Generative AI can accelerate software development, but the evidence does not support a single productivity gain that applies to every developer or team. Controlled experiments and company field trials have found faster task completion or more completed work; a randomized trial with experienced developers on familiar, mature projects found the opposite. The results differ because the tasks, developers, tools, and measurements differ—and self-reported speed is not the same as measured output.
What the studies actually found
These results are best read as evidence about particular settings, not as competing estimates of one universal effect. A timed coding exercise, a company field experiment, and work on a codebase a developer already knows answer different questions.
| Study and setting | What was measured | Result | What it does—and does not—show |
|---|---|---|---|
| Microsoft Research, 2023; recruited developers implementing a JavaScript HTTP server with GitHub Copilot access | Time to complete one bounded coding task | Participants with Copilot completed the task 55.8% faster than the control group. | A controlled task can show a substantial speed gain. It does not establish that routine development across a team or organization will be 55.8% faster. |
| GitHub, 2022; the same timed HTTP-server experiment, described in GitHub’s write-up | Task completion rate and average completion time | Completion rates were 78% with Copilot and 70% without. Average time was 1 hour 11 minutes with Copilot and 2 hours 41 minutes without. | These are company-published results from a specific task and experiment, not an estimate for all software work. |
| Microsoft Research, June 2025; three randomized field experiments at Microsoft, Accenture, and an anonymous Fortune 100 company | Completed tasks among 4,867 developers across the experiments | The authors report 26.08% more completed tasks for developers with access to an AI code-completion assistant (standard error 10.3%). | This is a combined result from three company trials. Each experiment was noisy; the authors report higher adoption and larger gains among less experienced developers. It is not a guaranteed effect for a different company or tool. |
| METR, 2025; 16 experienced open-source developers, 246 tasks, and mature projects developers had worked on for an average of five years | Measured completion time with early-2025 frontier AI tools available versus without them | Completion time increased by 19% with AI assistance in this trial. | This result applies to the experienced developers, familiar projects, tasks, and tools studied. The authors said experimental artifacts could not be entirely ruled out, while arguing the slowdown was robust across their analyses. |
The Microsoft and GitHub figures describe the same kind of bounded HTTP-server exercise, not a second independent confirmation of an organization-wide effect. The 2025 company field trials measured completed tasks in work settings; METR measured time on selected tasks in familiar open-source repositories. Their percentages should not be averaged into a forecast for your team.
Why AI can speed up one task and slow down another
Task shape matters
A short, well-specified implementation task can be a good fit for code completion. A task in a large, mature project may demand repository exploration, interpretation of local conventions, test runs, careful review, and revisions. AI can help with some of those steps, but generated code can also add work if it is incorrect or poorly matched to the codebase. The METR trial’s familiar-project setting is particularly important: it measured work by developers who knew those repositories, not first-time work in an unfamiliar codebase.
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Experience and familiarity matter
Microsoft Research’s 2025 field-trial summary reports higher adoption and larger productivity gains among less experienced developers. METR’s 2025 trial, by contrast, involved experienced open-source developers working on projects they knew well. That difference is a reason to avoid treating either finding as the expected effect for every seniority level or project. A tool may help a newer developer get oriented or produce a first draft, while an expert working in a familiar system may spend time checking suggestions that do not fit the project.
Tools and timing matter
The METR result reflects the frontier tools available in early 2025; participants primarily used Cursor Pro and Claude 3.5 or 3.7 Sonnet when AI was allowed. The GitHub and Microsoft results concern GitHub Copilot in earlier experiments. These are snapshots of different tools and periods, not a controlled comparison of products against one another. Adoption also changes who uses AI and how: in February 2026, METR said it was changing its developer-productivity experiment design because wider adoption had created selection effects.
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Measured productivity is not perceived productivity
In METR’s 2025 trial, developers forecast that AI would reduce completion time by 24%; after the trial, they estimated a 20% reduction. Yet measured completion time increased by 19%. The gap illustrates why confidence or satisfaction can be meaningful to developers without serving as a substitute for a measured time or output result.
A separate METR survey conducted from February through April 2026 included 349 technical workers, 87 of them software engineers. Respondents reported a median value uplift between 1.4x and 2x and a median self-reported speed change of 3x. Those figures are counterfactual self-reports from a convenience sample, not causal experimental estimates. METR explicitly gives reasons to be skeptical of their size. The reported value of work and raw speed are also different outcomes.
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Productivity includes more than speed
Time to finish a task is useful, but it does not capture all the ways developers experience work or all the outcomes a team may care about. GitHub’s 2022 write-up describes the SPACE framework, which considers satisfaction and well-being, performance, activity, communication and collaboration, and efficiency and flow.
Among respondents signed up for Copilot’s technical preview, GitHub reported that 60–75% said they felt more fulfilled, less frustrated, or able to focus on more satisfying work. In that same selected user group, 73% reported help staying in flow and 87% said Copilot preserved mental effort on repetitive tasks. These are survey responses, not measured causal effects across developers generally. They can describe perceived experience, but should not be presented as proof of a corresponding increase in output.
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How a development team can evaluate AI use
The studies point to a practical principle: evaluate representative work from your own environment, and keep different outcomes separate. A useful internal comparison should account for:
- Task type: Include the kinds of work your team actually does, not only small implementation exercises.
- Codebase familiarity: Distinguish work in a known repository from onboarding or work in an unfamiliar system.
- Developer experience: Record experience level rather than assuming a result for one group applies to another.
- Tool and period: Note the assistant and model version used; findings from earlier tools may not describe a later setup.
- Time and output: Track completion time and completed work separately, and define what counts as complete.
- Quality and review: Include defects, test results, rework, and review effort so a fast first draft is not mistaken for a finished result.
- Human experience: Ask about frustration, focus, or satisfaction separately from measured delivery outcomes.
Where practical, compare similar tasks with and without AI assistance and use the same completion and quality criteria. Record whether the developer accepted, edited, or discarded generated suggestions. This will not eliminate every source of bias, but it makes the result more useful than a single self-reported productivity percentage.
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Using AI agents for browser-facing development work
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