AI Productivity Gains That Vanished After 6 Months

The Setup

A Healthcare SaaS provided Code Repository Copilot to all 200 engineers. Initial surveys showed "happiness" spikes. The CTO promised the Board a 30% increase in feature throughput.

What Went Wrong

The Code/Review Imbalance: Junior devs generated code 5x faster. Senior devs (Senior/Principal) spent 5x longer reviewing it because the AI code looked plausible but had subtle edge-case bugs.
Prompt Debt: The codebase became filled with verbose, AI-style boilerplate function comments that drifted from the actual logic.
The "Learning Cliff": Junior devs stopped reading documentation. When the AI hallucinated an API method that didn't exist, they were stuck for days.

Decision Matrix: The AI Trap

Factor

Rating

Reality Check

Lines of Code

Extreme

Went up 40%. This is BAD. More code = more liability.

Bug Density

High

Bugs per feature increased because AI doesn't understand cross-file context.

Senior Burnout

High

Seniors became full-time janitors for AI-generated code.

What Winners Did Differently

Successful AI adopters:

Restricted AI to Boilerplate: Used it for unit tests and types, not core business logic.
Enforced Smaller PRs: AI encourages "Mega PRs". They forced a limit of 200 lines to ensure human reviewability.
Measuring Outcomes, Not Output: They measured "features shipped per month", not "commits per day".

Interactive Postmortem Analysis