Everyone is racing to make AI smarter.
Almost nobody is asking what it's allowed to remember.
I'm starting a daily series on the least understood layer in AI.
Day
1.
Here's the confusion at the center of it: we've started calling the context window "memory." It isn't.
A context window is a desk.
You pile things on it, you work, and at the end of the session someone clears the desk.
Bigger models just give you a bigger desk.
Memory is the filing cabinet.
What survives the desk being cleared.
So picture hiring a brilliant analyst with no long-term memory.
Every morning you re-brief them on the company, the customers, the decisions you already made together.
They nod.
They do genuinely excellent work.
And by tomorrow, it's gone.
You'd never call that person a knowledge worker.
You'd call it a very expensive Groundhog Day.
That is most "AI agents" running in production today.
The industry's answer has been to treat this as a storage problem.
Bigger context.
Another vector database.
Stuff more in, hope the right thing comes out.
I think that's the wrong frame entirely.
Memory isn't a storage problem.
It's a trust problem.
Tomorrow, Day 2: why RAG is not memory — and why confusing the two costs teams more than they realise. — We at Alphanimble building Memuron, a memory system for AI agents.
This series is the thinking behind it, in the open.
Every post is something I've had to figure out to build the thing.
AI #AIAgents #AIMemory