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A burst of bug reports after adopting AI coding assistants does not, by itself, prove the team is introducing defects at a higher rate. If implementation speeds up while review, testing, debugging, and product decisions do not, more changes—and the defects in them—can reach users in a shorter period. The distinction matters: faster discovery is a plausible explanation for clustered bugs, not proof that AI leaves defect rates unchanged.
Why bugs can seem to arrive all at once
AI coding assistants can shorten implementation time, but they do not automatically shorten the time required to understand requirements, review changes, test behavior, diagnose failures, or decide what the product should do. When code arrives faster than those activities can keep pace, a team may release more work in a shorter window and encounter its problems closer together.
Otto Hilska, founder and CEO of Swarmia, put the timing argument this way in LeadDev on August 25, 2026: “What’s changed is the speed. Skip any of this and you used to get away with it for a couple of months. Now, you find out in the second week.” That describes a possible change in when teams discover problems. It does not establish that AI-assisted code has the same, lower, or higher defect rate.
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What the available numbers show—and what they do not
Swarmia’s June 16, 2026 analysis of its own platform data reported that median lines changed per pull request roughly doubled between Q1 2025 and Q1 2026 across a sample of more than 1,450 engineering organizations. Among organizations with at least 1,000 pull requests in each period, the median increase was 97.5%. Swarmia is the source of these figures; the reported sample is not established as independent or representative.
The 97.5% figure is a measure of pull request batch size, not bugs, incidents, or defect rates. Larger changes may be harder to review and can make it more difficult to isolate the cause of a failure, but batch size alone cannot show that code quality declined. The figures support a discussion about faster, larger batches—not a causal conclusion about AI and defects.
How to tell whether quality is actually getting worse
Do not use the raw number of bug reports as a stand-alone verdict. A team shipping more changes may see more bugs in total even if the proportion of changes that fail has not risen. Compare periods or teams with work volume and batch size in view, and examine several signals together:
- Change failure rate: how often changes lead to a failure or require remediation, interpreted consistently across the periods being compared.
- Time to recovery: how long it takes to restore service or resolve the impact after a failure.
- Pull request batch size and review time: whether larger changes are taking longer to review or are associated with more difficult diagnosis.
- Maintenance share: how much engineering capacity goes to fixes, upkeep, and keeping systems reliable rather than new features.
- Deployment confidence: whether engineers feel comfortable shipping, and what risks or missing context explain their answer.
Interpret throughput and quality measures together. No single metric establishes overall quality: a rising bug count can accompany rising output, while stable incident totals may conceal slower recovery or growing maintenance work.
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How to make room for fixes without stopping feature work
Hilska’s recommendations focus on preserving engineering practices as implementation accelerates, rather than treating quality as a cleanup project for a future pause:
- Make code review an engineering conversation, not merely a checkpoint; discuss intent, design, risks, and system context.
- Write tests as implementation proceeds so expected behavior and regressions are addressed alongside the change.
- Pair on work where the cost of being wrong is high, and make time for design before a fast implementation hardens a poor decision.
- Give technical debt and maintenance a predictable share of capacity. If fixes happen only when feature work is paused, the organization has not made a durable allocation for them.
- Build engineers’ understanding of the product and help product managers prototype. When coding gets faster, unclear product decisions can become the bottleneck.
These are Hilska’s recommendations, not a universal allocation formula. The practical point is to decide openly how much capacity goes to root-cause fixes and ongoing maintenance, then review whether the quality signals improve without relying on occasional emergency slowdowns.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to conclude from a post-adoption bug spike
A short-term cluster of bug reports is a reason to investigate, not enough evidence to conclude that AI caused more defects. Check whether more work or larger batches are reaching production, compare failure and recovery measures, and ask engineers where shipping feels unsafe. If defects per change or change failure rate are worsening, address the underlying causes; if total reports rose alongside output while quality rates remained steady, timing and volume may explain part of the increase.
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