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Best overall approach: use AI Layoffs for source-cited, employer-level events; BLS JOLTS to anchor those events against total U.S. layoffs; OECD.AI for labor-demand and skills indicators; AIExposure for downloadable occupation, industry, state and company-risk data; and AIimpacted as an independent event-level cross-check. No single dashboard measures all AI-driven job changes, so combining observed events, all-cause labor statistics and exposure estimates gives a more defensible picture.
The free tool stack at a glance
These services answer different questions. Event registers document announced job cuts, macroeconomic tables count layoffs for any reason, and exposure datasets estimate where automation could change work. Keeping those categories separate prevents an ordinary restructuring from being mislabeled as an AI layoff.
| Tool | Best use | What it provides | Update or coverage note | Main limitation |
|---|---|---|---|---|
| AI Layoffs | Primary event monitor | Monthly AI Layoffs Index, source-cited event register, open JSON API, methodology, evidence tiers and confidence bands | 110 source-cited events; coverage through August 2026 | Captures documented events, not every unannounced replacement or productivity-driven reduction |
| BLS JOLTS, Table 5 | U.S. macro baseline | Monthly layoffs and discharges level and rate | August 2026 provisional: 1.641 million layoffs and discharges, 1.0% rate | All-cause measure; it does not identify AI as the reason |
| OECD.AI | Labor-market and skills trends | Live indicators for labor demand, supply, skills, migration and related measures | Views can use different sources and methods | Definitions and geographies must be retained when comparing indicators |
| AIExposure | Downloadable risk and exposure analysis | CC BY 4.0 downloads covering occupations, industries, states and company fields | Download page lists a 2026-07-01 update | Exposure or risk is not proof that a layoff occurred |
| AIimpacted | Independent event cross-check | Employer-attributed events with a source and date for each record | 43 events covering 81,886 jobs from 13 January 2025 through 6 October 2026 | Documented floor, not a complete estimate of job losses |
| AI Crisis | Strict inclusion checks | Records requiring a headcount and an explicit AI or automation link | Explains why national WARN coverage is incomplete | Will omit cases that lack the required public evidence |
| JobShift | State-level triangulation | Combines WARN filings, public filings, ATS data and named news sources | Useful for joining administrative and labor-market signals | Mixed source types require careful definition and date tracking |
1. AI Layoffs: the best free event monitor
Start here when you need to know which employer announced what, when and why. The free service includes a live AI Layoffs Index that is updated monthly, a full source-cited event register, read access to an open JSON API, a published methodology, evidence tiers and confidence bands. Its FAQ says the register draws on SEC EDGAR, Challenger, Gray & Christmas, BLS/FRED, Census Business Trends and Outlook Survey (BTOS) data, and peer-reviewed exposure research.
The register contains 110 source-cited events with coverage through August 2026. Treat that number as the count of records meeting the register’s inclusion rules, not as the number of all jobs affected by AI. One event can represent a small team or a large workforce, and public announcements can omit contractors, backfills or quiet non-replacements.
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How to use it
- Open the monthly index and note the dashboard’s “as of” date before copying any value.
- For every event that matters to you, open the linked source in the register. Record the employer, location, announcement date, headcount, stated reason and evidence tier.
- Use the confidence band to distinguish an explicit employer attribution from a report that only infers an AI connection.
- For repeat monitoring, read the JSON API and save a dated snapshot so later revisions do not overwrite your historical record.
What makes an event strong evidence?
The strongest records contain an employer filing or statement that directly connects the reduction to AI or automation and gives a headcount or affected function. A news article saying that a company is “investing in AI” is not equivalent. When the source only describes a broad restructuring, keep the event in a lower evidence tier or exclude it from an AI-specific count.
2. BLS JOLTS: the denominator for U.S. layoffs
Use Table 5 from the U.S. Bureau of Labor Statistics Job Openings and Labor Turnover Survey (JOLTS) to show the scale of all U.S. layoffs. The August 2026 provisional release reports 1.641 million layoffs and discharges, a 1.0% rate. This is an all-cause estimate: it includes closures, seasonal changes, performance decisions, mergers and other reasons alongside any AI-related cuts.
JOLTS cannot tell you how many of those 1.641 million separations were caused by automation. Its value is contextual. If an event register lists several thousand AI-attributed jobs in a month, JOLTS lets you show the size of that documented subset without claiming that the entire national total was AI-driven.
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Correct comparison practice
- Label the figure “U.S. layoffs and discharges, all causes” and retain the word provisional for August 2026.
- Do not divide an event-register count by JOLTS and call the result the AI share unless both measures use compatible periods, units and populations.
- Keep the month and release status with every copied value because BLS can revise provisional estimates.
3. OECD.AI: monitor demand, skills and supply
OECD.AI is the better choice when your question is broader than layoffs: Are employers changing the skills they request? Which occupations or countries show rising demand? How are migration and labor supply moving? Its live AI jobs-and-skills portal brings those indicators together.
Do not treat two charts as directly comparable merely because both mention AI. OECD warns that indicators can use different data sources and methods. Preserve the indicator definition, geography, population, time window and unit in your notes. A job-posting measure, for example, describes advertised demand; it does not count filled jobs or layoffs.
Context from the OECD’s 2026 reporting
The OECD reports that 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. The report says labor-market disruptions tied to reported AI incidents have been rising steadily while remaining a modest share of all reported incidents. These are media-reported incidents, not a census of employment changes, so use them as a trend signal rather than a job-loss total.
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4. AIExposure: download occupation, industry and state data
Choose AIExposure when you need a file for analysis rather than a dashboard headline. Its downloads are licensed CC BY 4.0 and cover occupation risk, industry and state views, 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.
Questions the downloads can answer
- Which occupations have higher estimated exposure to AI-enabled task change?
- How do exposure patterns differ among industries or U.S. states?
- Which companies have both total-layoff and AI-related-layoff fields?
- Does a company-level record have a WARN filing, SEC disclosure or another documented source?
Exposure scores describe the potential for tasks or jobs to be affected; they do not establish that an employer eliminated those jobs. Keep a separate column for “observed event” and “exposure estimate” in any spreadsheet or database.
5. AIimpacted: a second event-level check
AIimpacted is useful for challenging a single-source narrative. It lists 43 employer-attributed AI job-loss events covering 81,886 jobs from 13 January 2025 through 6 October 2026, with a source and date on every event. The site explicitly describes the total as a floor because unannounced replacement and reductions that employers do not attribute to AI are not captured.
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Use the register to find omissions, not to average its total with AI Layoffs’. The two services may apply different inclusion rules, count the same announcement differently or update on different schedules. Compare the underlying event records, employer, date, geography and stated rationale before calling a difference a trend.
6. AI Crisis and JobShift: methodology and state-level triangulation
AI Crisis
AI Crisis applies a conservative rule: a record needs a headcount and an explicit AI or automation link. That approach reduces speculative entries, but it necessarily leaves out announcements where the employer gives no number or uses broad language. Its warning about national WARN data is important: there is no complete national WARN database, so a search of WARN notices cannot be treated as a complete AI-layoff census.
JobShift
JobShift combines WARN filings, public filings, applicant-tracking-system data and named news sources. This mixture can add state detail and lead time, but each source measures something different. A WARN filing is a legal notice, an SEC disclosure is a corporate filing, ATS data reflects recruiting behavior, and news is secondary reporting. Keep the source type beside each row rather than merging them into one undifferentiated count.
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How to decide whether a layoff was actually caused by AI
Attribution is the hardest part of this topic. Use a hierarchy so your count reflects what the evidence says.
- Explicit employer statement: the company names AI, automation or a specific technology as a reason for reducing roles.
- Filing with a stated link: an SEC filing, WARN notice or official announcement connects the headcount reduction to automation.
- Named executive or regulator statement: a documented quotation attributes the positions to AI, with a date and affected group.
- Corroborated reporting: multiple named sources connect the same reduction to AI, but the employer does not make the attribution directly.
- Inference only: the company adopts AI, changes a product or has a restructuring without saying that jobs were removed because of it. Do not count this as an AI layoff.
Record the exact wording, not just your interpretation. “We will automate customer support” describes a plan; “we eliminated 500 support roles because of automation” is an attributed event. If the announcement gives a range, preserve the range rather than choosing the largest number.
A repeatable monitoring workflow
Weekly event scan
- Check AI Layoffs and AIimpacted for new or revised records.
- Open the original filing, employer statement or named report for every candidate event.
- Capture employer, affected job group, headcount or range, announcement date, effective date, country or state, stated reason, source type, evidence tier and confidence band.
- Mark whether the number is announced jobs, estimated jobs, a WARN-notice maximum or an unannounced replacement inference.
Monthly macro and trend review
- Copy the latest BLS JOLTS Table 5 value and label its release status.
- Review OECD.AI indicators for changes in demand, skills, supply or migration, keeping each indicator’s definition and geography.
- Export or archive the AI Layoffs event data and note revisions to older records.
- Compare AIExposure occupation, industry and state downloads with event records, but do not combine exposure values with job-loss counts.
Suggested record structure
| Field | Why it matters |
|---|---|
| Employer and affected function | Prevents one company-wide headline from being mistaken for a single occupation |
| Headcount and unit | Separates people, positions, sites and percentage reductions |
| Announcement and effective dates | Allows correct monthly or quarterly grouping |
| Geography | Keeps country, state and local results comparable |
| Attribution wording | Shows whether AI was explicit, reported or inferred |
| Source and source type | Makes the entry auditable and exposes mixed-method totals |
| Evidence tier and confidence | Preserves uncertainty instead of presenting every record as equally certain |
| As-of date and revision note | Prevents a live dashboard revision from silently changing your historical analysis |
How to track automation by industry or state
For industry analysis, begin with AIExposure’s industry fields to identify where exposure is estimated to be high, then look for matching employer-attributed events in AI Layoffs or AIimpacted. Report the two outputs in separate columns: “estimated exposure” and “documented job-loss events.” A high-exposure industry with few documented events may reflect gradual productivity changes, missing disclosure or a lag between adoption and layoffs.
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For state analysis, use AIExposure’s state download and JobShift’s state-oriented sources. Check whether a record is a WARN filing, a public filing, ATS signal or news report. State totals can be distorted by headquarters reporting, multi-state reductions and different notice rules, so identify the location represented by each number.
Common mistakes and how to correct them
- Calling all layoffs AI layoffs: use JOLTS only as an all-cause baseline and require explicit attribution for an AI event count.
- Adding incompatible dashboards: compare definitions and duplicate events before totaling records from two registers.
- Confusing risk with outcomes: label AIExposure values as exposure or potential impact, never as confirmed cuts.
- Ignoring update dates: save the dashboard or download date and identify provisional or revised figures.
- Treating WARN as complete: remember that national WARN coverage is incomplete and many reductions fall outside its rules.
- Using a headline without its source: open the underlying filing or statement and preserve the original wording.
- Assuming silence means no AI effect: an employer may automate without announcing replacement; record that case as unconfirmed rather than forcing it into either category.
Which tool should you choose?
| Your question | Start with | Then verify with |
|---|---|---|
| “What AI-linked layoffs were announced this month?” | AI Layoffs | AIimpacted and the underlying source |
| “How large are layoffs overall in the United States?” | BLS JOLTS Table 5 | Release status and month |
| “Which skills or occupations are changing?” | OECD.AI | Indicator definition and geography |
| “Can I download state, industry or occupation risk data?” | AIExposure | License, update date and field definitions |
| “Can I build a conservative event list?” | AI Crisis | Employer attribution, headcount and source |
| “What is happening in a particular state?” | JobShift and AIExposure | WARN, filing, ATS and news source type |
What the numbers can—and cannot—tell you
The 43 AIimpacted events and 81,886 jobs are a documented floor within that site’s rules and date range, not a forecast of total displacement. The 110 AI Layoffs records are a separate source-cited event count through August 2026, not a national total. The 1.641 million JOLTS layoffs and discharges in August 2026 are a provisional all-cause U.S. estimate, not an AI-attributed figure. OECD’s 5.2% share describes media-reported incidents in Q3 2025, not the percentage of workers laid off. Keeping those units and definitions visible is the difference between useful monitoring and a misleading headline.
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