AI in engineering

The unexpected things developer metrics can measure

We built metrics to evaluate developer tools. They turned out to explain everything from office design to Daylight Saving Time.

Jul 31 • Brian Houck

Measuring the impact of AI coding tools: Capacity, not horsepower

Separate what to measure from how, then measure across dimensions.

Jul 29 • Brian Houck

The State of AI Impact in Engineering: Q2 2026

Data from 500+ teams reveals that AI is delivering measurable velocity gains, but velocity alone isn't the story.

Jul 22 • Justin Reock

UKG’s system for driving effective AI use

How they created internal scorecards that managers could use to coach and guide their teams’ AI use.

Jul 15 • Abi Noda

Five studies changing how I think about AI in software engineering

AI compressed the upstream work. What does that mean for everything downstream?

Jul 10 • Brian Houck

From dashboards to decisions

What three real-world case studies taught us about productivity measurement that works.

Jul 8 • Brian Houck

Revisiting the DX Core 4 in the age of AI

Why the dimensions that matter most for engineering productivity remain stable, and how to interpret them as AI reshapes work.

Jun 24 • Brian Houck

AI-authored code has nearly doubled, but so has PR size

Findings from our analysis of over 400 organizations from the past year.

Jun 17 • Justin Reock

Five years later: Reflecting on SPACE with the people who built it

The authors of SPACE met in person for the first time. Here's what five years of AI, remote work, and real-world use taught them.

Jun 9 • Brian Houck

8 myths on software engineering and AI

What the latest research actually says about AI's impact on developers, and where leaders are still getting it wrong.

Jun 3 • Brian Houck

The AI efficiency plateau

Tracking the trajectory of developer time savings from AI

May 27 • Brian Houck

AI productivity debate

Researchers and practitioners weigh in on AI’s current and future impact.

May 20 • Justin Reock

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