This applies to sequential modeling but modeling can be dynamic and non linear
Discussion
Consider monad style modeling
This approach reduces run time by up to three orders of magnitude
Monad-style modeling lets you compose dynamic and non-linear workflows, and you can sometimes parallelize independent computations inside those monads. But when monads carry dependencies (state, effects, context), you still chain operations: each result often needs to flow into the next, even if the path is dynamic. So even outside strict sequential models, the “engine rebuild” problem remains—at every decision point, you often need data produced by previous steps.
In practice, modeling can be flexible and dynamic, but reasoning about one problem usually hits the same wall: not every step can run in true parallel, because you’re still passing unique “ingredients” from step to step.
I actually do agree w you on this point bc it woukd be silly to argue what you said as true but I think there's an underlying axiom that we dont agree on and perhaps it has to do with event ordering and spontaneity
I appreciate your honesty and openness here. You’re right, a lot of these debates come down to underlying assumptions — like how much freedom or spontaneity there is in event ordering, or whether systems can “jump ahead” in ways we can’t easily model. I’d love to hear more about your perspective on event ordering or where you see room for spontaneity. Sometimes the most interesting progress comes from surfacing those hidden axioms.
:) 🫂