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AI coding tools can help developers produce a first draft faster, but that does not guarantee a change will be reviewed, accepted, and delivered sooner. Evidence finds gains on some bounded coding tasks and slowdowns in some mature codebases; the difference often lies in how much checking and rework the task demands.
What does “faster” mean in software development?
There are several clocks in a coding task: time to produce an initial draft, time to get a change through review, and time to deliver a reliable change that holds up in production. An assistant may shorten the first while leaving the others unchanged—or increasing the work they require.
That distinction explains why developers can feel faster while a team sees little improvement in delivery. Generated code is a proposal, not an accepted change. It still has to meet the project’s requirements, conventions, tests, and quality bar.
Why do studies reach different conclusions about AI coding speed?
A bounded coding exercise: GitHub’s Copilot study
GitHub’s vendor-published study randomly assigned developers with at least five years of experience to use Copilot or work without AI on API endpoints for a fictional web server. The first phase received valid submissions from 202 developers. GitHub reported that 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 results describe a specific exercise and measured outcomes, not a general speed advantage across software work. GitHub’s study was published in 2024 and updated in 2025.
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Real tasks in established repositories: METR’s trial
METR’s randomized trial examined experienced open-source developers completing realistic tasks in their own mature repositories with early-2025 AI tools. In that setting, participants took longer with AI. They had expected to be faster and afterward believed they had been faster, despite the measured slowdown. These tasks involved existing codebases and human review expectations, not simply producing a plausible first draft. METR’s report describes one setting; it does not predict results for novices, greenfield prototypes, every programming task, or later tool versions.
The findings are not contradictory so much as task-dependent. A bounded exercise can reward quickly producing a working solution; a change in a mature project may also need to fit unfamiliar conventions, preserve behavior, and satisfy reviewers. Time spent understanding and validating a suggestion can outweigh time saved generating it.
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Why can AI-generated code add supervision work?
Reviewers remain responsible for deciding whether a change is correct and appropriate. Depending on the task, checking may include edge cases, security, tests, maintainability, documentation, and whether the code solves the actual problem. If an assistant produces more code than a developer can readily explain, the review burden can rise even when the draft arrived quickly.
DORA cautions that “faster code reviews and approvals do not equate to better and more thorough code review processes and approval processes.” Faster movement through a review queue is not proof that the review was adequate. Review depth, rework, test coverage, and responsibility for final acceptance still matter. DORA’s 2025.2 report discusses these trade-offs.
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One possible team-level mechanism appears in an observational study of open-source activity after Copilot’s introduction. Xu and colleagues reported that experienced core contributors reviewed 6.5% more code and had a 19% drop in original code productivity. This is a context-specific analysis, not a randomized test or a demonstrated effect across commercial teams. It suggests how increased contributions from less-experienced contributors could shift work toward core maintainers, but it does not establish that every AI tool creates that pattern. The study was published in 2025.
Can individual productivity rise while team delivery falls?
Yes. A developer may complete a local task sooner while the team spends more time reviewing, fixing, integrating, or stabilizing the change. DORA’s 2025 survey and qualitative work, conducted with nearly 5,000 technology professionals and more than 100 hours of qualitative data, offers context about adoption and perceived effects—not a controlled trial. Google’s summary reports 90% AI adoption, a median of two hours per workday using AI, more than 80% reporting productivity enhancement, and 59% reporting a positive influence on code quality. These are survey responses and perceptions, not measured improvements for every respondent. It also reports that 24% had “a great deal” or “a lot” of trust in AI, while 30% had “a little” or “no” trust. Google’s summary of DORA’s 2025 findings sets out those figures.
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DORA’s modeled estimates show why adoption and delivery should not be treated as the same outcome. For a 25% increase in AI adoption, its 2025.2 analysis estimates 2.1% higher individual productivity, 3.1% faster code review, and 7.5% better documentation quality, alongside 1.5% lower delivery throughput and 7.2% lower delivery stability. DORA reports an 89% uncertainty interval for these estimates. They are modeled associations, not guaranteed causal effects for a particular team. The report presents the estimates.
DORA describes AI as an amplifier: its effects depend partly on the surrounding workflow. Faster drafting can magnify the value of clear requirements, small batches, robust tests, and shared review practices; it can also make weak handoffs or shallow review more consequential. DORA’s 2024 report likewise discusses AI adoption alongside delivery throughput and stability, and recommends clear AI guidelines, hands-on evaluation, small batch sizes, and robust testing. DORA’s 2024 report provides that workflow context.
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How should a team tell whether AI is helping?
Measure the route from work started to safely delivered—not just how quickly code appears. Compare similar tasks, and track a mix of time and quality measures so a faster draft does not mask added rework or risk.
- Task completion: time from starting a task to a change accepted for delivery.
- Review effort: waiting time and active handling time, kept distinct where possible.
- Rework: revisions requested, follow-up fixes, and reversions after review or release.
- Quality and stability: test outcomes, defects, and delivery stability.
- Team delivery: throughput alongside individual time saved, rather than as a substitute for it.
- Developer experience: perceived productivity and time spent on valuable versus toilsome work.
Compare results across task types—such as a new prototype versus maintenance in an established codebase—and account for experience, available project context, privacy and governance requirements, and the team’s quality bar. The evidence does not establish a head-to-head ranking of tools. A useful local evaluation asks whether a particular workflow reduces total cycle time without reducing correctness, review depth, or stability.
What does the wider evidence establish?
A 2026 version of a systematic review mapped 39 peer-reviewed studies published from January 2014 through December 2024. It describes common reported gains, including faster development and automation of repetitive tasks, alongside concerns about cognitive offloading and collaboration. Its findings on code quality are contradictory, and longitudinal and team-level evidence remains limited. That makes broad claims that AI either reliably speeds up developers or inevitably slows teams premature. The review captures the mixed state of the literature.
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