We Doubled Our AI Tooling Budget. Our Release Rate Dropped Anyway

· Source: Towards AI - Medium · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, medium

Summary

Despite doubled AI tooling budgets and increased commit volumes, engineering release rates to production are declining, revealing a critical disconnect in productivity metrics. CircleCI's 2026 State of Software Delivery report, based on over 28 million CI/CD workflows, shows a 59% year-over-year jump in daily workflow runs. However, for the median team, feature branch throughput rose only 15% while main branch throughput actually fell, indicating more code is written but less is shipped. This shift suggests AI coding assistants move bottlenecks downstream to code review, testing, and deployment. The top 5% of teams nearly doubled output, while the bottom quartile saw no real gain, widening the performance gap. AI amplifies existing engineering culture; strong processes become more efficient, while weak ones become messier. The article advocates tracking main branch success rate (healthy target 90%, industry average 71%), mean time to recovery, and review depth, over vanity metrics like commit count.

Key takeaway

For Directors of AI/ML or VPs of Engineering evaluating AI coding tool ROI, your focus must shift from commit volume to actual delivery metrics. If your dashboard shows increased throughput but your gut senses slowdowns, trust it. You should prioritize tracking main branch success rate, mean time to recovery, and review depth. These metrics reveal where AI-generated code bottlenecks occur, allowing you to invest in validation and review processes rather than just more code generation tools. This ensures AI truly accelerates shipping, not just writing.

Key insights

AI coding tools inflate code output but often shift bottlenecks downstream, hindering actual delivery.

Principles

Method

Measure developer productivity by splitting throughput into feature branch and main branch activity, tracking main branch success rate, mean time to recovery, and review depth.

In practice

Topics

Best for: CTO, Executive, Director of AI/ML, VP of Engineering/Data, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.