Ask HN: Are there software engineering areas that are safe from LLMs invasion?

Legacy systems - there are legacy systems that are like house of cards and you have to move forward very carefully. These areas might have code/languages that are older and the LLM wont have as big a model to learn from

Businesses often rely on these systems - and they rely on the processes to protect them so are reluctant to adopt AI

From what I’ve seen, LLMs are good at making stuff that has already been made and posted to GitHub a thousand times before. At my job we’re constantly asked to do things that really haven’t been done before, at least not by people sharing source code, so the LLMs suck at most of it.

LLMs make for great tech demos, but when it comes to writing code for production that actually does something new and useful, it hasn’t impressed me at all.

* Consulting. Businesses are fond of repeating mistakes with great dedication that sometimes it takes some outside help to steer the ship right to great animosity from the people writing code.

* Accessibility. Accessibility isn’t a huge challenge unless you’re in a business with a pattern of largely ignoring it. Then it can be a huge challenge to fix. AI won’t be enough and it nightly likely require outside help.

* Speed. If you want faster executing software you need to measure things. AI will be learning from existing code that likely wasn’t well measured.

Fintech, banking..

Defence. We don't use any LLMs, and couldn't even if we wanted to.

To be fair the code they produce is dogshit, so it isn't a problem.

There's quite a few, although LLM's are slowly creeping in: 1. everything with less data to train on: - Compiler / language toolchain development. - Specialized embedded robotics (industrial robotics, drones). - Scientific / high-performance computing

2. Low tolerance for LLM-induced errors: - Network protocols / telecom software - Medical software - Aerospace, automotive

3. Performance-critical code: - Game engine / graphics engine development (probably an area where we'll see them soon) - Kernels, drivers, microcontrollers.

etc. Not all is lost yet.

Just get really good at something, in the top 10% where you would be writing books and disagreeing with reddit.

AI is predictive. Most people will fall to a comfort zone where AI tells them what to do. But you should become an expert and be one of the few who are telling it what to do.

Many excellent LLM are being created. I feel that this era is similar to the emerging automotive industry era. In other words, we are currently in an era of engine performance competition, competing for power and speed. However, I believe that this era will eventually transition to the next phase.

Embedded systems in infrastructure systems should be save as they not only need to be specific but are just important and dangerous but you never know.