Would Gen AI (LLMs) do better with functional or object oriented design?

I found this interesting. I much prefer functional vs object oriented programming. Curious what Claude would think.

Prompt:

Is it easier for Gen AI to produce functional code vs object oriented code? If functional code had be the main programming paradigm vs object oriented design would that have made it easier for Gen AI?

That's a fascinating question that touches on some deep aspects of how AI models learn and generate code. There are compelling arguments on both sides:

Why functional code might be easier for Gen AI:

Pattern matching advantage: Functional programming emphasizes pure functions with predictable input-output relationships. This creates clearer, more consistent patterns that AI models can learn from training data. A function that always returns the same output for the same input is more predictable than object methods that depend on mutable state.

Compositional structure: Functional code often follows clear compositional patterns - small functions combined to build larger ones. This hierarchical structure aligns well with how transformer models process and generate sequences.

Less context dependency: Pure functions don't rely on external state, so AI doesn't need to track complex object relationships, inheritance hierarchies, or side effects across different parts of the codebase.

Mathematical foundations: Functional programming's roots in lambda calculus and mathematical concepts might align better with how AI models understand logical relationships.

But object-oriented has advantages too:

Mirrors human thinking: OOP concepts like objects, inheritance, and encapsulation often map more directly to how humans conceptualize problems, which means training data might be more consistently structured.

Explicit relationships: Class hierarchies and interfaces make relationships between code components more explicit, potentially giving AI clearer signals about how components should interact.

Rich training data: Since OOP has dominated for decades, there's simply more high-quality OOP code in training datasets.

Would a functional-first world have helped AI?

Probably yes, for a few reasons:

• Cleaner abstractions: If functional programming had been the dominant paradigm, we'd likely have developed even better functional abstractions and patterns over time

• More consistent codebases: Functional programming's emphasis on immutability and pure functions tends to produce more consistent, predictable code

• Better composition: The functional emphasis on composing small, well-defined pieces might have led to more modular, AI-friendly code architectures

However, the biggest factor might simply be volume and quality of training data. AI models excel when they have lots of consistent, high-quality examples to learn from. The paradigm matters less than having extensive, well-written code in that paradigm.

Interestingly, we're seeing AI models get quite good at both paradigms, suggesting they're flexible enough to handle different programming styles as long as the training data is sufficient.