David wasn't the only one who had noticed the problem. Over the next few weeks, other senior engineers started speaking up.

"I found three bugs this week," said Maria, who had been with the company for twelve years. "All caused by 'optimizations' that removed code I wrote specifically to prevent those bugs."

"The new hires are confused," added James, a team lead. "They can't understand why the code works the way it does, because all the explanatory comments are gone. It's taking them twice as long to get up to speed."

"The AI is creating technical debt," David argued in the next team meeting. "It's just a different kind—invisible debt. We're losing the wisdom embedded in our codebase."

The product manager, who had been quietly taking notes, spoke up. "What's the business impact?"

David paused. He knew this was the wrong question, but he also knew it was the only question that would matter to leadership.

"Short term? Probably minimal. Long term? We're eroding our ability to understand and maintain our own systems. Every deleted comment is a future debugging session. Every removed error handler is a potential outage."

"So... we can't quantify it," the product manager concluded.

"Not yet. But when the next major bug hits, we'll be able to."

As if on cue, the next major bug hit two weeks later. And this time, it wasn't just a payment processing error. It was a data corruption issue that affected thousands of customer records.