The AI Coding Assistant Productivity Paradox

You're leading engineering at a company that's adopted AI coding assistants (like Cursor, GitHub Copilot) across the team. The promise is huge: AI can write code faster than humans, potentially making engineers 10x more productive.

After six months, you're seeing mixed results:

The good:

• Engineers are writing more code faster
• Features are getting shipped more quickly
• Developers report enjoying using the tools
• Code generation feels magical

The concerning:

• Production bugs have increased
• Code review is taking longer than before
• Engineers are spending lots of time debugging AI-generated code
• Some engineers report feeling less productive overall
• Customer issues are creeping up

One engineer explains: 'When I write code myself, I can quickly spot my own bugs. But debugging someone else's code is hard - and AI code feels like someone else's code. I spend more time trying to understand what the AI wrote than I would have spent just writing it myself.'

A recent study shows engineers are actually less productive with AI assistants for complex tasks, even though they're writing more lines of code.

Your CEO is pushing to double down on AI coding tools. Your VP Engineering is skeptical and wants to pause the rollout. Some engineers want more AI, others want less.