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AI can help developers produce a first draft faster, but that does not guarantee a change reaches production sooner. The answer depends on the task and on what counts as “faster”: generating code, getting it accepted, or delivering a reliable change. Studies have found both gains and slowdowns, while evidence also suggests that review and maintenance can absorb some of the time saved.
What does “faster” mean in software development?
There are at least three different clocks to consider:
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- Time to first draft: how quickly code or a proposed solution appears.
- Time to an accepted change: how long it takes to test, revise, document, and pass review.
- Time to reliable delivery: how long until the change is safely released and remains stable.
An AI assistant may shorten the first clock while leaving the others unchanged—or lengthening them if its output needs substantial checking or rework. A useful productivity claim must specify which outcome it measures.
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Why controlled studies reach different conclusions
A bounded API task showed measured gains
GitHub’s vendor-published Copilot study randomly assigned experienced developers to work with or without Copilot on API endpoints for a fictional web server. In the study’s first phase, 202 developers with at least five years of experience submitted valid results. The Copilot group was 53.2% more likely to pass all 10 unit tests, produced 13.6% more lines per readability error, and had a 5% higher likelihood of approval. These are findings from that specific exercise, not a general estimate of how much faster developers will be on other work. GitHub’s study was published in 2024 and updated in 2025.
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Work in mature repositories showed a slowdown
METR ran a randomized trial with experienced open-source developers doing realistic tasks in their own established repositories, using AI tools available in early 2025. Participants took longer with AI in this setting, despite expecting to be faster and later believing they had been faster. The tasks involved fitting changes into existing projects and meeting human reviewers’ expectations for matters such as style, tests, and documentation. That differs substantially from writing endpoints for a fictional server. METR’s report is important evidence about this kind of maintenance work, but it does not establish that AI slows novices, greenfield prototypes, or every programming task.
The studies should not be averaged into a single speed figure: they tested different work and outcomes. Tool capabilities also change, so METR’s early-2025 result is not a forecast for every later tool version.
Why faster code can mean more supervision
Generated code is a proposal, not an accepted change. Someone still has to establish that it solves the actual problem and fits the project. Depending on the change, that means checking correctness, edge cases, tests, security, maintainability, and documentation. If code arrives more quickly, review can become the next constraint rather than disappear.
DORA’s 2025.2 report cautions: “Of course, faster code reviews and approvals do not equate to better and more thorough code review processes and approval processes.” A quicker approval is not proof that review was careful enough; test coverage, rework, and accountability for acceptance still matter. DORA’s report discusses these workflow relationships.
Review load may shift to experienced contributors
An observational study of open-source activity after Copilot’s introduction found that experienced core contributors reviewed 6.5% more code and had a 19% drop in original code productivity. The authors’ findings describe the projects and activity they analyzed; they are not a randomized estimate or proof that all commercial teams will see the same effect. One plausible team-level pattern is that more contributions from peripheral contributors create additional review and rework for experienced maintainers. Xu and colleagues’ 2025 study examines this open-source context.
Individual productivity and delivery can move in opposite directions
DORA’s 2025.2 analysis estimates that a 25% increase in AI adoption is associated with a 2.1% increase in individual productivity and a 3.1% increase in code-review speed, alongside a 1.5% reduction in delivery throughput and a 7.2% reduction in delivery stability. It also estimates a 7.5% increase in documentation quality. DORA reports these as model estimates with an 89% uncertainty interval; they are not guaranteed causal effects for any particular organization.
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That distinction matters because an individual can feel more productive while the team’s delivery system becomes less predictable. DORA’s 2025 survey context is different evidence: Google’s summary reports nearly 5,000 technology professionals surveyed and more than 100 hours of qualitative data. It says 90% used AI, median use was two hours per workday, more than 80% reported productivity enhancement, and 59% reported a positive influence on code quality. Those are adoption and perception findings, not measurements proving that each person’s shipped output improved. The same summary reports that 24% had a great deal or a lot of trust in AI and 30% had a little or no trust. Google’s DORA summary presents the survey findings.
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Measure the full path from work started to safely delivered, rather than counting generated lines or celebrating a quick draft. Compare similar tasks with and without AI where practical, and pair speed measures with quality measures.
Best Value
- Track task completion time and accepted changes, not just time to first output.
- Separate review waiting time from the time reviewers spend understanding and handling a change.
- Record rework after review, defects, and reversions alongside review speed.
- Watch delivery throughput and stability as well as individual productivity.
- Ask developers whether time shifted from repetitive work to valuable work—or to checking and repairing output.
- Keep task type, developer experience, project context, and quality bar visible when interpreting results.
DORA’s 2024 report recommends clear AI guidelines, hands-on evaluation, small batch sizes, and robust testing. These practices help teams assess whether a tool fits their workflow instead of assuming that faster generation translates into faster delivery. DORA’s 2024 findings discuss these workflow foundations.
What the wider evidence can—and cannot—settle
A 2026 version of a systematic review mapped 39 peer-reviewed studies published from January 2014 through December 2024. It reports common gains such as faster development and automation of repetitive tasks, alongside concerns about cognitive offloading and collaboration. Findings on code quality remain contradictory, and longitudinal and team-level evidence is limited. This means there is no settled universal answer: benefits and supervision costs can depend on the work, the people, and the delivery process. The systematic review by Mohamed, Assi, and Guizani summarizes that evidence base.
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