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

Memorizing a solution to a classic brainteaser is not the same as having the reasoning skills needed to solve it. Finding out separate solutions for related problems might allow someone to pattern-match, but not to understand. This is about as true for humans as for LLMs. Lots of people ace their courses, even at university level, while being left with questions that demonstrate a stunning lack of comprehension.

Now this assumes that LLMs plateau around their current scores. While open models are catching up to closed ones (like Open AI), we are still to see a real jump in consciousness compared to GPT-4. That, and operating LLMs is too damn expensive. If you have explored bolt.new for a little while, you'll find out quick enough that a developer becomes cheaper as your code base gets larger.

The way I see it

1. LLM do not plateau and are fully capable of replacing software developers: There is nothing I can or most of us can do about this. Most people hate software developers and the process of software development itself. They'd be very happy to trade us in an instant. Pretty much all software developers are screwed in the next 3-4 years but it's only a matter of time before it hits any other desk field (management, design, finance, marketing, etc...). According to history, we get a world war (especially if these LLMs are open in the wild) and one can only hope he is safe.

2. LLMs plateau around current levels. They are very useful as a power booster but they can also produce lots of garbage (both in text and in code). There will be an adjustment time but software developers will still be needed. Probably in the next 2-3 years when everyone realizes the dead end, they'll stop pouring money into compute and business will be back as usual.

tl;dr: current tech is not enough to replace us. If tech becomes good enough to replace us, there is nothing that can be done about it.

Yes. We should conclude that syntax highlighting is not useful in languages that the syntax highlighter does not support. I think basically everyone would agree with this statement.

Similarly an llm that worked 100% of the time and could solve any problem would be pretty useful. (Or at least worked correctly as often as syntax highlighting in situations where it is actually used does)

However that's not the world we live in. Its a reasonable question to ask if llm is good enough yet where the productivity gain outweighs the productivity lost.

They produce mostly working code, often with odd design decisions that the bot can't fully justify.

The difficult part of coding is cultivating a mental model of the problem + solution space. When you let an LLM write code for you, your mental model falls behind. You can read the new code closely, internalize it, double-check the docs, and keep your mental model up to date (which takes longer than you think), or you can forge ahead, confident that it all more or less looks right. The second option is easier, faster, and very tempting, and it is the reason why various studies have found that code written with LLM assistance introduces more bugs than code written without.

There are plenty of innovations that have made programming a little bit faster (autocomplete, garbage collection, what-have-you), but none of them were a silver bullet. LLMs aren't either. The essential complexity of the work hasn't changed, and a chat bot can't manage it for you. In my (admittedly limited) experience with code assistants, I've found that the faster you move with an LLM, the more time you have to spend debugging afterwards, and the more difficult that process becomes.