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20/08/2026 5:42 pm
TL;DR: AI is creating a “great toil shift” in software delivery, with 88% of developers reporting at least one negative impact on technical debt and 53% saying generated code often looks correct while hiding defects, according to Sonar’s State of Code Developer Survey. The security and governance implication is that faster generation without deterministic verification turns code review into a control point, not a postscript.
NHIMG editorial — based on content published by Sonar: State of Code Developer Survey, Chapter 4 on the new developer toil
By the numbers:
- 88% of developers report at least one negative impact of AI on technical debt.
- 53% of developers say AI generates code that looks correct but is unreliable.
- 70% of developers are already using static code analysis tools.
Questions worth separating out
Q: How should security teams govern AI-generated code in production pipelines?
A: Security teams should treat AI-generated code as a controlled identity event, not just a development artifact.
Q: Why does AI-generated code create more technical debt so quickly?
A: AI increases output volume and repetition, so insecure patterns can spread before teams notice them.
Q: What do teams get wrong about static code analysis and AI-assisted development?
A: They often treat static analysis as a quality add-on instead of a release control.
Practitioner guidance
- Enforce deterministic verification before merge Require static analysis, tests, and policy checks to pass before AI-assisted code can merge into main branches.
- Track generated-code rework as a control metric Measure how often AI-generated code is rewritten, rejected, or patched after review.
- Separate experimentation from release paths Allow developers to use AI for drafting and exploration, but require hardened review gates for anything that can reach production systems or security-sensitive workflows.
What's in the full report
Sonar's full report covers the operational detail this post intentionally leaves for the source:
- Developer survey breakdowns on where AI increases rework, debugging effort, and code cleanup across different experience levels
- The specific static analysis practices Sonar associates with better code quality and lower rework costs in SonarQube environments
- Chapter-level findings on how AI changes technical debt, documentation work, and legacy-code maintenance
- The survey framing and response context behind the “great toil shift” findings
👉 Read Sonar's analysis of AI, developer toil, and technical debt →
AI-generated code quality: is verification keeping up with speed?
Explore further
20/08/2026 6:25 pm
AI-generated code creates a verification debt, not just a productivity gain. The article shows that output speed is improving faster than assurance quality, which leaves teams with more artefacts that appear correct but have not been meaningfully validated. That is the same governance pattern identity teams see when provisioning or automation scales faster than review. The discipline shifts from producing more to proving more, and practitioners should treat verification as a core control.
A question worth separating out:
Q: How can organisations tell whether AI is improving code quality or just increasing output?
A: Look at rework rates, defect escape rates, and how often generated code is rewritten after review. If output rises but cleanup effort and remediation also rise, the programme is producing volume rather than quality. Real improvement shows up in fewer escaped defects and lower review friction.
👉 Read our full editorial: AI code generation is shifting technical debt into verification
