Research Report
AI Ships More Code. SRE Pays the Price.
AI coding tools are accelerating development velocity. Downstream teams — SRE, platform, on-call — haven't scaled with that throughput. This report maps the widening capacity gap with data from LinearB, GitClear, and Google's DORA research, and lays out three paths forward.
- 41%
- Capacity gap by 2027
- 2X
- Code churn increase since 2021
- -60%
- Decline in refactoring activity
What's in the Report
- The Data How AI coding tools are accelerating code velocity, with benchmarks from LinearB (6.1M PRs), GitClear (211M lines), and Google DORA
- The Quality Problem Why speed without discipline compounds technical debt: code churn up 2×, refactoring down 60%, system stability declining
- The Capacity Gap Code output vs. SRE headcount from 2021 through 2027, and why the gap compounds every year
- Three Paths Forward Scale ops capacity, constrain change, or scale operations with AI — and how to decide which fits
- A Diagnostic Question A simple frame to know whether this is already costing your team
"When incidents pile up, roadmap progress stops and people burn out."
— Director of SRE, Enterprise CX Company
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