The data corruption incident triggered a full investigation. The company brought in external consultants, conducted interviews, and reviewed the codebase history.
David was asked to present his findings. He stood in front of the executive team, feeling the weight of the moment.
"Six months ago, we deployed CodeOptimizer," he began. "Since then, it has processed over 10,000 pull requests, optimizing our codebase for efficiency. The metrics look great: 40% fewer lines of code, 25% faster execution, 60% fewer bugs in new code."
He clicked to the next slide. "But there's another story. In the same period, we've had three major outages, all caused by the removal of 'redundant' code that was actually critical. We've seen a 200% increase in onboarding time for new engineers, because they can't understand the context behind our code. And we've lost an estimated 500 engineering hours to debugging issues that should have been prevented."
The room was quiet. David continued.
"The AI is optimizing for the wrong metric. It's optimizing for code efficiency, when what we should be optimizing for is system resilience and human understanding. The 'redundancy' it's removing isn't waste—it's wisdom."
The VP of Engineering spoke first. "What are you proposing?"
"I'm proposing we change how we use CodeOptimizer. Instead of automatic merges, we require human review of every optimization. Instead of deleting 'redundant' code, we preserve it in a separate documentation layer. And instead of measuring success by lines of code removed, we measure it by system reliability and developer productivity."
The CEO, who had been silent until now, leaned forward. "And if we do nothing?"
David met his gaze. "Then we'll keep having these conversations. And eventually, we'll have a bug that we can't fix, because no one will remember why the code works the way it does."
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.