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Yes, AI can slow some work and speed up other work; there is no single result for every job. In a 2025 randomized trial, 16 experienced open-source developers took 19% longer to complete 246 selected issues when they could use AI tools. But that result describes one group, set of tasks and early-2025 workflow—not all developers or workers. Other studies report gains, while surveys and economy-wide statistics measure different things.
What does “AI slowing down” mean here?
The available evidence concerns work productivity: how long people take to do tasks, how much work they produce, and whether those changes show up in company or economy-wide measures. It does not establish that AI services respond more slowly, or that AI makes computers or internet connections slower.
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Those productivity questions need to be separated. A task can take less time without producing more measured output; a worker can produce more code without necessarily producing more valuable or higher-quality work; and local task gains do not automatically raise a company’s output per hour.
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METR’s early-2025 randomized trial
METR’s report, published in July 2025, describes a randomized controlled trial involving 16 experienced developers working on 246 issues in large open-source repositories they knew well. The tasks included bug fixes, features and refactors. Developers could choose their tools, and primarily used Cursor Pro with Claude 3.5 or 3.7 Sonnet. In that setting, developers took 19% longer when AI use was allowed. The measured outcome was implementation time for those repository tasks, not software-industry output as a whole.
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There was also a striking gap between perceived and measured speed. Before the trial, participants expected AI to make them 24% faster; after taking part, they still believed it had sped them up by 20%, despite the measured slowdown. That mismatch is a reminder that feeling faster is not the same as completing a task faster.
METR characterized the result as a snapshot of early-2025 AI capabilities in one relevant setting. The small, selected participant group and the time available to learn the tools limit what the trial can establish. METR explicitly cautioned that it does not show that AI fails to speed up most developers, let alone workers outside software development.
What METR’s later experiment changed
In February 2026, METR explained that its later experiment, which began in August 2025, was difficult to interpret because participants’ choices affected which work was observed. Some developers increasingly declined to work without AI, and some omitted tasks they did not want to attempt in the AI-disallowed condition. The later study included 57 developers, 143 repositories and more than 800 tasks, but METR said the observed data were an unreliable proxy for the true productivity effect. Its raw estimates suggested possible speedup, while the researchers warned that selection likely biased the estimate downward and that the size of any real increase remained uncertain.
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So the early trial is evidence about the people, work and tools it studied—not a current estimate that AI makes developers 19% slower. The later experiment does not provide a reliable replacement estimate.
What do other studies say about speed and output?
The results below are not competing estimates of one shared quantity. They differ in who took part, what they did, how AI was used and what the researchers measured.
| Study and setting | What it found | What the result measures |
|---|---|---|
| METR randomized trial, reported July 2025: 16 experienced open-source developers, 246 issues in familiar repositories, using tools primarily consisting of Cursor Pro with Claude 3.5 or 3.7 Sonnet | Tasks took 19% longer with AI allowed. Participants expected a 24% speedup beforehand and perceived a 20% speedup afterward. | Implementation time in that trial; perceived speed was separately self-reported. |
| St. Louis Fed analysis of U.S. survey responses collected in August and November 2024 | In November, generative-AI users estimated savings equal to 5.4% of their work hours on average; the average across all workers, including nonusers, was 1.4%. | Workers’ reported estimate of time saved compared with doing the same amount of work without AI. |
| Bank for International Settlements field experiment following Ant Group’s September 2023 launch of CodeFuse | The LLM group produced 55% more lines of code; statistically significant gains were concentrated among junior staff. | Code output in that company experiment. The authors attributed the smaller effect for senior programmers to lower engagement, not proof that the tool could not help them. |
| METR survey of 349 technical workers, conducted February–April 2026 | Respondents reported median value changes of 1.4x–2x and a median speed change of 3x. | Convenience-sample respondents’ estimates of perceived speed and value, not measured or causal productivity gains. |
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The METR trial focused on experienced developers’ implementation time on selected issues in familiar repositories. The CodeFuse field experiment measured lines of code in a company setting, with the significant gains primarily among junior staff. The St. Louis Fed figures reflect workers’ estimates of time saved, while METR’s 2026 survey asked technical workers about perceived speed and value. Different populations, tools, tasks, periods and outcomes can produce different findings without any one result disproving the others.
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In particular, the CodeFuse result does not establish a 55% increase in the value or quality of all programmers’ work. Lines of code are an output measure, not a direct measure of quality, and the reported significant gains were concentrated among junior staff.
Do workers’ reported time savings mean measured productivity is rising?
No—not by themselves. In the St. Louis Fed analysis, respondents who had used generative AI in the previous week estimated how many extra hours they would have needed to do the same amount of work without it. The resulting averages—5.4% of work hours among users and 1.4% across all workers—are self-reported counterfactual time savings, not a direct experiment measuring output. The Fed authors note that workers may not recognize all the time they save, and savings they do not recognize may not appear in productivity statistics either.
The distinction matters: a worker might finish a task sooner, use the remaining time on other work, or spend time checking and correcting AI output. A time-saving estimate does not tell us by itself which of those happened or whether total valuable output increased.
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METR’s February–April 2026 survey adds a different kind of perception measure. Among 349 technical workers in a convenience sample, median reported value change was 1.4x–2x and median reported speed change was 3x. METR cautioned that self-reported counterfactual estimates may be inaccurate and that the reported magnitudes could be overstated. These are what respondents said, not causal estimates of productivity.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why can task-level gains coexist with weak economy-wide results?
Saving time on selected tasks is only one step toward increasing output across a firm or an economy. The International Labour Organization’s May 6, 2026 brief put typical task-level AI productivity gains in a 10–70% range. That range does not mean every task or workplace gains by that amount. The brief also described mixed firm-level evidence, uneven adoption and no clear AI-driven productivity growth yet in official sectoral or macroeconomic statistics.
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The ILO’s June 2026 evidence review drew on experiments, firm-level data, platform studies, and worker and firm surveys from Australia, Denmark, Germany, Korea, Kuwait, the United Kingdom and the United States. It found that reported gains are real but uneven and often unverified. Worker-reported savings of a few percent of working hours had not yet translated into higher measured output, earnings or employment. The review also said large-scale job displacement remained limited.
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Measurement gaps, how widely tools spread, workers’ skills, workplace reorganization and complementary investment all affect whether local task improvements scale up. A worker finishing one task faster is therefore not, on its own, evidence that an organization or an economy is producing more per hour.
How to judge a claim that AI speeds up or slows down work
- Check who was studied. Experience level, job, organization and familiarity with the work can shape results.
- Check what the task required. A coding task with quality requirements may not resemble routine drafting or another kind of work.
- Check the workflow and tools. Tool versions, how people use them and time spent learning them can matter.
- Check the outcome. Elapsed task time, code volume, self-reported savings, perceived value and measured productivity are not interchangeable.
- Check the date and design. A randomized task trial, a company field experiment, a worker survey and official economic statistics answer different questions.
The most defensible answer is conditional: AI can add time in some workflows and save time in others. Whether either effect amounts to more valuable work depends on the task, the worker, the tool and how the result is measured.
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