As a new career I'd probably not choose SWE now. But if you've done 10 years already I'd ride it out, there is a good chance most of us will remain employed for many years to come.
As a new career I'd probably not choose SWE now. But if you've done 10 years already I'd ride it out, there is a good chance most of us will remain employed for many years to come.
However rationally I can see where these models are evolving, and it leads me to think the software industry is on its own here at least in the short/medium term. Code and math, and with math you typically need to know enough about the domain know what abstract concept to ask, so that just leaves coding and software development. Even for non technical people they understand the result they want of code.
You can see it in this announcement - it's all about "code, code, code" and how good they are in "code". This is not by accident. The models are becoming more specialised and the techniques used to improve them beyond standard LLM's are not as general to a wide variety of domains.
We engineers think AI automation is about difficulty and intelligence, but that's only partly true. Its also about whether the engineer has the knowledge on what they want to automate, the training data is accessible and vast, and they even know WHAT data is applicable. This combination of both deep domain skills and AI expertise is actually quite rare which is why every AI CEO wants others to go "vertical" - they want others to do that leg work on their platforms. Even if it eventuates it is rare enough that, if they automate, will automate a LOT slower not at the deltas of a new model every few months.
We don't need AGI/ASI to impact the software industry; in my opinion we just need well targeted models that get better at a decent rate. At some point they either hit a wall or surpass people - time will tell BUT they are definitely targeting SWE's at this point.
This is what I mean by generalization skills. You need trillions of lines of code to RL a model into a good SWE right now, but as the models grow more capable you will probably need less and less. Eventually we may hit the point where a large corporations internal data in any department is enough to RL into competence, and then it frankly doesn't matter for any field once individual conglomerates can start the flywheel.
This isn't an absurdity. Man can "RL" itself into competence in a single semester of material, a laughably small amount of training data compared to an LLM.
I think the best period of Software Devs will be gone in few years. Knowing how how to code and fix things will be important still but more important to be also Jack-of-Many-Trades to provide more value: know a little about SEO, have a good taste of design and be able to tweak simple design, good taste how to organise code, better soft skills and managing or educating less tech-savvy stuff.
Another option is to specialise in some currently difficult subfield: robotics, ML, CUDA, rust and try to be this elite dev with expectation would have to move to SV or any such tech hub.
Best general recommendation I would give right now (especially for someone who is not from US) to someone who is currently studying is to use that a lot of time you have right now with not much responsibility to make some product that can provide you semi-passive income on a monthly basis ($5k-$10k) to drag yourself out of this rat race. Even if you not succeed or revenue stream will run out eventually you will learn those other skills that will be more important later if wanna be employed (SEO, code & design taste, marketing, soft skills).
Because most likely this window of opportunity might be only for the next few years in similar way when the best window for Mobile Apps was first ~2 years when App Store started
The real answer is either to pivot to a domain where the computer use/coding skills are secondary (i.e. you need the knowledge but it isn't primary to the role) or move to an industry which isn't very exposed to AI either due to natural protections (e.g. trades) or artifical ones (e.g regulation/oligopolies colluding to prevent knowledge leaking to AI). May not be a popular comment on this platform - I would love to be wrong.
You assume nothing LLMs do are actually generalization. Once Field X is eaten the labs will pivot and use the generalization skills developed to blow out Field Y to make the next earnings report. I think at this current 10x/yr capability curve (Read: 2 years -> 100x 4 years -> 10000x) I'll get screwed no matter what is chosen. Especially the ones in proximity to computing, which makes anything in which coding is secondary fruitless. Regulation is a paper wall and oligopolies will want to optimize as much as any firm. Trades are already saturating.
This is why I feel completely numb about this, I seriously think there is nothing I can do now. I just chose wrong because I was interested in the wrong thing.
For people who aren't in SV for whatever reason and haven't seen the really high pay associated with being there - SWE is just a standard job often stressful with lots of learning required ongoing. The pain/anxiety of being disrupted is even higher then since having high disposable income to invest/save would of been less likely. Software to them would of been a job with comparable pay's to other jobs in the area; often requiring you to be degree qualified as well - anecdotally many I know got into it for the love; not the money.
Who would of thought the first job being automated by AI would be software itself? Not labor, or self driving cars. Other industries either seem to have hit dead ends, or had other barriers (regulation, closed knowledge, etc) that make it harder to do. SWE's have set an example to other industries - don't let AI in or keep it in-house as long as possible. Be closed source in other words. Seems ironic in hindsight.
Smaller teams and more bootstrapping (as opposed to venture-funded rapid growth) seems likely to reduce the reliance on recruiters who are a plague on the industry, with few exceptions.
On the other hand, maybe a lot of tasks you could previously get a lot of leverage on with a simple Perl script will just be done directly by LLMs. Not if they're customer-facing, maybe, but in cases where you just need to get some data in a different format or something.
People who know both coding and LLMs will be a whole lot more attractive to hire to build software than people who just know LLMs for many years to come.
[0] https://x.com/karpathy/status/1886192184808149383