Sometimes—but there is no reliable, universal productivity gain. Studies have found faster completion on a bounded coding exercise, reported time savings in a government workplace trial, and slower completion in a trial with experienced developers working in familiar, mature projects. The result depends on the developer, task, tool, and what counts as productivity.
What the studies found
| Study | Setting and method | Reported finding | What the finding can tell you |
|---|---|---|---|
| METR, July 2025 | A randomized controlled trial with 16 experienced developers who had moderate AI experience. They completed 246 tasks in mature open-source projects where they had an average of five years of prior experience. The tested tools were available at the February–June 2025 frontier. | Participants took 19% longer on average with AI tools in this setting. Their expectations and impressions were more favorable than the measured completion-time result. | This is evidence against assuming that AI speeds up every kind of coding. It applies to this sample, these tasks, and early-2025 tools—not automatically to novices, greenfield work, other tools, or later tool generations. |
| UK public-sector trial, November 2024–February 2025 | The Department for Science, Innovation and Technology and Government Digital Service made 2,500 licences available across central government organisations. The report drew on surveys, telemetry, satisfaction and exit-survey data. | Participants reported saving an average of 56 minutes per working day, including 24 minutes on code creation and analysis. The 2,500 figure is licences offered, not a count of daily users. | These are useful workplace observations, but the time-saved figure is self-reported. It is not a randomized estimate of how much faster users completed work. |
| GitHub, July 2022 | GitHub’s vendor-published controlled study compared Copilot use on a defined programming task. | Average completion time was 1 hour 11 minutes with Copilot and 2 hours 41 minutes without it. | This supports the possibility of faster completion on a bounded task under study conditions. Its age, vendor origin, and narrow task do not establish the same result for complex production work or current tools. |
| Microsoft Research, June 2025 | Three randomized field experiments studied coding assistants at Microsoft, Accenture, and an anonymous Fortune 100 company. | The paper reports workplace experiments across those settings; a single general percentage is not established here. | Field experiments can show how tools fare in company settings, but results should be read by experiment and outcome rather than collapsed into one headline figure. |
Why the results differ
“Productivity” can mean finishing a task sooner, spending less time typing, feeling faster, accepting more suggestions, or delivering code that works with less follow-up. Those are different outcomes. A tool might reduce time spent creating code yet add time for prompting, waiting, checking suggestions, revising them, or integrating the result. A perceived time saving is also not the same as a measured reduction in end-to-end completion time.
The studies above differ in their participants, tasks, tools, and methods. A short, well-defined exercise is not interchangeable with maintaining a mature codebase; a developer’s estimate of time saved is not equivalent to a randomized comparison. Their numbers should not be averaged into a single productivity percentage.
What to measure on your own team
For the practical question—whether an assistant helps your developers—evaluate the work your team actually does. A useful comparison should include the full path from starting a task to having an acceptable result, not just the minutes spent generating code.
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- Choose representative work. Include the task types where you expect the assistant to be used, such as maintenance, debugging, or new feature work. Keep the task definition and acceptance criteria clear enough to compare outcomes.
- Record the starting conditions. Note the developer’s experience, familiarity with the codebase, assistant and model generation, configuration, and evaluation dates. These details help explain why results may differ across tasks or over time.
- Measure end-to-end time. Count time spent prompting, waiting, reviewing, revising, testing, and fixing follow-up issues—not only time spent writing code. Define in advance what qualifies as an accepted completion.
- Track quality alongside speed. Record whether the result meets the acceptance criteria and what review or correction it needs. A faster draft is not a productivity gain if it shifts substantial work downstream.
- Separate measured results from impressions. Collect developer feedback, but label it as perceived benefit. Compare it with task completion and quality measures rather than treating the two as interchangeable.
- Report results by task and developer context. Show where the assistant helped, had little effect, or added overhead. A team-wide average can conceal meaningful differences between kinds of work.
How to interpret newer tools and evidence
METR’s February 24, 2026 update said wider adoption created selection effects in its second developer-productivity study: people choosing to use AI may differ from those who do not. It also said participants found it difficult to account for time spent on tasks while agentic systems ran in the background, and that METR was changing the experiment design. The update did not report a completed replacement estimate for the 2025 trial.
That update is a reason to be cautious about treating any one older result as a forecast for every assistant available now. It does not erase the 2025 finding, nor does it establish a new universal speedup or slowdown. For a decision about current tools, the most relevant evidence is a careful evaluation of the tool, tasks, developers, and end-to-end outcomes in your own setting.
Quick Recap
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