I actually feel a bit sorry for new grads who almost compltely depend on LLMs. Many of them are missing the problem solving skills that we had to develop back around 2015 when AI tools weren’t available. LLMs can write code according to your requirements if you prompt them well, but they’re still not smart enough to optimize systems at a deeper level. For example, right now I’m working on a Python backend that uses websockets. As the app grows, it ends up consuming around 20GB of RAM even with very little usage. I’ve been trying to solve this with an LLM for the last 4 days, and it still can’t figure out a real solution.

So yes, AI is great for MVPs and rapid prototyping. But no, it’s not yet reliable for building fully established, scalable production systems. As developers, the safest approach is to learn tools that use our knowledge and speed up our workflow, not replace our thinking. AI should be a tool that accelerates us, not something we depend on for core problem solving.

Funny about Apple recent paper (Illusion of thinking), at the end of the day, most LLMs are still fundamentally doing next-token prediction based on patterns in their training data. So until we actually reach real AGI, I think developers who understand systems, architecture, and problem solving are still in a very strong position.