alt Hacker News> That sounds great until you realize that "hey Claude, read this Jira ticket and implement the feature in this code base" isn't really worth $200K/year.
Nor even €50k/year.
Two things are true:
1) The coding part of my career is over. LLMs are capable of doing everything I've ever been paid to *write*.
2) My actual job also included non-coding work: Does this attempted solution even work at all? Is this solving the right problem? Even if it's a valid solution to the right problem, is it the best solution given the time constraints?
That last one, "given the time constraints", is a place where my experience is still useful. The AI is as lazy (or as optimised for fast wins) as the humans whose examples it was trained upon; but an LLM costs so little that the answer is always "do it right" rather than "do it fast". A lot of people don't know what "do it right" even looks like, having only ever known e.g. websites that take 5 seconds to load because of all the adverts and analytics, and never the world where machines with 1% of the CPU and no GPU at all could fit a fully playable first person shooter in the same memory footprint as that page.
At least, I hope this "is a place where my experience is still useful"; I may just be telling myself a nice story, same as all the other people through history who have found themselves obsoleted when the automation came for them.
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Before, if I would start working / thinking on a problem and discover it was harder than anticipated, that often was a signal that the feature may not be worth it. Because implementing it doesn't fit the current model of how things work or similar. Then it was back to the drawing table and find a better way of solving the underlying issue.
But an LLM just happily chugs along and does it, no one feels the friction, which means you never stop up to think if you're solving the right problem, or in the right way. I'm already seeing this bite us in the ass, as you get hacks upon hacks.
>My actual job also included non-coding work: Does this attempted solution even work at all? Is this solving the right problem? Even if it's a valid solution to the right problem, is it the best solution given the time constraints?
This is not non-coding work. This is coding.
I feel like there is also the "Nerd Factor" to consider which is this unrelenting passion to type away at a screen all day, whether that is yesterday's code or tomorrow's prompt. Few non-technical people have the attention span to suffer such monotony. (Even most devs don't have that to varying extents)
FWIW: The highest paid devs I know excel at 2, and aren't even that good at 1.
> The coding part of my career is over. LLMs are capable of doing everything I've ever been paid to write.
That is certainly not true. LLMs can't write code worth a damn still, and you have to babysit them to make sure they aren't doing stupid stuff. A human is still by far the best choice for doing programming, and when trend-chasing companies realize the damage they've done to their businesses they will stop pushing LLMs so hard.
This I solemnly swear. To take my paycheck with gratitude, even if my employer makes three times as much based on my output. To take whatever technological disruption that graces my path as an immaterial and impersonal force. To politely and discreetely bow out when the statistics soup trained on my fellow colleagues makes me unemployable and worthless. Amen.
staying within free tiers has led to some really great optimizations for me
took complex pub/sub event architectures down to the events postgres can do on its own, and a system architecture that works with that payload. am aware of the limits with this approach and stress potential on db
Ai can work through all of those questions in number 2 and design around it.
I was just doing that yesterday designing a game demo lol
> The coding part of my career is over. LLMs are capable of doing everything I've ever been paid to write.
Remember we used to spend enormous amount of time in school and in our spare time studying computer science? Algorithms, operating systems, compilers, and etc. All kinds of insights. All kinds of fun. All kinds of hard engineering. Yet, how much time do we really need to spend in our day-to-day work implementing or using the algorithms and etc that we have learned?
Engineers have done amazing work of abstracting away the hard algorithms and data structures. In the meantime, there has been little progress or few new fields in the past 10 years or so in business that ask for implementation of new algorithms. In contrast, getting LLM to work is a new field, so it requires tons of new implementations: KV caches, speculative decoding, all kinds of variants of attention like FlashAttention, all kinds of parallel processing techniques, RL pipelines, post-training pipelines, and etc. It's just that the field is so concentrated that only luck few get to work on them.
So, maybe it's not LLM per se that removes the need of writing code. It is the maturity of the software engineering that has done so. It's just that LLM fills the last gap: making knowledge transfer so much faster and cheaper - if all that's left for most of us is slicing and dicing of what has been already been implemented, then LLM can reliably take over.