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?
I’m in software engineering. AI is pretty far along in my field, and also very suited for it. I’ve spent two decades writing everything from very low-level to high-level code. I enjoy it, but it’s just labor at this point. What LLMs allows me to do is move to the role of systems architect. I have an endless supply of tireless jr engineers to execute my designs.
I get quick results. But I’m not as in touch with the codebase as if I had written it myself. You can do things wrong, and you can do things right. I like to compare it with a chainsaw. You get the job done a lot faster than with a regular saw, but you can fuck up a lot harder, too.
The field is moving fast. In the last two years it’s gone from helpful auto-complete to finishing projects on its own.
I expect to be downvoted.
I’m also in software engineering. I spent some time thinking like you, then some time trying to find a middle ground between using AI like that and doing everything myself.
Now I do it all without AI and am slowly purging all of the rituals around LLM usage from my brain. I’m trying to reclaim that space for useful knowledge in the languages I like to work with.
The reasons are numerous, some ethical, some practical, and some personal.
Ethical:
Practical
Personal
On that last point: I knew I had to stop entirely - not just limit my usage or restrict myself to local models - when I realized part of my brain really didn’t want me to. I had to have an argument with myself in order to do anything by hand, and I like coding by hand. I enjoy the puzzle-solving. AI was replacing my main source of enjoyment in my job.
And it wasn’t even doing that part well, it was just doing it faster. I am a better coder than any LLM, and that’s not a brag, because I know there are tons of developers out there better than me. Faster doesn’t equal better.
So that’s where I’m at today.
Thanks for the great write up. Especially the ethical reasons are so often ignored, overlooked, cast aside with a smirk.
My pleasure. The ethical problems with LLMs are my top concern, and I can only conclude that many people in my industry are too addicted to them now to internalize those problems.
My experience has been that when you educate people on the background and non ethical training on AI, you get a blank stare.
Either the scope is too big or they do not really believe the implications. I don’t know. I wish there was a short layman relatable and believable explanation.
I mean, you’re not wrong. You’re only saying facts. I’m a strong AI opponent, but these things you’re saying are simply true. 🤷♂️ That said, I like coding and doing that bit myself, so I’ll keep coding. 😅
My concern is that the current models have been trained on current technology by stealing knowledge from the existing pool of senior and junior engineers as well as a whole load of hobbyists.
If everyone moves up to senior now using LLMs, that’s all well and good now, but then there’ll be no juniors to steal the grunt work from to train AI and no-one to move up to senior in a decade or two.
Do you really think that the hobbyists are going to be enough to steal from to perpetuate the system in the near future?
It also means that when some new technology comes out there won’t be a wealth of knowledge for the AI to consume and regurgitate, so the use of new technologies will be hindered if everyone relies on AI trained on current technology.
I had something about stagnation in the comment at one point, but I guess my attempt to make it implicit didn’t quite survive the edit.
I read yours to be about stalling with current tech, which is certainly the case. I was trying to make it explicit that the stagnation is an issue for both current and new technologies that doesn’t have anything for AI to be trained on.
Reworded to make that clearer.
That’s my concern too. We’re already seeing middle school and high school kids out sourcing all their thinking to AI. Once this generation of devs is gone, who’s going to replace them? I’m sure there will be SOME people who find a fascination with learning and knowing and trying code on their own. But that won’t be enough people.
You act like it is their code …
The majority of coders “steal” code from other coders on Stack Overflow. This has been happening since coding was invented.
The thing is that it’s apparently expected that everyone use AI.
With StackOverflow it was less than the full complement of developers who went there for help, so there was always someone to provide answers.
AI will kill StackOverflow and places like it. AI will create a dearth of new developers, and those that still choose the profession will be AI rotted and unable to move up or advance. They won’t post anywhere on the Internet, and even if they do, what they know was derived from StackOverflow before it stopped being updated. That’s not even a complete cover of tech now, let alone several years down the line when tech has moved on.
This whole thing is cashing in an investment and in 20 years we’re going to be confused about why that investment is no longer accruing interest.
We’ve already seen models degrading from being trained on LLM-generated data. The big companies were overly eager to follow the EU directive to watermark output. I think that’s solely so that they can filter it out when scraping for future training data. They’re running out of good training data to steal. I don’t imagine they’ll be paying a fair wage to anyone generating novel good data.
Entry jobs disappearing is uncharted territory. My latest hire had three years work experience in a very niche field. I’d have a hard time justifying anyone more jr than that for a startup burning investor capital.
I like the chainsaw analogy but I think you need to apply it more like, you’re trying to carve something out of a tree stump and you only have a chainsaw nothing else. So you can make it look good. But the fine little detail work isn’t as possible. You have you go back in with other tools to do fine detail work.
And like a chainsaw, without practice, you have no idea what you’re doing at first. If you vibe code something without coding experience, a lot can go wrong but if you don’t understand how it works, you can’t fix it.
I heard an analogy about an electric screwdriver, too. It makes the job go faster, but you still need to know what kind of screws to use and where to place them.
But yes, it can be faster. And you can dig yourself into a hole faster if you let your agent take lead.