AI in Engineering

What the 2025 DORA Report means for your AI strategy

AI amplifies bad practices, real gains come from focusing AI efforts on systems, and success depends on strong change management.

Oct 8 •

AI’s impact on quality: A volatile, uneven landscape

Data from 43,000 engineers across ~100 companies shows quality outcomes ranging from big gains to serious declines.

Oct 1 •

Who leads in AI adoption: Tech or non-tech?

Data from more than 50,000 developers suggests that traditional enterprises are beating big tech on AI adoption and time savings.

Sep 24 •

How 18 companies measure AI’s impact in engineering

The real metrics that GitHub, Google, Dropbox, and 15 other companies use to measure AI impact.

Sep 17 •

AI cuts onboarding time in half for new hires in the enterprise

Data from DX shows that engineers who use AI daily reach onboarding milestones nearly twice as fast as non-users.

Sep 10 •

The evolving role of DevProd teams in the AI era

AI is reshaping the mandate for platform teams. Here’s what they should focus on to stay relevant and drive impact.

Sep 4 •

and

The SPACE of AI: Real-world lessons on AI’s impact

Developers overwhelmingly believe AI enhances their productivity, yet its impact is far from uniform across all tasks and teams.

Aug 21 •

How Faire's platform team built an AI code review agent

A closer look at how Faire is putting AI to work beyond just code generation.

Aug 13 •

and

Unpacking METR’s findings: Does AI slow developers down?

Why the slowdown occurred, and advice for using AI tools more effectively.

Aug 6 •

Measuring engineering productivity in the AI era

How to track AI's impact, keep an eye on quality, and use metrics to drive action.

Jul 30 •

and

METR’s study on how AI affects developer productivity

In this study, developers were 19% slower when using AI.

Jul 23 •

Introducing the AI Measurement Framework

A research-based approach for measuring the impact of AI-assisted engineering in your organization.

Jul 9 •

and

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