AI On A Team
Part of the Vibe Coding: Advanced Engineering course
Individual productivity gains from AI are real and they do not automatically become team productivity gains. Code that is faster to write is not faster to review, and review is where most teams were already constrained. This lesson is about the system-level consequences: what actually speeds up, what silently gets worse, and which policies are worth having.
Before AI, on most teams, writing was slow and review was slower, and both were roughly matched. When writing gets several times faster and review does not, the queue forms in front of review. This is Little's law, not an opinion, and the symptoms are recognizable ...
Want to learn Vibe Coding: Advanced Engineering AI On A Team in a real environment?
Click below, get a fresh Linux box with Vibe Coding: Advanced Engineering ready to go, and work through AI On A Team hands-on with an AI tutor that knows your code.
Start this lessonAI On A TeamWhat you'll learn
- Review capacity as the real bottleneck
- Shared conventions and rules files as team artifacts
- What review bots and CI agents are good and bad at
- Measuring impact without gaming the metric
Read this lesson in your language
All lessons in this course
12 lessons- 1Context Is The Whole Game35 min
- 2Pick The Right Brain, Pay The Right Price25 min
- 3Spec-Driven Development35 min
- 4Build Your Harness35 min
- 5Verification Loops: Make It Prove Itself40 min
- 6Reading Generated Code Like A Senior40 min
- 7Legacy Archaeology40 min
- 8Migrations At Scale40 min
- 9Debugging Code You Did Not Write40 min
- 10The Security Model Of An Agent40 min
- 11AI On A Team30 min
- 12Judgment: Knowing When Not To35 min
