AI is changing software development across the whole workflow—not just by generating code. Developers use it to search, explore ideas, learn, and review as well as to write code. Whether it saves time depends on the task, the developer, the tools, and the organization. Current studies do not support a universal productivity figure or a settled conclusion about AI’s effect on developer jobs.
Where AI fits in the software development workflow
AI coding assistants can draft functions, tests, and explanations, but code generation is only one use. In a workplace study at a large multinational software company, developers also described using generative AI for web searches and creative ideation. A UK public-sector trial asked participants about time spent coding, reviewing, searching, learning, and solving problems. These findings point to AI acting as an assistant across varied tasks rather than as a replacement for the entire development process.
Common uses include:
- Writing and revising code: generating a starting point, suggesting changes, or explaining unfamiliar syntax.
- Searching and learning: helping developers find information or understand an unfamiliar concept.
- Ideation and problem-solving: offering possible approaches that a developer can evaluate.
- Review: surfacing potential issues or suggesting improvements, which still need human verification.
These uses can reduce friction, but none establishes that the resulting code is correct, secure, maintainable, or ready to ship.
Does AI make developers more productive?
There is no reliable single percentage for how much faster AI makes developers. Studies measure different things—from time to complete selected tasks to participants’ estimates of minutes saved at work—and involve different developers, projects, and tools. Those results should be read in context, not treated as competing measurements of the same effect.
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| Study | What it examined | Reported result | What to keep in mind |
|---|---|---|---|
| METR randomized trial, 2025 | 16 experienced open-source developers completed 246 tasks on mature repositories where they averaged five years of prior experience. The trial compared AI-allowed and AI-disallowed tasks; participants primarily used Cursor Pro and Claude 3.5/3.7 Sonnet, tools at the February–June 2025 frontier. | With AI allowed, participants took 19% longer on average. Before the trial they expected a 24% reduction in task time; afterward, they estimated a 20% reduction. | This measured task completion in a narrow setting: experienced developers working in codebases they knew well. The authors noted experimental artifacts could not be entirely ruled out, while reporting robustness across their analyses. |
| UK Government Digital Service trial, 2024–2025 | The trial distributed 2,500 licenses across more than 50 public-sector organizations and received 424 survey responses from users in 31 departments. Seventy-three percent of respondents reported at least five years of coding experience. | Participants estimated an average saving of 56 minutes per working day. | This is a survey estimate, not a controlled measure of time saved. The report warns that estimates may overlap and optimism bias may overstate savings; it also notes a missing month of telemetry. |
The numbers are not a head-to-head comparison: METR measured task time in a randomized trial, while the UK figure came from participants’ estimates of daily savings across work. The studies also differ in repository familiarity, tasks, and tool settings. METR’s result is a useful warning against assuming that assistance always speeds up experienced developers; the UK trial reflects perceived benefits in a different workplace context.
What newer evidence says—and does not say
In a February 2026 update, METR described a later experiment involving ten developers from the original study and 47 newly recruited developers. It reported selection effects: some developers declined because they did not want to work without AI, and some participants said they withheld tasks they did not want to do without it. Concurrent agent use also made some time reports unreliable. Although raw estimates suggested speedups for some groups, METR called the signal unreliable and the evidence for the size of any effect weak. It considered increased speedup likely, but its data did not establish how large that speedup was. METR’s update is therefore not a settled productivity figure.
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Why organizational conditions matter
AI’s value depends partly on whether the organization can turn suggestions into dependable software. DORA’s 2025 State of AI-assisted Software Development report drew on nearly 5,000 technology professionals and more than 100 hours of qualitative data. Its conclusion was that AI magnifies what is already happening in an organization: “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.” (DORA 2025 report)
In practice, an assistant can help a team with clear requirements, useful feedback, and effective review practices move through work more smoothly. It can also produce more suggestions for a team already struggling with unclear ownership or weak validation. Faster code production alone does not ensure faster delivery or better software.
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Code suggestions still need developer judgment
Acceptance metrics show that developers use some suggestions, not that the suggestions are correct or valuable after release. In the UK trial, GitHub Copilot telemetry showed an average acceptance rate of 15.8% for suggested code lines. Separately, 39% of users reported committing code suggested by the assistant. Those are different measures: accepting a line is not the same as committing code, and neither proves correctness, quality, or overall productivity.
Microsoft Research’s 2025 workplace study combined surveys, a randomized controlled trial, and a three-week diary study. Eighty-four percent of participants reported positive changes in daily work practices, and 66% reported shifts in how they felt about their work. Sustained use increased perceived usefulness and enjoyment, while trust in the quality of AI-generated code remained unchanged. These experience findings are not a universal measure of engineering output. Microsoft Research’s study illustrates why perceived usefulness, trust, and measured speed should not be treated as interchangeable.
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A practical review checklist
Before incorporating AI-generated or AI-modified code, developers should:
- Check that it meets the requirement and fits the surrounding codebase.
- Review behavior at edge cases, not just the example that prompted the suggestion.
- Run relevant tests and add tests for behavior that was not covered.
- Inspect dependencies, data handling, permissions, and security-sensitive logic.
- Verify explanations and claims against the actual code rather than treating them as proof.
- Follow the team’s normal review and release process.
These checks are especially important because reported acceptance does not certify quality, and the cited studies do not establish long-term effects on software quality, maintainability, security, or developer skill retention.
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Will AI replace software developers?
The cited studies do not settle whether AI will reduce, increase, or otherwise change software employment. They examine task performance, reported work practices, and developer experience—not job displacement or job creation. They do show that developers continue to judge and verify AI output, but that alone cannot predict how roles or employment will change.
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