Will AI regulation become law by 2026? [ resolves 2027-01-01 (179D) ]
AI regulation by 2027?
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 | ||
|---|---|---|---|---|---|---|---|---|
| AI regulation by 2027 | 2027-01-01 | 18.0% | $3 | $7.6K | A | KXAILEGISLAT…N01 |
resolution architecture
| venue | proposer | source | citation | arbitration | class | analyst notes |
|---|---|---|---|---|---|---|
| kalshi | Exchange Staff | Library of Congress | not provided | Kalshi Staff | Other | — |
verbatim rules
kalshi
If a bill becomes law regulating AI by Jan 1, 2027, then the market resolves to Yes.
The bill must impose one of the following restrictions on products using large language models: forbid their creation; set limits on how they're trained, for example by limiting access to previously usable training data or by setting limits on the number of parameters they may be trained with; prevent their use for certain applications or uses, such as interacting with customers, interfacing with other applications, or performing actions on the web; or restrict the ability of US citizens to use foreign LLM-based products or restrict US operated LLM products or businesses from being sold to foreign customers or entities.
The export restriction must be specific, and not universally applied to all exports or exports of a broad industry. For example, a blanket ban on exports to a specific country would not qualify, and neither would a blanket ban on allowing the purchase of US businesses by investors in a specific country. Introducing a ban that specifically limits the export of artificial intelligence or machine learning software to a specific country or a broad number of countries would qualify if it was known to apply to LLM products. An introduced bill specifically classifying artificial intelligence software or machine learning models such that they would newly qualify for existing export bans would qualify.
platform source field
kalshi.settlement_sources → "Library of Congress" sources (1) · single authority
- Library of Congress source of record venue-listed
recent wire items
monitoring status: active no active wire
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CM Signal’s news-cycle scan surfaces the day’s top stories alongside the prediction markets pricing them. When a story references this event, its wire is published here and links back to this page.
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/kxailegislation-27/ |
| JSON | REST API for developers | https://api.clearmarket.fyi/v1/events/kxailegislation-27 |
| MCP | agentic AI tool call (Claude Desktop, Cursor, Continue) | clearmarket.get_event("kxailegislation-27") |
| AGENT | AI crawler discovery index | /llms.txt |
Snapshot 2026-07-06. 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-SL62VN0HR4",
"slug": "kxailegislation-27",
"question": "Will AI regulation become law by 2026?",
"category": "politics",
"tags": [
"politics",
"ai-regulation",
"us-politics",
"legislative-outcome",
"technology-policy",
"2026"
],
"venues_covered": [
"kalshi"
],
"market_count": 1,
"cumulative_volume_usd": 7626,
"resolution_clarity_grade": "A",
"rcg_score": 90,
"rcg_caps": [],
"resolution_source": "Library of Congress",
"resolution_source_url": "https://www.congress.gov/",
"source_status": "platform_named",
"source_of_record": "Library of Congress",
"resolution_source_list": [
{
"name": "Library of Congress",
"url": "https://www.congress.gov/",
"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"
}
}
}