Sometimes. AI can help developers finish particular coding tasks faster, but more generated code can also mean more to understand, test and maintain. Studies report both faster workflows and added review or rework burdens; the outcome depends on the task, the team and what is measured. That makes review capacity a real operational risk—not a universal consequence of using AI.
What the evidence says about review and rework
Open-source projects: review work shifted toward core maintainers
A 2025 preprint by Feiyang Xu and co-authors, updated to version 3 on January 28, 2026, analyzes developer activity in open-source projects after GitHub Copilot’s introduction. The authors report that core developers reviewed 6.5% more code and saw a 19% decline in their original code productivity. They associate the added burden with rework and maintenance, while reporting productivity gains among peripheral or less-experienced contributors.
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This is observational, project-level evidence, not proof that Copilot alone caused the changes or that every team will see the same pattern. It does, however, illustrate how faster contributions from some developers can shift work toward experienced maintainers.
Survey: developers report extra verification effort
Sonar’s 2026 survey of more than 1,100 professional developers found that 38% said AI-generated code took more effort to review than human-written code. Sonar also reports that 96% of respondents did not fully trust AI-generated code and 48% always verified it before committing. These are respondents’ reported views, not repository measurements or evidence that AI caused review delays. Sonar says respondents estimated AI accounted for 42% of committed code; that figure is self-reported, not a direct measurement of repositories.
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Why the results are not all negative
Enterprise workflow outcomes improved in one study
GitHub and Accenture reported enterprise research that combined a randomized controlled trial with a company-wide adoption analysis. In that participating organization, the report found a 15% increase in pull-request merge rate and 84% more successful builds. It also reported that about 30% of Copilot suggestions were accepted. These findings show that more AI-assisted output does not automatically mean worse review outcomes, but they describe one company and its workflows rather than a universal effect.
A constrained coding task produced favorable quality results
In a GitHub Customer Research study, developers with at least five years of Python experience worked on a fictional restaurant-review web-server task. Of the original 243-person sample, 202 submissions were valid. GitHub reported that the Copilot-access group was 53.2% more likely to pass all ten unit tests and that Copilot-authored code was 5% more likely to be approved. The blind review phase involved 25 developers and 1,293 reviews.
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The study’s code-error rubric focused on readability and maintainability practices, not functionality errors. Its results are useful evidence about a bounded exercise, but they do not measure the workload or long-term quality of a production team’s review queue.
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Why AI coding can speed one task and slow another
Task completion time, code quality, review effort and delivery speed are different outcomes. A tool can help someone produce a working first draft quickly while leaving a maintainer with more context to reconstruct, more changes to verify or more follow-up work. Conversely, assistance that helps produce readable, tested changes can improve the path to approval or a successful build.
- Task and codebase: A short, isolated exercise is unlike changing a large, established project with unfamiliar conventions and dependencies.
- Developer experience: A new contributor’s extra output can create work for core maintainers, while an experienced developer may be able to direct and validate suggestions more efficiently.
- Tool and workflow: Autocomplete, chat assistance and agentic task execution do not create identical changes or review needs. Results also depend on how teams test and integrate them.
- Outcome and time horizon: A benchmark can capture immediate task time or test results; it may not capture review effort, later rework, defects or accumulated maintenance costs.
METR’s 2025 study, as reported by TIME, is a useful contrast: 16 experienced developers working on complex software projects estimated they were about 20% faster with AI, but measured results showed an approximately 20% slowdown. The authors cautioned against broad generalization. This result concerns that study’s participants and projects; it should not be averaged with enterprise or coding-exercise results to produce a single estimate of AI’s effect.
METR also reported that the length of tasks frontier AI agents could complete at 50% reliability had doubled roughly every seven months over the preceding six years. That is a trend in agent capability, not a measurement of current team delivery speed or review workload.
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How to tell whether review is becoming your team’s bottleneck
Measure the path from a change being proposed to its safe delivery, not just the amount of code generated or the number of suggestions accepted. Compare a defined period before and after adoption, or use matched teams or tasks where practical. Record whether AI was used and for what kind of work, and keep the comparison consistent enough to interpret.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors- Delivery: Track time from pull-request creation to merge, along with time spent waiting for review. A faster first draft is not the same as a faster delivery.
- Reviewer effort: Record review time, review rounds and the number of changes requiring clarification or correction. Ask reviewers about effort, but distinguish those reports from telemetry.
- Rework: Track follow-up commits, reopened changes, reverted code and work needed after review. Separate routine edits from substantive fixes where possible.
- Automated checks: Monitor test and build pass rates, and whether failures occur before or after review. Passing checks do not establish maintainability or the absence of defects.
- Quality over time: Watch defects, incidents and maintenance work after merge, not only approval rates. These outcomes may take longer to appear.
- Context: Compare similar task types, codebases and developer experience levels. A team’s workload, staffing or release schedule can change at the same time as AI adoption.
Use several measures together. A higher merge rate could reflect improved flow, but on its own it does not show that reviewers spent less effort or that later maintenance stayed stable. Likewise, a review queue that grows while build success improves calls for investigation, not an automatic conclusion that quality has fallen.
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What teams can do before the queue grows
These are practical controls, not guarantees that any one study has proved will eliminate review burden.
- Keep changes small and legible. Ask contributors to explain the intent and scope of a change so reviewers can evaluate decisions rather than infer them from a large generated diff.
- Require verification proportional to risk. Run relevant tests and code-quality or security checks before review, while treating their results as evidence to inspect—not a substitute for human judgment.
- Protect reviewer capacity. Make review ownership explicit and monitor queue age and reviewer workload. If assistance raises contribution volume, plan for the corresponding validation work.
- Make responsibility clear. The author remains accountable for understanding and checking submitted code, including AI-assisted changes. IBM Research’s internal watsonx Code Assistant case study underscores that perceived productivity benefits did not necessarily apply to every user and raises questions of ownership and responsibility for generated code.
- Review the measures regularly. If output rises but review effort, rework or post-merge problems rise too, adjust the workflow or limit AI use for the affected work until the team can validate it reliably.
How to interpret the findings
The evidence supports a conditional conclusion: AI can improve task or workflow outcomes, and it can also increase verification or maintenance demands in some settings. The Xu et al. preprint, company-published surveys and studies, and METR results differ in populations, methods and outcomes. None establishes that a review bottleneck is becoming universal across software teams.
For an engineering team, the practical question is whether assistance improves end-to-end delivery without outpacing the capacity to understand, test and maintain the changes. Generated lines are an input; review effort, rework, delivery time and quality after merge show whether the workflow is working.
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