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AI can help developers complete more tasks in some workplaces and slow experienced developers in other settings. Those results are not necessarily contradictory: they measure different people, work, tools, and outcomes. More generated code—or faster typing—does not by itself show that a team is delivering more useful, maintainable software.
Why more code is not the same as more productivity
Software work produces more than source files. A change has to solve the right problem, fit the existing system, pass checks, survive review, and remain maintainable. Code volume captures activity, but not whether the change is correct, accepted, or worth its cost to build and support.
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A coding assistant might generate a first draft quickly while leaving a developer to verify assumptions, adapt the code to local conventions, fix defects, or resolve integration problems. Those are plausible ways that time saved during generation could be offset elsewhere; the study results below do not establish one universal explanation for either gains or slowdowns.
Productivity also includes dimensions that are not reducible to output counts. GitHub’s Copilot research uses the SPACE framework, which considers satisfaction and well-being, performance, activity, communication and collaboration, and efficiency and flow. The research examines a subset of these dimensions, underscoring why a single activity measure cannot stand in for the whole picture.
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What the studies measured—and why their results differ
The findings are best read as evidence about particular work settings, not as a vote on whether AI always speeds developers up or slows them down.
| Study | Participants and work | Reported result | What the result does—and does not—show |
|---|---|---|---|
| Microsoft Research, June 2025 | Three randomized field experiments at Microsoft, Accenture, and an anonymous Fortune 100 company; 4,867 developers combined. | 26.08% increase in completed tasks, with a reported standard error of 10.3%. | Evidence of higher task output across those workplace experiments. It is not a universal estimate for every developer, project, or definition of completed work. The summary also reports higher adoption and larger productivity gains among less experienced developers. |
| METR, 2025 | Randomized trial involving 16 experienced open-source developers and 246 issues in projects they knew; participants could use early-2025 AI tools. | Issues took 19% longer to complete. | A bounded slowdown in this setting, not evidence that most developers are slower with AI. METR says its result does not establish that AI fails in other domains or that future tools will not speed work in the same setting. |
| GitHub, 2022; updated 2024 | Controlled exercise in which 95 professional developers wrote a JavaScript HTTP server using Copilot. | GitHub reports 55% faster task completion. | A task-specific result for that exercise and its Copilot context, not a general estimate for work in mature repositories or for software delivery as a whole. |
These figures should not be compared as if they came from one shared experiment. The Microsoft result counts completed tasks across three companies; METR timed issue completion in familiar open-source projects against expectations for reviewable work; GitHub tested one controlled programming exercise. The participants, task definitions, tools, periods, and outcome measures all differ.
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Why realistic repository work can expose hidden costs
METR’s trial focused on issues in established codebases, where the work included meeting a human reviewer’s expectations for style, tests, and documentation—not merely producing a snippet that passes a narrow test. In that environment, context and integration matter alongside code generation. A plausible implementation may still require additional work before it is ready to merge.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesThat distinction helps explain why benchmark performance and day-to-day delivery can diverge. A benchmark may reward a correct answer against explicit test cases, while an issue in a mature project can depend on implicit requirements and local conventions. The study does not establish which possible cost—review, rework, context loading, ambiguity, or integration—caused its slowdown, so those should be treated as hypotheses to test, not as proven mechanisms.
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Tool and task also matter. A completion assistant used for a small, well-defined exercise is not the same intervention as chat or agent-style assistance on a multi-step change. Nor does a result from early-2025 tools establish the effect of later versions.
Why developers may feel faster even when work takes longer
Perception and elapsed time can diverge. In METR’s trial, participants expected a 24% speedup and, after finishing, still believed they had been sped up by 20%, despite the measured 19% longer completion time. That gap is a warning against treating developer confidence or satisfaction as a substitute for outcome measurement—but it is not a reason to ignore those experiences.
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GitHub’s controlled exercise and qualitative research provide a different, compatible kind of evidence. The company reports faster completion on its JavaScript HTTP-server task. It also quotes an anonymized senior software engineer describing Copilot as making coding “more fun and more efficient.” That is useful testimony about an individual’s experience, not a measured productivity result or proof that every task becomes faster.
Feeling less friction can be valuable even when total elapsed time does not improve. Teams should therefore keep experience and delivery as separate measures rather than collapsing them into one productivity score.
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How organizational conditions shape the result
DORA’s 2025 report, published by Google Research, draws on more than 100 hours of qualitative research and responses from nearly 5,000 technology professionals worldwide. Its central framing is that “AI’s primary role in software development is that of an amplifier. It magnifies the strengths of high-performing organizations and the dysfunctions of struggling ones.”
In practical terms, an organization with clear requirements, maintainable systems, useful tests, effective review, and manageable delivery processes has a different starting point from one burdened by unclear ownership or fragile code. AI’s effect should be evaluated within that environment, not assumed from the tool’s ability to generate code.
How to measure AI productivity in your own team
No single metric is a universal standard. The following is a practical evaluation approach derived from the differences in the studies, not a published measurement formula.
- Define the outcome before the trial. Decide what counts as a completed task: for example, a change accepted after review and meeting the team’s quality checks. Keep that definition stable for the comparison.
- Measure the whole delivery path. Track elapsed time through review and acceptance, along with rework, defects, and other relevant quality checks. Code lines or generated suggestions can describe activity, but should not be the success measure on their own.
- Compare against a credible baseline. Use similar work done without the assistant, or a suitable prior period, and record differences in task scope, team, and workflow. A comparison is hard to interpret if the work changed at the same time as the tool.
- Segment the results. Look separately at task types, repository maturity, developer experience, and the kind of assistance used. An overall average can hide a benefit in one category and a cost in another.
- Allow for variation and learning. Evaluate enough comparable work to avoid drawing a conclusion from one unusually easy or difficult task. Note whether people are still learning the tool and review results again as habits or tools change.
- Report more than one dimension. Pair delivery measures with developer experience and collaboration observations. A faster task that creates defects or burdens reviewers is not an uncomplicated gain; a tool that improves the work experience may still matter even if elapsed time stays flat.
Use the results to decide where assistance belongs in the workflow. If it helps on well-scoped tasks but adds friction in a complex repository, the useful conclusion is about that task-and-context fit—not that AI either works or fails everywhere.
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