Ask HN: SWEs how do you future-proof your career in light of LLMs?

I have personally used LLMs in my job to write boilerplate code, write tests, make mass renaming changes that were previously tedious to do without a lot of grep/sed-fu, etc. For these types of tasks, LLMs are already miles ahead of what I was doing before (do it myself by hand, or have a junior engineer do it and get annoyed/burnt out).

However, I have yet to see an LLM that can understand an already established large codebase and reliably make well-designed additions to it, in the way that an experienced team of engineers would. I suppose this ability could develop over time with large increases in memory/compute, but even state-of-the-art models today are so far away from being able to act like an actual senior engineer that I'm not worried.

Don't get me wrong, LLMs are incredibly useful in my day-to-day work, but I think of them more as a leap forward in developer tooling, not as an eventual replacement for me.

1. There will be way more software

2. Most people / companies will be able to opt out of predatory VC funded software and just spin up their own custom versions that do exactly what they want without having to worry about being spied on or rug pulled. I already do this with chrome extensions, with the help of claude I've been able to throw together things like time based website blocker in a few minutes.

3. The best software will be open source, since it's easier for LLMs to edit and is way more trustworthy than a random SaaS tool. It will also be way easier to customize to your liking

4. Companies will hire way less and probably mostly engineers to automate routine tasks that would have previously be done by humans (ex: bookkeeping, recruiting, sales outreach, HR, copywriting / design). I've heard this is already happening with a lot of new startups.

EDIT: for people who are not convinced that these models will be better than them soon, look over these sets of slides from NeurIPS:

- https://michal.io/notes/ml/conferences/2024-NeurIPS#neurips-...

- https://michal.io/notes/ml/conferences/2024-NeurIPS#fine-tun...

- https://michal.io/notes/ml/conferences/2024-NeurIPS#math-ai-...

The problem was it never worked. When generating the code, the best the tools could do was create all the classes for you and maybe define the methods for the class. The tools could not provide an implementation unless it provided the means to manage the implementation within the tool itself - which was awful.

Why have you likely not heard of any of this? Because the fad died out in the early 2000's. The juice simple wasn't worth the squeeze.

Fast-forward 20 years and I'm working in a new organization where we're using ArchiMate extensively and are starting to use more and more UML. Just this past weekend I started wondering given the state of business modeling, system architecture modeling, and software modeling, could an LLM (or some other AI tool) take those models and produce code like we could never dream of back in the 80s, 90s, and early 00s? Could we use AI to help create the models from which we'd generate the code?

At the end of the day, I see software architects and software engineers still being engaged, but in a different way than they are today. I suppose to answer your question, if I wanted to future-proof my career I'd learn modeling languages and start "moving to the left" as they say. I see being a code slinger as being less and less valuable over the coming years.

Bottom line, you don't see too many assembly language developers anymore. We largely abandoned that back in the 80s and let the computer produce the actual code that runs. I see us doing the same thing again but at a higher and more abstract level.

Except, of course, that isn't true.