Comment by Quothling

I knew OOP had failed when we started getting programmers who solved memory issues with batching instead of thinking about why they were loading that much data into the memory to begin with. For the previous 20-30 years that didn't really matter. Code was the bottleneck and implicity and abstractions helped developers ship changes faster. It turned out that it didn't give us maintainable or safe code bases, and there is a sweet irony to be found in the world of banking. Where the JAVA systems meant to replace the old parallel paradigms are now being replaced with systems that are better links between the COBOL systems and the customers than JAVA ever was.

AI changes that. Especially because it appears that LLM's can't understand the OOP abstractions any better than your hardware can compute it.

That being said. OOP and DOD both have advantages and disadvantages. If you go back to what I said first it wasn't exactly a failing of the OOP paradigm. The biggest issue I have with OOP is actually that it's too easy to do things wrong with it. Which isn't helped by the multimillion dollar industry which thrives on teaching developers everything except core computer science. People know their DRY, SOLID, CLEAN, TDD, Agile and every design pattern in the world, but they don't know how the interface they've just implemented actually handles their data.