I Gave Five AI Systems the Same Architecture Test 10 Times. The Test Became More Interesting Than the Models

It started with DeepSeek.

In conversations about AI architecture, it kept returning to the same ideas: persistent memory, state across interactions, learning from experience, and interaction with the environment. Other models repeatedly brought up similar themes.

That raised an obvious question:

— Do different AI systems consistently select different properties when asked what is fundamental to a general-purpose computational architecture?

Asking a model directly what it “needs” would be nearly useless. The answer would mix training data, prompt framing, and anthropomorphic interpretation.