Comment by padolsey
5 days ago
Bit of a humbling/jarring moment when I realized that people are doing real paid work using LLMs that they could not otherwise do. I mean, it's quite obvious I suppose. But up until now I just assumed it was only a (massive) catalyst for things people would already be able to do with enough time. But nope -- it seems people are right now employed in roles that they would not be able to fulfil the tasks within if AI wasn't there telling them what to write/say/produce. Nobody is really going to come out and say that ... it's not something the less-AI-literate superiors would take kindly to.
I mentioned this then other day, my university class had maybe 80% of people who could not code. No joke. And a lot have gone onto to code professionally.
AI is a godsend to this cohort of code monkies.
Edit: we graduated in 2006.
At a FAANG adjacent company in 2017 we hired a guy from Google with 4 years experience who passed our very hard coding interviews. He literally couldnt code. It blew my mind.
I’m sure any profession has this issue.
My families business is bookkeeping, they hired someone who graduated with an accounting degree. They didn’t grasp the basics of bookkeeping nor accounting once employed.
You’d think Google who my understanding pride themselves on technical chops would weed this out with all the interviews they do. But maybe it’s just big places have more cracks for people slip into.
I’ve long been curious what you (and a lot of people that express this) feel the cutoff is to know how to code? Because obviously there’s a sliding scale between truly knowing very little, being able to adapt similar work, extensive modifications, coding everything from scratch, etc. on top of understanding certain programming concepts and organization.
Can you build a desktop/terminal app, web app/site, mobile app, or even a library without engaging AI is probably where I’d draw the line. Not everything you touch when you know how to code is doing the whole thing, but I’d expect you’d be able to piece it all together with some time and docs. Pick your own platform that suits too, I’m not saying iOS or Windows only.
For context to my above comment re university course. The final project we had to do was make a very rudimentary room reservation server/client on the terminal. Java, however you wanted to store the data was fine, TCP sockets were the parameters given.
It took me all of a week to finish it. Lots struggled to even begin. We were given 10 weeks.
I didn’t know much then but I read the docs and figured it out.
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You can learn to code in a few months in your free time and get good in your job. That's different from refusing to learn it, because you outsource it.
Also coding is not that hard. You kinda only write the algorithms down you make up in your head. And you do have an algorithmic understanding when you graduate. You also invented some/several languages in during your studies. The learning an actual developed language IS learning, but it's just boring learning, nothing compared to what you did for your exams.
Cool story, glad it worked out for you.
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FWIW, this is not exactly new; those same people were just using other sources like Stack Overflow, blog posts, etc. before, cobbling together random code snippets, libraries, and so on without actually understanding any of that at a relevant detail level.
Sure, with LLMs, one can naturally tailor this much closer to the current need (or at least the need one thinks they have) and iterate ("spew") faster, but it's not a new phenomenon in general.
I think this is fine as long as the success criteria are strict enough that they are forced to learn something in the end.
Any significant testing will inevitably create that situation. The LLM won't have enough context to handle the more precise business requirements. The dev will have to read the code carefully and make their changes by hand. Additional rounds of testing may cause thrashing between regressed states until something clicks for the developer. That lightbulb going off is called "learning" and they are human after all!
> I think this is fine as long as the success criteria are strict enough that they are forced to learn something in the end.
They are fired, get promoted to management, or learn the technical aspects.
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Yeh but I also mean outside of software engineering. Within the gamut of 'being a programmer' it seems fair to bleed into adjacent areas without too much cheek. We've all done it; it's part of the learning curve. But I was talking more about people in other knowledge work who have to come up with a lot of prose-like material about {insert thing}. Marketing, consultants, PMs, or even domain-specific analysts, .. ya know, the types of office jobs where people basically write emails, attend meetings, discuss reports, and produce mostly text-or-data artefacts all day long.
That sounds like fraud. You claimed you have knowledge of the job you got hired for, but you don't actually have.
If the company want's hire someone who doesn't have a clue and only uses SO, that's of course fine, but I doubt, that this is the case.
People gotta eat.
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But if the AI has a blindspot then they will fail hard since they rely on the LLM for everything.
aka there are a lot more bullshit artists around these days
i know of several engineers who produce absolute slop and who probably would have produced nothing at all in pre AI times (which would have been preferable) and probably let go or never hired (even better).
they impose such an enormous drag on productivity that they more than wipe out any gains from people using the tools responsibly.
All those programmers who could not write fizzbuzz can ship something now.