The Tool Desk
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Because generating code is only one part of finishing a software change. AI can produce a first draft quickly, yet prompting, review, testing, debugging, and integration can absorb the saved time—or even make the whole task take longer. Whether that happens depends on the work, developer, codebase, and tools; the available evidence does not show that AI universally speeds up or slows down coding.
Why fast code generation may not mean a faster finished task
A coding assistant can shorten the trip from an idea to a block of code. But a task is not complete when the code appears: it must fit the existing system, behave as intended, pass tests, and be safe to maintain. If a suggestion is wrong in a subtle way, the time saved during drafting can reappear as review and debugging work.
For a fair comparison, count the whole task: writing and refining prompts, waiting for suggestions, inspecting them, creating and running tests, fixing failures, and integrating the change. “Time to first draft” and “time to a working, maintainable change” are different measures.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsWhat the task-time evidence says
METR’s 2025 study found longer completion times in a specific setting
METR’s randomized study involved 16 experienced open-source developers completing 246 tasks in mature projects they knew well; participants averaged five years of experience with those projects. With tools available from February through June 2025, the study estimated that tasks took 19% longer when developers had AI access. That is a result for this group, repository context, task set, and period—not a forecast for every developer or current tool.
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After completing the tasks, participants estimated that AI had reduced their completion time by 20%, despite the measured increase. This illustrates why impressions of speed can differ from tracked end-to-end time; it does not establish that every developer misjudges their productivity.
METR’s 2025 study is the closest evidence here to the question of total task time in real repositories. Its limited sample and setting matter when applying the result to other work.
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The size of the effect from newer tools is not settled
In a February 24, 2026 update, METR said problems with participant selection and timekeeping made its later experiment too noisy and biased to reliably quantify the current productivity effect. The update says conversations with participants suggest developers may be more sped up in early 2026 than METR’s early-2025 estimate, but it describes the experiment’s evidence for the size of that change as very weak. It does not provide a dependable new speedup figure.
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METR’s update therefore supports uncertainty about today’s effect size, not a precise claim that newer tools do or do not save time.
Why other studies can show benefits without contradicting that result
GitHub measured code quality on one bounded exercise
GitHub’s randomized code-quality study analyzed 202 valid submissions from developers with at least five years of Python experience. Participants worked on one fictional restaurant-review API endpoint. The group with access to Copilot was 53.2% more likely to pass all 10 unit tests.
That finding concerns test performance on a defined exercise. It does not measure debugging time or end-to-end completion time in developers’ own mature repositories, so it neither confirms nor disproves METR’s task-time result. See GitHub’s study for its task and method.
DORA measured delivery outcomes and reported associations
DORA’s 2024 report found positive associations between AI adoption and individual productivity, flow, and job satisfaction, alongside negative associations with delivery stability and throughput. It estimated a 1.5% reduction in delivery throughput and a 7.2% reduction in delivery stability for each 25% increase in AI adoption. These are report-level estimates and associations, not proof that AI caused a particular developer’s debugging burden.
The findings can coexist: an individual may feel more productive or complete coding work more quickly while a team’s delivery outcomes remain difficult. DORA’s evidence concerns organizations and delivery, not an individual task timer. Its report emphasizes keeping changes small and testing robustly; see the 2024 DORA report.
Surveys describe perceptions, not causal task-time effects
GitHub’s 2024 survey, updated in 2025, included 2,000 respondents across the United States, Brazil, Germany, and India. It asked about AI use and perceptions; it was not a controlled measurement of time saved. GitHub also cautions that AI-generated tests, like generated code, need human review so missed scenarios are caught. GitHub’s survey findings are useful context about reported experience, not a stopwatch-based answer to whether debugging takes longer.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to find out whether AI is costing you time
No study in this evidence set directly measures whether AI causes you, in your own workflow, to spend more time debugging. A small local comparison can answer the practical question more usefully than counting suggestions or lines of generated code.
- Choose comparable tasks. Compare a run of similar work rather than one unusually easy or difficult task. Keep the project and task type as consistent as practical.
- Record the conditions. Note whether AI was available, which tool and version you used, your familiarity with the codebase, and the task’s complexity.
- Time the complete workflow. Include prompting, waiting, review, test writing and execution, debugging, and integration—not just typing or generation.
- Track quality as well as elapsed time. Record test outcomes, defects found during review, rework, and whether the change remains easy to inspect. A fast draft that creates more downstream correction may not be a net gain.
- Review generated tests yourself. Check that they exercise meaningful behavior and cover relevant scenarios; a passing AI-generated test suite is not a substitute for judging what should be tested.
- Keep changes small and test them robustly. Smaller batches make review and fault isolation more manageable, while reliable tests help catch mistakes before delivery.
After several comparable tasks, compare total time and quality between AI-assisted and unassisted work. Treat the outcome as a measurement of your workflow under those conditions, not a universal verdict on AI coding.
