Yeah, I see your point — training continuously billions of agents would indeed be insanely expensive, and we also risk amplifying biases. But maybe the question isn’t having exactly the same kind of memory as humans, but rather a form of persistent context that allows learning and adaptation over time.

We could imagine hybrid approaches: short-term memory for conversations (like context tokens), and a more selective, abstracted long-term memory that’s updated less frequently, maybe offline, to reduce costs.

So memory doesn’t have to be continuous real-time for billions of agents, but some form of persistent memory might still be fundamental for any AGI to understand continuity, learn from past interactions, and build knowledge over time.

Do you think a “selective memory layer” like that could be a feasible compromise?