If I told you one of your production services had a memory leak, you'd probably stop everything to investigate.
Memory leaks are dangerous.
Performance slowly degrades.
Systems become unpredictable.
Eventually something crashes.
Now consider a different kind of memory leak.
One that affects engineering organizations instead of software.
I think nearly every company has one.
Most just don't recognize it.
Ask a new engineer a simple question.
> Why did we build the authentication system this way?
If the answer is:
> You'll have to ask Sarah.
You don't have documentation.
You have a dependency.
Organizations often mistake information storage for organizational memory.
A Confluence page isn't memory.
Neither is Slack.
Neither is a Git repository.
Memory is the organization's ability to reconstruct understanding without depending on a specific individual.
That's a much higher bar.
Engineers leave companies.
People change teams.
Architects retire.
Consultants finish projects.
None of that is unusual.
What's unusual is how much understanding disappears each time.
Not because anyone failed to write documentation.
Because documentation usually captures conclusions.
It rarely captures reasoning.
Future engineers inherit:
What they don't inherit is the chain of thinking that connected all those pieces together.
That disappears surprisingly fast.
Organizations with memory leaks tend to ask the same questions over and over.
Sometimes the answers exist.
More often, the people who knew the answers have moved on.
The organization starts paying interest on forgotten decisions.
We talk constantly about technical debt.
We almost never talk about reasoning debt.
Reasoning debt accumulates whenever the implementation survives but the explanation disappears.
Unlike technical debt, reasoning debt doesn't immediately slow down software.
It slows down engineers.
Every change begins with archaeology.
Engineers read old pull requests.
Search Slack.
Message former employees.
Reverse engineer architecture.
They aren't building.
They're reconstructing.
Many people assume AI will simply answer these questions.
But AI can only synthesize information that exists.
If the reasoning behind a decision was never captured, AI has to infer it from the implementation.
Sometimes it'll infer correctly.
Sometimes it won't.
That's a risky foundation for production engineering.
The better solution isn't smarter AI.
It's richer engineering memory.
Imagine clicking on a service and immediately seeing:
Original Intent
Why it exists.
Assumptions
What had to be true for this design to make sense.
Alternatives
What was considered and rejected.
Predictions
What the team expected to happen.
Evidence
What actually happened after deployment.
Now imagine every engineer contributing to that body of knowledge over time.
The organization no longer depends on perfect recollection.
It develops institutional memory.
Software doesn't remain static.
Neither should organizational understanding.
Every incident.
Every migration.
Every architecture review.
Every production deployment.
Should leave behind more understanding than existed before.
That's how organizations compound knowledge instead of repeatedly recreating it.
If your engineering organization keeps asking the same questions every few years, you probably don't have a hiring problem.
Or a documentation problem.
Or an AI problem.
You have a memory leak.
The solution isn't writing more documents.
It's preserving the reasoning that future engineers will otherwise be forced to reconstruct.
Software engineering solved memory leaks in programs decades ago.
The next challenge is solving them in organizations.
The way to do that is to treat memory as infrastructure — not a documentation project, not a wiki initiative, but a dedicated layer that preserves decisions, assumptions, evidence, predictions, incidents, and outcomes alongside the systems they describe. Every successful computing platform eventually adds memory. Engineering organizations should be no different. Without it, every generation of engineers starts by reconstructing what the previous generation already learned, and AI systems trained to help inherit all the same gaps.