on each record | 43 events covering 81,886 jobs from 13 January 2025 to 6 October 2026 |
| AI Crisis | Strict inclusion rules | Records require a headcount and an explicit AI or automation link | Warns that no complete national WARN database exists |
| JobShift | State-level triangulation | Combines WARN filings, public filings, ATS data and named news sources | Use its methodology to understand how records are assembled |
What a good AI-layoff tracker must tell you
Before choosing a dashboard, decide whether you need confirmed events, a broad labor-market denominator, forward-looking exposure or a geographic view. Compare every candidate on four axes.
Attribution strength
The strongest record contains an employer statement that connects a job reduction to artificial intelligence or automation, plus a named source and date. A filing that only says “restructuring,” “cost reduction” or “efficiency” is not proof of an AI cause. AI Crisis therefore requires both a headcount and an explicit AI or automation link. AI Layoffs adds evidence tiers and confidence bands so you can distinguish direct statements from weaker signals.
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Coverage
Check whether the dataset covers the countries, states, industries and employer sizes you care about. A worldwide event list will not answer a question about one U.S. state, while an occupation-exposure file will not tell you which employers announced cuts. Keep geography attached to every row rather than combining national and state totals without labeling them.
Timeliness
A live portal, a monthly index and a historical download can all be useful. They are not interchangeable. Record the publication or update date, and expect live dashboards to revise historical values. A monthly count should never be presented as a real-time total.
Auditability
Prefer records that expose the underlying filing, employer statement or named news report, explain inclusion rules and offer downloadable data. Auditability lets you challenge an attribution, remove a duplicate and reproduce a chart months later.
AI Layoffs: the focused event monitor
Use AI Layoffs as the primary watchlist for announced, source-cited events. Its free tier includes a live AI Layoffs Index updated monthly, a full event register, read access to an open JSON API, methodology notes, evidence tiers and confidence bands. The site’s FAQ says the register draws on SEC EDGAR, Challenger, Gray & Christmas, BLS/FRED, Census BTOS and peer-reviewed exposure research. It reports 110 source-cited events with coverage through August 2026.
How to use the register
- Open each newly added event and save the employer name, announcement date, affected geography and headcount exactly as displayed.
- Read the cited filing or statement instead of relying on the dashboard label. Look for wording that explicitly attributes reductions to AI, automation or a named technology program.
- Copy the evidence tier and confidence band into your own notes. Do not turn a lower-confidence signal into a confirmed causal count.
- Use the JSON API or register export to preserve a dated snapshot. Add an “as of” field because later revisions can change historical rows.
- Compare the event with AIimpacted before publishing a total. A match raises confidence that the event is not a duplicate; a mismatch is a reason to inspect both sources.
What it cannot establish
An event register counts disclosed cases, not every job that may have been replaced by software. Employers can automate work without announcing it, and an announcement can cite several reasons at once. Treat the index as a documented event series, not a census of AI-caused unemployment.
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BLS JOLTS: the all-cause denominator
Use BLS JOLTS Table 5 to anchor the size of the overall U.S. layoffs market. The August 2026 provisional table reports 1.641 million layoffs and discharges and a 1.0% rate. That figure covers all causes, so it must not be relabeled as AI-caused layoffs.
How to compare it safely
- Keep the JOLTS month, provisional status, level and rate in separate fields.
- Compare an AI-attributed event count with the JOLTS total only as context, never as a subtraction that produces “non-AI layoffs.”
- Use the same geography and period when making a chart. A global event register and a U.S.-only monthly survey do not share a denominator.
- Expect later BLS revisions; preserve the release vintage used in your analysis.
JOLTS answers “how many layoffs and discharges occurred in the U.S. labor market?” It does not answer “how many were caused by AI?” That distinction is the most important guardrail in any trend report.
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OECD.AI’s live AI jobs-and-skills portal helps explain what is changing around the events. It covers labor demand, labor supply, skills, migration and related indicators. Use it when you need to know whether an occupation or skill is gaining or losing demand, rather than when you need an employer’s layoff announcement.
Preserve each indicator definition
OECD warns that its indicators use different data sources and methods. Record the indicator name, definition, geography, period and source before comparing two views. A change in a job-posting measure is not directly comparable with a change in an administrative employment series. Avoid ranking countries or states until you have confirmed that the units and coverage match.
Use it with event data
Pair an AI Layoffs event with the relevant OECD.AI occupation or skill view to test whether the surrounding labor market is moving in the same direction. Treat that as triangulation, not proof of causality: a fall in postings can reflect business cycles, seasonality or sampling changes as well as automation.
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AIExposure: downloadable risk and company fields
AIExposure is the most useful free option when the question is broader than announced layoffs. Its downloads are licensed CC BY 4.0 and cover occupation risk, industry and state analysis, plus company-level fields such as total layoffs, AI-related layoffs, WARN filings and SEC disclosures. The download page lists a 2026-07-01 update.
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Separate exposure from observed events
An occupation-risk score estimates potential sensitivity to AI; it does not count people who lost jobs. Industry or state exposure similarly describes potential disruption, not confirmed announcements. Keep a field named “measure type” with values such as observed event, aggregate labor statistic or exposure estimate so a chart cannot silently mix them.
Practical download workflow
- Download the release and record the 2026-07-01 update date, license and file name.
- Choose one grain for your analysis: occupation, industry, state or company. Do not average rows with different grains.
- For companies, keep total layoffs, AI-related layoffs, WARN filings and SEC disclosures as separate columns; they represent different evidence channels.
- Join only on stable identifiers and document any name or geography mapping. If a row lacks a direct event source, label it as exposure or disclosure data rather than a confirmed AI layoff.
- When publishing a result, state that the file is an exposure or compiled-disclosure dataset and identify its update date.
AIimpacted: an independent event-level check
AIimpacted records employer-attributed cases worldwide and places a source and date on every event. Its page reports 43 events covering 81,886 jobs from 13 January 2025 through 6 October 2026. The site explicitly says to treat that total as a floor, not an estimate, because unannounced replacement is not captured.
Why the totals differ from AI Layoffs
Different inclusion rules, geographic coverage, duplicate handling and update dates can produce different counts without either list being “wrong.” Compare the employer, date, headcount and source for each apparent match. Never add the two totals together; use the second list to find omissions, duplicates or attribution disagreements.
AI Crisis and JobShift: methodology and state triangulation
AI Crisis
AI Crisis applies a strict requirement: a record needs a headcount and an explicit AI or automation link. That rule improves attribution quality but can exclude real changes where an employer did not disclose a reason or headcount. Its warning that no complete national WARN database exists is a reminder that WARN-based totals are inherently incomplete.
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JobShift
JobShift describes combining WARN filings, public filings, ATS data and named news sources. Such a mixed-source approach can broaden state coverage, but each source has different timing and definitions. Keep the origin of every row so a WARN notice is not mistaken for an employer-confirmed AI explanation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to decide whether a layoff was actually caused by AI
Use a repeatable evidence ladder instead of guessing from headlines.
- Find an explicit statement. Look for the employer saying that AI, automation or a specific AI deployment eliminated or changed the roles.
- Verify the headcount. Record whether the number is announced jobs, a filing estimate, a range or an inferred total. Do not convert a percentage into a headcount without a stated workforce denominator.
- Check the date and geography. Tie the announcement to a country, state or site and to the period in which jobs were affected.
- Read the primary document. Use the SEC filing, WARN notice, employer communication or named report cited by the tracker. A summary alone can omit a second reason.
- Classify the causal language. Mark “explicit AI/automation,” “AI mentioned among several reasons,” “technology investment without job attribution” or “no AI evidence.” Only the first category supports a strong AI-attributed count.
- Look for duplicates. The same reduction can appear in a filing, a news report and two dashboards. Match employer, location, date and headcount before counting it once.
- Assign confidence and preserve uncertainty. Carry the tracker’s evidence tier or confidence band into your own dataset; do not upgrade it because two secondary articles repeat the claim.
How to monitor industries and states for free
Start with AIExposure for occupation, industry and state fields, then use JobShift or AI Crisis to inspect state-level announcements. Use OECD.AI to add labor-demand and skills context, and AI Layoffs or AIimpacted to verify whether a visible employer event exists.
Industry view
- Choose a consistent industry classification and keep the classification version with the export.
- Plot exposure or risk separately from announced headcounts.
- For each large change, inspect company-level WARN and SEC fields and search the event registers for an explicit explanation.
State view
- Keep state totals limited to records that actually identify a state; do not infer a state from a company’s headquarters.
- Label WARN-derived rows as WARN data and note that national coverage is incomplete.
- Compare state patterns with the all-U.S. JOLTS baseline only after aligning month and measure type.
Context figures worth publishing, with their limits
The OECD’s 2026 report says 5.2% of media-reported AI incidents were labor-market disruptions in Q3 2025; its six-month rolling average was about 4.5% by mid-2025. These are shares of media-reported AI incidents, not shares of all layoffs or of all jobs. Present the denominator and period whenever you quote them.
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The August 2026 JOLTS figure of 1.641 million layoffs and discharges is a provisional U.S. all-cause estimate with a 1.0% rate. It supplies scale, not AI attribution.
AIimpacted’s 43 events and 81,886 jobs through 6 October 2026 are a documented floor. The site’s warning matters: unannounced replacement is not captured, so the total cannot be treated as a complete estimate.
A practical weekly and monthly monitoring routine
Every week
- Scan new entries in AI Layoffs and AIimpacted.
- Open the underlying source for each event.
- Record employer, stated reason, headcount, date, geography, evidence tier and confidence.
- Flag duplicates and mark whether attribution is explicit, mixed or absent.
Every month
- Export or copy the event data and save the update date.
- Compare the event list with the latest BLS JOLTS release, keeping JOLTS labeled as all-cause.
- Review OECD.AI for changes in skills, occupations and labor-demand indicators.
- Refresh AIExposure downloads when a new release appears and note the release date and CC BY 4.0 license.
- Review AI Crisis and JobShift for state-level additions or methodological changes.
When publishing a chart
- Put the dataset name, geography, period, update date and inclusion rule in the caption.
- Use different colors or panels for event counts, all-cause layoffs and exposure scores.
- Include a note when a value is provisional, revised, employer-attributed or a documented floor.
- Keep the underlying row-level notes so readers can audit a surprising jump.
Common failure modes and fixes
Calling every restructuring an AI layoff
Fix: require explicit causal language or label the event as unconfirmed. Cost cutting and automation investment are not interchangeable.
Adding incompatible totals
Fix: do not add AI Layoffs and AIimpacted counts, or an exposure score and a headcount. Reconcile records first and preserve measure type.
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Fix: present the 1.641 million August 2026 figure as an all-cause U.S. baseline with its provisional status and 1.0% rate.
Ignoring revisions
Fix: save monthly snapshots and cite the release or update date. Live dashboards can revise historical values.
Assuming WARN data is complete
Fix: state that no complete national WARN database exists and describe the states and sources actually covered.
Comparing unlike OECD.AI indicators
Fix: preserve each indicator’s definition, geography, source and method before ranking or calculating a trend.
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- You need confirmed event alerts: start with AI Layoffs, then cross-check AIimpacted.
- You need a denominator for a U.S. story: add BLS JOLTS Table 5 and keep it explicitly all-cause.
- You need occupations, industries or states: download AIExposure and label every result as exposure, disclosure or observed event.
- You need skills and labor-demand context: use OECD.AI, preserving the indicator definition and geography.
- You need strict attribution: apply AI Crisis’s headcount-plus-explicit-link rule.
- You need state-level triangulation: inspect JobShift’s WARN, public-filing, ATS and named-news inputs.
The most reliable free setup is therefore a layered one: event registers for what employers have said, JOLTS for the scale of all layoffs, OECD.AI for changing labor demand and skills, and AIExposure for structured risk and geography. Keeping those measures separate is what makes an AI-layoff monitor useful rather than misleading.
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