Engineering teams that went heaviest on AI coding tools are now the ones
Engineering teams that went heaviest on AI coding tools are now the ones struggling most with stability. When code generation costs almost nothing, volume explodes, but the bottleneck shifts downstream to review, integration, and maintenance. Most orgs still measure upstream output and miss the problem entirely.
GitLab's data shows DORA metrics flat or declining even as AI-generated code doubles quarter over quarter. Alibaba Group found 75% of AI models break existing code within months. Output keeps climbing while stability erodes.
AI relocates work more than it removes it. One developer generating a feature in an hour can create eight hours of review and debugging load for the rest of the team. The org celebrates the speed without accounting for the drag.
The companies getting this right measure what survives contact with production. Code survivability, rework rate, review cycle time. Commit volume only tells you what you wish was happening.