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Both—but not universally. Generative AI can help developers finish some coding tasks faster and may increase output in some workplaces. Yet a 2025 trial found experienced developers took longer with AI on tasks in codebases they knew well. These findings do not cancel each other out: they measured different work, people, tools, and outcomes. The useful question is not whether AI makes developers more productive in general, but when it helps—and whether the gain survives review and quality checks.
What the studies actually found
The results below are snapshots of particular experiments and surveys, not a single estimate of AI’s effect on software development. A timed programming exercise, tasks completed during company field trials, and a survey asking developers to estimate their own gains are not interchangeable measures.
| Study | Setting and participants | Reported result | What it can tell you |
|---|---|---|---|
| GitHub Copilot timed task, reported by Microsoft Research in 2023 and GitHub in 2022 | Recruited developers implemented a JavaScript HTTP server in a controlled task. | Microsoft Research reported the Copilot group completed the task 55.8% faster. GitHub reported completion rates of 78% with Copilot and 70% without, and average times of 1 hour 11 minutes versus 2 hours 41 minutes. | AI assistance can produce a substantial speed gain on a bounded task. This does not establish the effect on routine work across a team or organization. |
| Three company field experiments, Microsoft Research summary, June 2025 | Randomized experiments at Microsoft, Accenture, and an anonymous Fortune 100 company; 4,867 developers combined. | The authors reported 26.08% more completed tasks for developers with access to an AI code-completion assistant (standard error 10.3%). | Access to an assistant coincided with more completed tasks in these field settings. Each experiment was noisy; the result is not a guaranteed gain for another company, assistant, or definition of a task. |
| METR randomized trial, 2025 | Sixteen experienced open-source developers completed 246 tasks in mature projects where they averaged five years of prior experience. The early-2025 tools included Cursor Pro and Claude 3.5/3.7 Sonnet. | Measured completion time increased by 19% when developers used AI. Before the trial, participants forecast a 24% reduction; afterward, they estimated a 20% reduction. | In this setting, measured time and developers’ expectations diverged. The authors said experimental artifacts could not be entirely ruled out, while arguing the slowdown was robust across their analyses. This is not a result for every developer or task. |
| METR survey, February–April 2026 | Convenience sample of 349 technical workers, including 87 software engineers. | Respondents reported median value uplift between 1.4x and 2x and median self-reported speed change of 3x. | These are retrospective, counterfactual self-reports—not causal estimates from a controlled productivity experiment. METR gave reasons to be skeptical of their size; perceived value and raw speed are different outcomes. |
Why results differ
The work may be bounded or embedded in a familiar codebase
A self-contained programming exercise gives a model a clear prompt and a defined finish line. Work in a mature repository can require understanding conventions, tracing dependencies, preserving behavior, and checking interactions with existing code. The Copilot task and METR trial therefore answer different questions; their results should not be averaged into one expected percentage.
Developer experience and codebase familiarity matter
The METR participants were experienced maintainers working on projects they already knew. Microsoft Research’s account of the company experiments reported higher adoption and larger productivity gains among less experienced developers. That is a difference between these study populations, not proof that every junior developer benefits more or every senior developer benefits less.
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Tools and timing matter
The METR trial used early-2025 frontier tools, while the company field experiments tested access to AI code-completion assistants in their own workplaces. Results describe the tools and conditions tested then. In February 2026, METR said it was changing its experiment design because wider AI adoption was creating selection effects: people who choose to use AI may differ from people who do not. A finding should be read with its date and population attached.
“Productivity” can mean more than speed
GitHub’s 2022 write-up used the SPACE framework: satisfaction and well-being, performance, activity, communication and collaboration, and efficiency and flow. Among respondents signed up for Copilot’s technical preview, 60–75% said they felt more fulfilled, less frustrated, or able to focus on more satisfying work; 73% reported help staying in flow, and 87% said Copilot preserved mental effort on repetitive tasks. These are survey responses from a selected user group, not measured causal effects across developers generally. A faster first draft does not by itself show improved quality, delivery, or team performance.
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How to evaluate AI’s effect on your team
Do not choose a tool or set expectations from a single headline percentage. Treat the studies as reasons to run a local evaluation on representative work and track the costs as well as the apparent speed gain.
- Pick representative tasks. Include the kinds of work your team actually does, such as bounded changes and changes that require navigating a familiar repository. Record task type and how well the developer knows the codebase.
- Define the outcome before comparing. Decide whether you are measuring elapsed time, tasks completed, or another outcome. Keep those measures separate; do not describe self-reported value as measured speed.
- Count work through acceptance, not just first draft. Include review, corrections, testing, and any rework in the time comparison. Also check whether the change meets the team’s normal quality requirements.
- Compare like with like. Record who used the assistant, which tool and model were available, the task context, and the evaluation period. A comparison can mislead if AI users, assignments, or levels of codebase familiarity differ systematically.
- Include outcomes beyond throughput. Ask whether the tool changes developers’ focus, frustration, or ability to spend time on satisfying work, while keeping those perceptions distinct from measured delivery results.
- Revisit the result as adoption changes. Keep dates and tool versions with your notes. A result from one group or tool period may not carry over as tools and usage patterns change.
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