More tech layoffs in 2026 than in 2025? [ resolves 2027-03-01 (209D) ]
More tech layoffs in 2026 than in 2025?
kalshi: [A] single source·objective outcome methodology ›
markets · by resolution date · marks: daily snapshot
kalshi 1 market
| market | resolves | P(YES) | vol (24h) | vol (cum) | RCG | venue id | ||
|---|---|---|---|---|---|---|---|---|
| More tech layoffs in 2026 than in 2025 | 2027-03-01 | 90.4% | $77 | $28.42M | A | KXLAYOFFSYIN…000 |
resolution architecture
| venue | proposer | source | citation | arbitration | class | analyst notes |
|---|---|---|---|---|---|---|
| kalshi | Exchange Staff | FRED | link | Kalshi Staff | Other | — |
verbatim rules
kalshi
If there are more than 447,000 layoffs in the information sector in 2026, then the market resolves to Yes.
Important information: This market was listed using an incorrect underlying value for tech layoffs in 2025. The correct floor strike is 447,000 layoffs, not 494,000 as currently specified. If the final layoff count falls between those two numbers, we will pay out $1.00 to all traders with an open position as of March 13, 2026 at 5:00 PM ET. The rulebook variable has been updated accordingly. Trades executed after this time are not eligible for reimbursement.
platform source field
kalshi.settlement_sources → "FRED" ↗ sources (1) · single authority
FRED committed source
recent wire items
- More tech layoffs in 2026 than 2025 stays heavily favored Kalshi 95%
- Kalshi prices a 95% chance that 2026 sees more tech-sector layoffs in total than 2025, despite currently low weekly claims.
- The 197,000 claims print reflects a resilient labor market overall, yet the Kalshi contract suggests prediction-market participants expect tech-specific layoffs to accelerate through year-end.
- The weekly claims data and tech-layoff contract measure different things: broad layoffs vs. sector-specific headcount cuts, so the two readings are not directly contradictory.
- Resolves via FRED data; the contract captures annual tech-layoff totals, not weekly claims, so near-term labor strength alone does not settle it.
- More tech layoffs in 2026 than 2025 stays heavily favored Kalshi 95%
- Kalshi prices a 95% probability that 2026 tech layoffs will exceed 2025 totals, resolving via FRED data.
- Weak payroll headlines and ongoing cost-cutting in tech are consistent with the near-certain pricing; the market is not fading the layoff narrative.
- Resolves via FRED official data series tracking tech-sector layoffs; the year must close before a final count can be confirmed.
- More tech layoffs in 2026 than 2025 stays heavily favored Kalshi 95%
- Kalshi puts 95% odds on 2026 tech layoffs exceeding 2025 totals, a heavily favored outcome.
- The weak 90,000 ADP print and slowing wage growth are consistent with the broad labor softening this contract implies.
- Resolution via FRED data; the ADP figure is a private-sector proxy, not the official measure, so the final read depends on reported tech-sector layoffs in FRED.
- No companion contract with a price is available to frame a term-structure comparison.
- Tech layoffs outpacing 2025 levels solidifies near full pricing Kalshi 91%
- Kalshi prediction market prices 91% probability that 2026 tech layoffs will exceed the 2025 total.
- Rising job openings coexist with deep public pessimism, consistent with a market pricing continued tech-sector displacement even as aggregate demand holds.
- The 91% reading leaves little room for upward revision; the market is near fully committed to a worse-than-2025 tech layoff year.
- Resolves via FRED data tracking layoff and discharge rates in the technology sector for the full calendar year 2026.
- Tech layoff consensus solidifies above 2025 levels Kalshi 89%
- Kalshi places 89% odds that 2026 tech layoffs exceed 2025 totals, resolving via FRED data.
- The Challenger report showing tech cuts at their highest since 2023 is consistent with this elevated probability.
- AI-driven displacement is now the stated primary reason for cuts three months running, reinforcing the structural rather than cyclical read.
- A companion Kalshi contract on white-collar layoffs broadly (CM-EVT-0QDTY1D1Y1) sits at 72%, showing markets view tech as the hardest-hit segment within a wider white-collar trend.
programmatic access · four surfaces, same payload
One canonical record at every surface — embedded JSON-LD, REST, MCP, and /llms.txt.
| HTML | browsers, AI grounded search, crawlers (embedded JSON-LD @type: Dataset) | https://clearmarket.fyi/events/kxlayoffsyinfo-26/ |
| JSON | REST API for developers | https://api.clearmarket.fyi/v1/events/kxlayoffsyinfo-26 |
| MCP | agentic AI tool call (Claude Desktop, Cursor, Continue) | clearmarket.get_event("kxlayoffsyinfo-26") |
| AGENT | AI crawler discovery index | /llms.txt |
Snapshot 2026-08-04. Venue data via Kalshi + Polymarket APIs. Editorial fields (tags, editorial_notes) are ClearMarket-drafted with AI assistance under editorial review. Derived fields (venues_covered, resolution_clarity_grade, rcg_score) computed at serve time. Full per-field map in the JSON record under field_provenance.
raw JSON record · same payload returned by REST endpoint {
"event_id": "CM-EVT-ZTGN9MPFL9",
"slug": "kxlayoffsyinfo-26",
"question": "More tech layoffs in 2026 than in 2025?",
"category": "economics",
"tags": [
"economics",
"tech-layoffs",
"employment-metrics",
"2026-economics",
"year-over-year-comparison",
"labor-market"
],
"venues_covered": [
"kalshi"
],
"market_count": 1,
"cumulative_volume_usd": 28423407,
"resolution_clarity_grade": "A",
"rcg_score": 96,
"rcg_caps": [],
"resolution_source": "FRED",
"resolution_source_url": "https://fred.stlouisfed.org/series/JTU5100LDL#",
"source_status": "platform_named",
"source_of_record": "FRED",
"resolution_source_list": [
{
"name": "FRED",
"url": "https://fred.stlouisfed.org/series/JTU5100LDL#",
"provenance": "platform_api"
}
],
"arbitration_model": "kalshi_staff",
"proposer_model": "platform_staff",
"field_provenance": {
"question": {
"source": "clearmarket_editorial"
},
"tags": {
"source": "clearmarket_editorial",
"ai_drafted": true
},
"resolution_clarity_grade": {
"source": "derived",
"method": "rcg_v2_7factor"
},
"venues_covered": {
"source": "derived"
}
}
}