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What do people even mean by "If you don't use AI, you'll fall behind"? (lemmy.world)
submitted 3 days ago by to c/asklemmy@lemmy.world
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it's getting to the point where I notice people say it a lot, especially IRL now for whatever reason recently.

And for clarity I'm not in research or anything, so these people just mean 'LLM/image gen', not utilities like OCR or (usually not) transcription.

Some have argued it's just more efficient (which I can kind of get), while others think you're actively hindering your intelligence somehow.

On the first point:

I've tried it occasionally to see how it compares to my own skill, and while it produces a functional result, it's always very derivative work to the point where you can find things with the exact same names of other 'public' (but not libre) works, and often isn't the ideal solution to what it targets. So I can see how you can get things out of it, but it never felt really that profound to me.

But for the second... isn't this supposed to be the tool for people to do things they aren't experienced in? If anything, you probably need to be able to understand how to write pertaining to the task so the token probabilities are biased toward writing from that area.

And even then, if all you end up doing is prompting AI, then wouldn't you ultimately serve no purpose outside of being glorified QA?

I guess I'm trying to figure out what exactly non-users would be 'falling behind' in that affects them more than those who use AI?

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[–] 2 points 2 days ago* (3 children)

Call me back when most of these projects actually are useful (a lot of stuff posted in various selfhost community are actually rehash of existing things, or provide no actual useful features), live more than a few months without being abandonned, and are not stagnating messes impossible to fix with issues growing faster than a magic bean.

This was true before AI. Open source projects have always had short life spans. Using AI means that you don't spent forever on a project that might only last a few weeks.

Using LLM to produce something decent requires a fair amount of work

Which is why I said you have to be careful when and where you use it. But it does boost productivity if you use it wisely.

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  • [–] 2 points 2 days ago (2 children)

    Ugh, a lot of tunnel vision in here. Good open source projects often last years or decades and are holding most of the internet alive today. They used 0 LLMs and are starting to use it today with dubious results. Many professionals ghat used it in programming reported an initial boost in production followed by an incedible boost in deal breaking bugs and higher fixing times: turns out that if you don't directly write code debugging is hell and all the gains vaporize when you need to manually verify every line again. That said, mental atrophy is also worth researching. All in all, whoever uses it in fun projects and didn't formally study code is always the most vocal about LLMs and those who did study and are forced to use it by their boss always describe it as bad. Wonder why

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  • [–] 1 point 2 days ago (1 child)

    Good open source projects often last years or decades

    This sounds like a no-true-scottsman argument. Go check github, literally a graveyard of dead open source projects, most made before AI. The harsh reality is that most projects don't gain much traction, and so the owner moves on.

    if you don't directly write code debugging is hell and all the gains vaporize

    Imo this depends on how you use it. I'd agree with you that I bet a lot of vibe coders didn't take formal classes in CS. So they probably aren't writing great code. But without AI, they probably wouldn't have any code at all. So compared to their non-AI non-CS buddies, they will feel "ahead" and might very well be, if they capitalize on their initial success correctly (like using the initial traction to get investers, and then going on to hire an actual engineer to re-write the project). Many start-ups start with an initial throwaway prototype. Not being constrained by "best practices" is great for prototypes.

    those who did study and are forced to use it by their boss always describe it as bad

    I'd add a caveat that those who are forced to use it, and don't make personal projects in their free time, describe it as bad. Part of the reason professiomals don't like it is because they are forced to use it. But when they have a choice to use it, and they actually do, then in my experience a lot of these professionals begradgingly accept that it does speed things up.

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  • [–] 1 point 1 day ago

    I mean, anectodal. I do passion projects, I studied, I used it: bad. As in muddy water bad, not unable to write or not working bad. You delegate and get complacent and it can cascade to a point you become dependent on it. That said, I specified good open source projects, not ALL. The internet is fucking built on linux. If that is not proof of what open source can achieve I don't know what to tell you. You have no idea how many drivers or libraries exist entirely open source still working and maintained today. That said, I'm not here to convince you of my OPINION, I just wanted to share my educated guess on these LLMs. Nobody knows what the heck is gonna happen in the future and the fact that management (uneducated on the topic) is forcing adoption on people that work and know their field is kind of a bad sign. New people adopting new technologies is on par with previous cases, so nothing surprising here. Will they surpass the older coders or will the new AI be too costly to compete or require more expert coders to fix the mess? We have seen all these cases so far happen, so let's wait for the big numbers to tell us who wins. Certanly not the planet lol

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