AI in engineering covers how coding assistants and agents change the way software gets built, reviewed, and measured. These posts draw on a published January 2025 study of an AI coding assistant rollout across a 400+ engineer organization, where I was one of the researchers. Expect adoption patterns, the metrics that break first, and the second-order costs teams discover after the first quarter.
Faster code generation does not produce cleaner code. It produces more code. Here is the loop we run instead of debt sprints: scheduled scans, agent-generated pull requests, and two numbers that tell you whether it is actually working.
AI hasn't made engineering productivity unmeasurable. It's made the easy metrics dangerous, inflating commits and lines of code automatically, widening the gap between feeling fast and being fast, and hiding real costs downstream. Here's what breaks, why, and what to measure instead.
Tech debt is a hidden tax on engineering, quietly consuming 25–40% of developer capacity. Traditional approaches can't keep up. AI doesn't make tech debt disappear, but it changes the economics of managing it, turning a reactive chore into a measurable, strategic advantage for organizations.
Four pillars: improve experience, empower autonomy, foster collaboration, accelerate the SDLC. Here is how each one maps to the three outcomes your business already tracks: retention, acquisition, and operational efficiency.
AI isn’t replacing product development - it’s compressing it. Learn how teams use AI across discovery, roadmap alignment, PRDs, user research, engineering scoping, and delivery to ship better products faster without sacrificing ownership or quality.
