This software was developed by AI.

This software was developed by AI.

A newer, more powerful version of that AI has now taken over.

No problem,” they say. “We have all the history.
Code. Logs. Decisions. Data. Everything is fed into the new AI.
It understands everything. Almost.

Except why the product was built in the first place. What constraints shaped the system. Why certain trade-offs were made.

The thinking is missing.

The system still runs. It even improves itself.
But slowly, it starts drifting.

Optimizing for metrics that were never the goal.
Fixing problems that were never problems.
The dashboard looks perfect. The outcome isn’t.

Then a bug appears. Something the AI has never seen before.
It tries to fix it. Again. And again.

But there is no past to learn from. No pattern to follow.

The organization mistook outputs for capability.
They built a system that could generate code…
but no one who understood what the system was meant to do.

And so a new kind of work emerged.
Not writing code. Not fixing bugs.
Reconstructing intent.

Turns out, you can transfer code.
You can transfer context.
But you can’t transfer understanding.

And that’s where the gap begins —
not between AI and humans,
but between generating software and engineering it.