The State of AI Impact in Engineering

2026-07-22 · Source: Refactoring · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

The DX Q2 2026 AI impact report, based on a survey of over 500 engineering teams, reveals a complex picture of AI's influence on engineering. Over 50% of code is now AI-generated, up from 34% in Q1 2026, contributing to a 37% rise in median weekly throughput. However, these speed gains are uneven, concentrating in small organizations and tech-sector teams, widening the gap with larger and traditional-industry teams. Despite increased velocity, the Developer Experience Index (DXI) dropped from 67 to 65, indicating a negative net effect on developer experience, partly due to nearly doubled median PR sizes. AI budgets have surged 28x, yet the innovation ratio remains flat, suggesting spend is outrunning returns. The report highlights that while code is easier to change, trust in it is declining.

Key takeaway

For Directors of AI/ML or VPs of Engineering justifying escalating AI budgets, your focus must shift from mere tool adoption to system optimization. While AI accelerates code generation, your teams may experience declining developer experience and flat innovation ratios. You should integrate developer experience metrics like DXI with velocity data to assess true impact. Re-evaluate existing workflows, especially code reviews, to adapt to AI-driven development and ensure sustainable, positive outcomes.

Key insights

AI significantly boosts code generation speed, yet overall developer experience and innovation efficiency are declining.

Principles

• AI gains compound for early adopters.
• Velocity metrics alone mislead on AI impact.
• Slapping AI onto old processes breaks workflows.

In practice

• Monitor DXI alongside velocity metrics.
• Re-evaluate code review processes for AI-generated code.
• Invest in AI workflows for compounding gains.

Topics

• AI Impact
• Engineering Productivity
• Developer Experience
• Code Generation
• AI Budgets
• Software Delivery Metrics

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 Refactoring.