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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →AI coding tools can make developers slower when the time they save drafting code is outweighed by the work of supplying context, checking output, correcting mistakes, and integrating changes. But they do not slow everyone down on every task: studies report different results because they tested different developers, tools, work, and definitions of productivity.
What the studies actually found
The strongest caution comes from a 2025 randomized trial by METR. Sixteen experienced open-source developers completed 246 tasks in mature repositories they already knew. With early-2025 AI tools available—mainly Cursor Pro and Claude 3.5 or 3.7 Sonnet—participants took 19% longer to finish tasks. The study’s result applies to that sample, tool generation, and maintenance-heavy setting; it does not establish that AI coding tools universally reduce productivity. METR’s study and results.
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The gap between expectation and measurement was striking. Before the trial, participants expected AI to reduce their task time by 24%. Afterward, they estimated it had reduced time by 20%, even though measured completion time had increased by 19%. A developer’s sense that a tool is helping is therefore not a substitute for timing the whole task.
Other experiments found gains in different settings:
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- Workplace field experiments: A 2025 Microsoft Research analysis combined three randomized experiments at Microsoft, Accenture, and an anonymous Fortune 100 company. Across 4,867 developers, the reported estimate was a 26.08% increase in completed tasks. Individual experiments were noisy, and gains were greater among less experienced developers. Microsoft Research’s study.
- A bounded coding exercise: GitHub reported that professional developers assigned to use Copilot built a JavaScript HTTP server in an average of 1 hour 11 minutes, compared with 2 hours 41 minutes for the control group—a 55% faster completion time. This was a specific exercise, not a trial of ongoing work in established repositories. GitHub is also the product vendor. GitHub’s productivity study.
These percentages should not be averaged or treated as direct contradictions. The studies differ in task type, developer and codebase familiarity, tool, study design, and outcome. A short exercise with a clear endpoint is not the same job as making a change in a large, familiar codebase that must satisfy review, style, test, and documentation expectations.
Why AI may add time to a coding task
METR’s trial does not establish a universal explanation for its slowdown result. Still, it highlights a practical distinction: in mature repositories, completing a change means more than producing code that looks plausible or passes a narrow test. Developers must understand local conventions and deliver work that can meet the project’s expectations.
When assessing your own workflow, inspect the entire path from request to accepted change. Plausible costs to track include:
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- Finding and providing enough codebase context for a useful response.
- Checking whether the proposed approach fits existing design and conventions.
- Correcting errors, edge cases, or assumptions in generated code.
- Reviewing the diff, running tests, and integrating the change.
- Maintaining enough understanding to explain and support the code later.
These are workflow hypotheses to test in your setting, not measured explanations for a particular share of METR’s result.
Productivity means more than typing faster
A tool can shorten time to a first draft while leaving total delivery time unchanged—or increase the number of small tasks completed while creating extra review work. Keep the outcome you care about explicit instead of treating “productivity” as one number.
- Task time: How long does a comparable task take from start through verification and integration?
- Throughput: How many tasks are completed in a defined period, and are they comparable in size and difficulty?
- Quality: Does the change meet correctness, maintainability, style, testing, and documentation standards?
- Developer experience: Does the tool reduce effort or improve flow, even if elapsed time is unchanged?
- End-to-end delivery: Does work reach users sooner, or does time shift into review and follow-up?
Quality evidence also needs its scope. In a separate 2024 randomized GitHub study, 202 experienced developers submitted code for a web-server API exercise. GitHub reported that the Copilot group was 53.2% more likely to pass all 10 unit tests, alongside small gains on several expert-rated quality dimensions. That is a result for the study’s exercise and test measure, not a claim about production defect rates or maintainability across real projects. GitHub’s code-quality study.
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How to improve the workflow without lowering the review bar
Treat AI assistance as a task-level choice, not a default. The following workflow recommendations are practical implications of the differences between study settings; the studies did not test this checklist as a package.
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- Choose the task before choosing the tool. Start with work where a draft, explanation, repetitive transformation, or unfamiliar API can be checked cheaply. Evaluate deeply contextual changes in a mature system rather than assuming assistance will save time.
- Give bounded context and a clear request. Identify relevant files, constraints, expected behavior, and tests. Ask for a small, reviewable change rather than inviting a broad rewrite by default.
- Keep verification inside the task. Inspect the diff, check assumptions against the codebase, run relevant tests, and meet the same review and documentation standard you expect for unaided work.
- Measure end-to-end work locally. Compare similar tasks with and without assistance. Include context-setting, correction, review, integration, and follow-up—not just typing time or code generation. Track quality and developer experience separately from elapsed time.
- Make the choice reversible. Use assistance where it helps; switch to direct work when context is expensive or the output is harder to verify than the change itself. Look at team-level effects as well as individual task speed.
Why team conditions matter
Individual tool use sits inside a wider delivery system: how work is specified, reviewed, tested, and integrated can shape whether faster drafting produces better outcomes. DORA’s 2025 report describes AI’s primary role as “an amplifier, magnifying an organization’s existing strengths and weaknesses.” Its implication is not that a particular assistant guarantees a productivity gain, but that the surrounding workflow matters. DORA’s 2025 report.
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