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Sometimes, but not universally. In a narrow early-2025 trial, experienced open-source developers took longer to complete selected tasks when AI tools were allowed. Other studies and surveys report faster work or time savings, but they examine different workers, tasks, tools and outcomes. The evidence does not establish that AI slows work in general—or that it speeds every job up.
Here, “slowing down” means the effect of AI on work productivity. The evidence summarized below does not establish whether AI services respond more slowly or whether AI makes computers or internet connections slower.
What the studies actually measured
“Productivity” can mean elapsed time to finish a task, the amount of work produced, a worker’s estimate of time saved, or output per hour across a company or economy. Those are related questions, but one result cannot stand in for all the others. A tool might help someone produce more code without reducing time on a particular task; a worker might report saving time without an official productivity measure showing higher output.
| Study and setting | What was measured | Reported result | What the result does not establish |
|---|---|---|---|
| METR randomized trial, reported July 2025; 16 experienced open-source developers and 246 issues | Implementation time on selected issues in familiar repositories | Participants took 19% longer when AI tools were allowed | Whether AI slows most developers, other workers, or work outside this particular setting |
| St. Louis Federal Reserve analysis of U.S. survey responses collected in November 2024 | Workers’ estimates of time they would have needed without generative AI | Average reported savings were 5.4% of work time among users and 1.4% across all workers | Whether measured output rose by those amounts |
| Bank for International Settlements field experiment, reported in 2024; programmers using CodeFuse | Lines of code produced by the treatment group compared with a control group | The LLM group produced 55% more lines of code; statistically significant gains were primarily among junior staff | Whether other teams, tools or output-quality measures would show the same result |
| METR survey, reported May 2026; 349 technical workers responding during February–April 2026 | Workers’ self-reported estimates of changes in speed and value | Median reported value change was 1.4x–2x; median reported speed change was 3x | A causal or objectively measured productivity increase |
| International Labour Organization evidence review and brief, 2026 | Evidence ranging from task-level experiments to firm and economy-wide measures | The May brief described task-level gains typically in a 10–70% range; the June review said gains were uneven and often unverified | That every task or workplace sees gains, or that official statistics already show clear AI-driven productivity growth |
These results should be read with their dates and methods attached. A controlled task experiment, a company field experiment, a worker survey and an economy-wide statistic answer different questions.
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Why one coding trial found slower work
METR’s early-2025 randomized trial
METR’s July 2025 report described a randomized controlled trial involving 16 experienced open-source developers and 246 issues in large repositories with which the developers were familiar. The tasks included bug fixes, feature work and refactoring. Participants could choose their tools; they primarily used Cursor Pro with Claude 3.5 or 3.7 Sonnet. On the trial’s measure—implementation time for those selected tasks—developers took 19% longer when AI was allowed.
The trial also found a mismatch between perception and measurement. Before participating, developers expected AI to make them 24% faster; afterward, they believed it had made them 20% faster. Those figures describe participants’ expectations and perceptions, not measured task speed. METR characterized the result as a snapshot of early-2025 AI capabilities in one relevant setting and cautioned that it did not show AI fails to speed up most developers or workers outside software development.
The finding is informative precisely because it is specific: experienced contributors were working in repositories they knew, on tasks intended to reflect work in those projects. It is not an estimate for all software development, much less every kind of work. The small, selected participant group and limited time available to learn the tools also constrain what can be inferred.
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METR’s later experiment was difficult to interpret
In a February 2026 update, METR said a later experiment, begun in August 2025, had become difficult to interpret because of selection effects. Some developers increasingly declined tasks that required working without AI, and some omitted tasks they did not want to attempt in the AI-disallowed condition. The later sample included 57 developers, 143 repositories and more than 800 tasks. Although the raw estimates suggested a possible speedup, METR said the observed data were an unreliable proxy for the true effect: selection likely biased the estimate downward, and the size of any actual productivity increase remained uncertain.
That update does not replace the early-2025 result with a reliable current estimate. It means the 19% slowdown should not be presented as a finding about today’s coding tools in general. The two experiments differ in timing and the later one did not yield a dependable estimate.
Why other studies report gains
Worker-reported time savings are estimates, not output measurements
The St. Louis Fed analyzed U.S. survey responses collected in August and November 2024. In the November survey, people who had used generative AI in the previous week estimated how many extra hours they would have needed to complete the same amount of work without it. The analysis calculated average reported savings of 5.4% of work time among AI users and 1.4% across all workers, including nonusers.
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These are self-reported counterfactual estimates: what respondents believe their work would have taken without AI. They are not a direct observation that workers produced 5.4% more output. A person may finish a task sooner and spend the time saved on other work, or may not recognize every time saving. Either way, reported hours saved and measured output growth are distinct outcomes.
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A 2024 Bank for International Settlements working paper examined a field experiment following Ant Group’s September 2023 launch of its CodeFuse coding assistant. The LLM treatment group produced 55% more lines of code than the control group. Statistically significant gains were primarily among junior staff. The authors attributed the smaller effect for senior programmers to lower engagement, rather than concluding that the tool could not help them.
Lines of code are a different measure from time to complete a task. This company experiment also involved a different workforce, tool and setting from METR’s study of experienced open-source developers. The results therefore do not cancel each other out or imply that one must apply to all coding work.
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A later survey captures perceptions, not causal gains
In a May 2026 report, METR surveyed 349 technical workers during February–April 2026. Respondents reported a median speed change of 3x and median value changes between 1.4x and 2x. METR asked about value as well as speed because completing work faster is not necessarily the same as producing work of greater value.
This was a convenience-sample survey, not a randomized experiment or an objective output measurement. METR cautioned that respondents’ counterfactual estimates could be inaccurate and that the reported magnitudes might be overstated. Treat the figures as what those respondents said, not as a measured or causal productivity gain for technical workers broadly.
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Why task-level gains may not appear in productivity statistics
A task can get faster without a company or an economy producing more per hour. Saved time may be spent checking AI output, handling additional work, or on activities that do not appear in conventional output measures. At a larger scale, gains also depend on how widely useful tools are adopted and whether organizations change processes, skills and complementary investment to make effective use of them.
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The International Labour Organization’s May 6, 2026 brief described task-level productivity gains as typically in the 10–70% range. That range is not a promise for each task or workplace. The ILO said firm-level evidence was mixed and adoption uneven, and that clear AI-driven productivity growth had not yet appeared in official sectoral or macroeconomic statistics.
The ILO’s June 2026 evidence review drew on experiments, firm-level data, platform studies, and worker and firm surveys in Australia, Denmark, Germany, Korea, Kuwait, the United Kingdom and the United States. It concluded that gains were real but uneven and often unverified. It also found that worker-reported time savings of a few percent of working hours had not yet translated into higher measured output, earnings or employment. This does not prove that AI has no economic benefit; it distinguishes local or reported task gains from gains confirmed in broader measures.
How to judge whether AI is slowing your work
For a particular role or workflow, the useful question is not whether AI is “fast” in the abstract. It is whether using it improves the result you care about after accounting for the full task. Compare similar work with and without AI, and track both time and the quality or value of the completed output.
- Match the task: compare like-for-like work, such as similar bug fixes or document tasks, rather than one easy AI-assisted task against a harder unaided one.
- Include the whole workflow: count prompting, waiting, reviewing, correcting and integrating the AI output—not only the time spent generating a draft or code.
- Check quality: faster output is not a productivity gain if it needs enough repair to erase the time saved or lowers the result’s usefulness.
- Separate individual time from team output: a worker’s saved minutes do not automatically mean a team ships more valuable work or that the organization produces more per hour.
- Reassess as tools and habits change: findings from early-2025 tools or a particular workflow may not describe later tools, different tasks or a team that has learned to use them.
On the available evidence, AI can slow a specific task, save time in another, or increase output without changing elapsed time in the same way. The outcome depends on the worker’s experience, task and quality requirements, tool and workflow, and the measure being used.
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