Sometimes—but there is no reliable, universal speedup. A controlled Copilot exercise and three workplace experiments found gains on their measured outcomes, while a 2025 trial with experienced developers working on familiar, mature open-source projects found that AI made task completion slower. The results differ because the studies measured different work, people, tools, and outcomes; none establishes a fixed productivity boost for every developer or codebase.
What the studies measured—and what they found
The numbers below are not estimates of the same thing. Finishing one bounded task faster, completing more tasks over a work period, and reporting that work feels easier are distinct outcomes.
| Study and setting | Participants and work | Reported result | What the result measures |
|---|---|---|---|
| GitHub, 2022, controlled Copilot experiment | 95 professional developers were randomly assigned Copilot access or no access while building a JavaScript HTTP server. | The Copilot group averaged 1 hour 11 minutes, versus 2 hours 41 minutes for the comparison group. GitHub reported a 55% faster completion time (p=.0017; 95% confidence interval for the speed gain: 21%–89%). Completion rates were 78% and 70%, respectively. | Time and completion for one specified coding exercise—not the speed of all software engineering work. |
| Microsoft Research, 2025, pooled field experiments | Three randomized experiments at Microsoft, Accenture, and an anonymous Fortune 100 company; 4,867 developers in total. | The pooled estimate was a 26.08% increase in completed tasks (standard error 10.3%). The authors report noisy results in the individual experiments, with higher adoption and greater gains among less experienced developers. | Task throughput across workplace experiments—not a 26.08% reduction in the time needed for each task. |
| METR, 2025, trial in familiar open-source projects | 16 experienced developers worked on 246 real issues in large projects they knew well, averaging five years of prior contributor experience. Participants primarily used Cursor Pro with Claude 3.5 or 3.7 Sonnet; tools were available during February–June 2025. | Allowing AI increased measured completion time by 19%. Before the trial, developers forecast a 24% time reduction; after doing the tasks, they estimated a 20% reduction. | Elapsed time on real issues in this specific setting. The forecasts and retrospective estimates are perceptions, not measured speedups. |
| UK Government Digital Service, 2025, public-sector deployment | A three-month trial from November 2024 to February 2025 distributed 2,500 licenses across more than 50 public-sector organizations; 1,900 licenses were assigned. The main analysis used 424 survey responses from 31 departments; 73% of respondents had at least five years of coding experience. | The report combines survey and telemetry evidence. It does not provide a clean randomized causal estimate of productivity in the summary figures here. | Deployment experience and survey evidence, not a controlled estimate that licenses caused a particular speed increase. |
GitHub’s separate survey of more than 2,000 developers offers context about experience rather than timed performance: 60%–75% agreed with statements about greater fulfillment, less frustration, and more focus; 73% said Copilot helped them stay in flow, and 87% said it preserved mental effort during repetitive tasks. Those self-reports describe perceived benefits, not proof that respondents completed more work or finished faster.
Why the results do not agree
The studies differ on several dimensions that can change how useful an assistant is. The evidence does not isolate one of them as the cause of the opposite results, so they are best treated as reasons to be cautious about transferring a number from one setting to another.
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- Task type and realism: Building a specified HTTP server in a controlled exercise is different from resolving an issue inside a mature repository. Workplace task counts and deployment surveys answer still different questions.
- Codebase familiarity: METR recruited contributors working in projects they had contributed to for years. That is a useful test of real work for experienced maintainers, but it is not the same setting as a new or isolated task.
- Developer experience: Microsoft Research reported higher adoption and greater gains among less experienced developers in its field experiments. That finding does not mean every beginner benefits or every senior developer loses time.
- Tools and timing: METR’s result concerns early-2025 tools, primarily Cursor Pro with Claude 3.5 or 3.7 Sonnet. It should not be treated as an evaluation of every assistant or later system.
- Work setting and duration: A timed exercise, company field experiment, and three-month public-sector deployment expose developers to different workflows and constraints.
- Outcome and design: Random assignment can support causal comparisons within a study’s conditions, but the measured outcome still matters. Completion time, task throughput, satisfaction, and perceived flow are not interchangeable.
What the 2026 METR update changes
In a February 24, 2026 update, METR said a later experiment was not a reliable estimate of current productivity effects: more developers declined to participate if they had to work without AI, which likely biased the estimated speedup downward. METR reported a −18% speedup estimate for returning participants (95% confidence interval −38% to +9%) and −4% for newly recruited participants (95% confidence interval −15% to +9%). Both intervals include no effect. METR said the true speedup could be higher among developers and tasks selected out of the experiment.
That update is a warning against treating the later experiment as a definitive positive result. It also does not erase the 2025 trial: the two findings come from different experiments, and the later one’s participation pattern limits what it can establish.
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Perceived speed is not the same as measured speed
METR’s 2025 trial illustrates why asking whether AI “feels faster” is not enough. Participants expected a 24% time reduction before the study and, afterward, estimated a 20% reduction, even though measured completion time increased by 19% in that trial. These are separate measures: expectations and retrospective impressions may matter to a developer’s experience, but they cannot substitute for timed task results.
The Copilot survey likewise supports claims about reported flow, focus, frustration, and mental effort—not a general claim that developers finish work faster. A useful productivity assessment should name its outcome rather than treating satisfaction, code output, task counts, and elapsed time as synonyms.
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How to apply the evidence to your own work
Use the studies as context, not as a promised percentage for your team. A reasonable local evaluation should compare similar work with and without the assistant and track the outcome that matters to your workflow.
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- Choose representative tasks. Include the kinds of work your team actually does, such as bounded changes and issues in repositories developers already know. Record which tasks are included so the result is not mistaken for a universal one.
- Define “faster” before measuring. Decide whether you care about elapsed time to completion, accepted tasks per period, or another explicit measure. Keep satisfaction and perceived flow as separate outcomes.
- Compare like with like. Track task difficulty, developer experience, repository familiarity, assistant and model version, and whether the tool is integrated into normal work. These details help explain where a result may or may not transfer.
- Include the whole completion path. Measure the work needed to reach the team’s definition of done, including review and correction where relevant. The cited studies do not settle long-term maintenance, software quality, or every organizational outcome.
- Report uncertainty. A small trial, noisy task counts, or a survey can be informative without supporting a precise prediction for every developer. Share the sample, period, measure, and limits alongside any percentage.
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