Development Productivity, Not Developer Productivity (redmonk.com)

AI code-generation tools have rapidly become the dominant AI use case in 2025, speeding individual developers and enabling “vibe coding,” but industry workflows haven’t kept pace—creating a “lump-in-the-snake” problem where output outstrips the software delivery lifecycle’s ability to absorb it. Evidence from the 2024 DORA report shows a paradox: roughly 75% of people say AI makes them more productive, yet a 25% increase in AI adoption is associated with estimated drops in valuable work time (‑2.6%), throughput (‑1.5%), and delivery stability (‑7.2%). Adoption concentrated around IDE-focused solutions (notably VS Code ecosystems) and a rich training corpus made code gen an easy early win, while SecOps, CI/CD, testing, and governance tooling remain fragmented and less mature. For the AI/ML community this matters because developer-facing gains don’t automatically translate to better system-level outcomes. Key technical implications: invest in scalable digestion layers—automated testing, robust CI/CD, security automation, observability, code review guardrails, and governance—to prevent regressions in stability and throughput. Reevaluate success metrics beyond individual “time saved” and toward holistic telemetry (DORA-style throughput/stability, cost-to-serve software). In short, celebrate faster code generation—but focus R&D and product efforts on making the rest of the SDLC digestible at that same pace.