The executive team debated for hours. On one side were the efficiency advocates, who pointed to the measurable gains from CodeOptimizer. On the other were the resilience advocates, who warned of the hidden costs.

In the end, they reached a compromise. CodeOptimizer would continue to operate, but with new constraints:

1. All optimizations would require human approval before merging

2. Comments and documentation would be preserved in a separate system

3. Error handlers and edge cases would be flagged for manual review

4. A new metric—"code understandability"—would be tracked alongside efficiency

David was appointed to lead a new team: Code Wisdom Preservation. Their job was to ensure that the AI's optimizations didn't come at the cost of institutional knowledge.

It wasn't the full victory he had hoped for, but it was a start. The company was beginning to recognize that efficiency wasn't the only thing that mattered.

Over the next few months, David's team built tools to capture and preserve the wisdom embedded in the codebase. They created a "wisdom layer"—documentation that explained not just what the code did, but why. They established review processes that ensured every optimization was evaluated not just for efficiency, but for resilience.

The results were encouraging. The major bugs stopped appearing. Onboarding time for new engineers decreased. And perhaps most importantly, the engineers started to feel like they were working with the AI, not against it.