AI Layoffs

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org.ailayoffs/ai-layoffsv1.0.0 Domain verified

Source-cited register of layoffs linked to AI, plus a live 0-100 AI job-loss index. No key.

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count_ai_job_losses Read-only

Answers 'how many jobs has AI replaced (or cost) this year?' with the register's published count: roles disclosed in layoffs linked to AI, roles where the employer itself named AI, the evidence-weighted headline figure, and the independent Challenger, Gray & Christmas count of US cuts attributed to AI. Each figure carries its scope (worldwide or US), its period and its definition, plus the ready-made answer sentence. The totals are computed over the whole register, so they are the published figures, not a sum of search results. Takes no arguments.

This tool takes no arguments.

Tools (4)

count_ai_job_losses

read-only

No arguments.

Input schema (JSON)

{
  "type": "object",
  "properties": {}
}

get_ai_layoffs_index

Current reading of the AI Layoffs Index, a 0-100 score of AI-attributed job-loss pressure scaled against AI's own history since 2023 (not a share of all jobs). Returns the value, its band, the uncertainty range, the change vs last month, the three weighted components with what each reads, the as-of date, and a ready-made citation string. Takes no arguments.

read-only

No arguments.

Input schema (JSON)

{
  "type": "object",
  "properties": {}
}

get_company_ai_layoffs

Did a specific company cut jobs because of AI? Returns the register's verdict for that company (explicit, mixed or blamed: the strongest grade among its cuts), its answer (the strongest position its own words hold: declared, cited, conflicting, attrition, denied, not-named, or statement for a workforce statement with no cut), a plain-language summary, the roles it disclosed vs the roles counted as AI-attributed, and every recorded event with the employer's own words and source. It also lists the company's later cuts that the register checked and left out because neither the employer nor credible press linked them to AI (later_cuts_not_ai_linked, never counted), so a question about the newest layoff is answered either way. Accepts a company name such as 'Klarna' or 'Salesforce'. If the company is not in the register, says so and what that does and does not mean; for a notable layoff the register checked and found not linked to AI, it returns that answer with the employer's own reason and source.

read-only

Argument	Type	Description
company*	string	Company name, e.g. 'Klarna', 'IBM', 'Salesforce'. Partial names work.

Input schema (JSON)

{
  "type": "object",
  "required": [
    "company"
  ],
  "properties": {
    "company": {
      "type": "string",
      "pattern": "\\S",
      "maxLength": 120,
      "minLength": 1,
      "description": "Company name, e.g. 'Klarna', 'IBM', 'Salesforce'. Partial names work."
    }
  },
  "additionalProperties": false
}

search_ai_layoff_events

Search the source-cited register of layoffs linked to AI (one row per event, 2023 to date). Filter by company, free text, attribution, evidence tier, execution status, country, sector, affected role and date range; call with no arguments for the most recent events. Each event returns the employer's own stated reason (claim), roles counted vs reported but not counted, execution status, and a link to its source and its ailayoffs.org company page; set full_context for each event's longer context paragraph.

read-only

Argument	Type	Description
role	string	Affected function or occupation, partial match, e.g. 'customer service', 'engineering'.
sort	string	newest first (default), oldest first, or the largest disclosed cut first.
limit	integer	Maximum events to return (default 10, at most 50).
query	string	Free text matched against the company, the stated reason, the context paragraph, sector, country, affected roles and source name. Every word must appear. Example: 'customer service'.
since	string	Earliest event date, inclusive: YYYY, YYYY-MM or YYYY-MM-DD.
until	string	Latest event date, inclusive: YYYY, YYYY-MM or YYYY-MM-DD.
sector

{ "type": "object", "properties": { "role": { "type": "string", "pattern": "\\S", "maxLength": 80, "description": "Affected function or occupation, partial match, e.g. 'customer service', 'engineering'." }, "sort": { "enum": [ "newest", "oldest", "largest" ], "type": "string", "description": "newest first (default), oldest first, or the largest disclosed cut first." }, "limit": { "type": "integer", "maximum": 50, "minimum": 1, "description": "Maximum events to return (default 10, at most 50)." }, "query": { "type": "string", "pattern": "\\S", "maxLength": 200, "description": "Free text matched against the company, the stated reason, the context paragraph, sector, country, affected roles and source name. Every word must appear. Example: 'customer service'." }, "since": { "type": "string", "pattern": "^\\d{4}(-\\d{2}(-\\d{2})?)?$", "description": "Earliest event date, inclusive: YYYY, YYYY-MM or YYYY-MM-DD." }, "until": { "type": "string", "pattern": "^\\d{4}(-\\d{2}(-\\d{2})?)?$", "description": "Latest event date, inclusive: YYYY, YYYY-MM or YYYY-MM-DD." }, "sector": { "type": "string", "pattern": "\\S", "maxLength": 80, "description": "Sector, partial match, e.g. 'Financial', 'Software'." }, "company": { "type": "string", "pattern": "\\S", "maxLength": 120, "description": "Company name or part of it, e.g. 'Klarna'." }, "country": { "type": "string", "pattern": "\\S", "maxLength": 80, "description": "Country as recorded, partial match, e.g. 'United States', 'India', 'Sweden'. For US-based roles use us_only." }, "us_only": { "type": "boolean", "description": "Only events whose affected roles are US-based." }, "execution": { "type": "array", "items": { "enum": [ "executed", "partial", "announced", "reversed", "unknown" ], "type": "string" }, "description": "Keep only these execution statuses. executed: The reduction has been carried out. partial: Some of the cut is done, the rest pending. announced: A stated plan, not yet carried out (often multi-year). unknown: Execution status not established. reversed: The cut was rolled back or rehired against (e.g. Commonwealth Bank)." }, "attribution": { "type": "array", "items": { "enum": [ "explicit", "mixed", "blamed" ], "type": "string" }, "description": "Keep only these attribution levels. explicit: The company itself declared the layoff AI-related, without citing other material factors. blamed: A credible source named AI, but the company did not. Shown as context and never counted: the register's standard requires the employer's own words to name AI, so a blamed event is a failed claim, not a discounted one. mixed: AI was cited alongside other material factors (cost, demand)." }, "counted_only": { "type": "boolean", "description": "Only events whose roles the register counts as AI-attributed (drops events that are reported but not counted)." }, "full_context": { "type": "boolean", "description": "Also return each event's context paragraph. Several times longer, so best with a small limit; get_company_ai_layoffs always includes it for one company." }, "evidence_tier": { "type": "array", "items": { "enum": [ "tier1", "tier2", "tier3" ], "type": "string" }, "description": "Keep only these evidence tiers. tier1 (Primary-source attributed): AI named as a workforce driver in the company's own SEC filing, on-record earnings call, or official statement, with the event corroborated by a structured source. tier2 (Reputable-press attributed): AI named as a cause by credible journalism quoting a named company source, but not yet in a company filing. tier3 (Inferred / single-source): Attribution from one secondary tracker, an unnamed source, or vague forward-looking language." } }, "additionalProperties": false }