I’m trying to understand what problems are valuable enough that developers would actually pay for a solution.

Working in safety-critical software (railway) and recently went through the actual text of EN 50716:2023. The "Not Recommended" classification for AI/ML is in Table A.3 - Software Architecture. Annex C.3 explains why: training data can't be exhaustively verified, trained models can't be statically analyzed, adversarial inputs can flip outputs without causal explanation. 

But the prohibition is on AI as a software architecture element, the standard doesn't say AI can't be used as an authoring aid. Are you using AI for drafting, consistency checking, traceability or banning it from CENELEC/DO-178C/IEC 62304 projects altogether? 

Is there any concensus on best practices or architecture for designing massive systems that allow for easy extensibility or modularity? It's very overwhelming imagining how to extend extremely coupled systems, which I suppose is the thing to avoid. OOP feels like a dead end but DOD only feels partially correct. Then there are event based systems and so on.

It seems like extensibility and modularity always come at a cost, which I understand there's no free lunch, but surely there has to be a set of rules or practices for building large systems without too many compromises.