It takes jobs because executives push it hoping to save six figures per replaced employee, not because it’s actually better. The downsides of AI-written code (that it turns a codebase into an unmaintainable mess whose own “authors” won’t have a solid mental model of it since they didn’t actually write it) won’t show up immediately, only when something breaks or needs to be changed.

It’s like outsourcing - it looks promising and you think you’ll save a ton of money, until months or years later when the tech debt comes due and nobody in the company knows how to fix it. Even if the code was absolutely flawless, you still need to know it to maintain it.

To many of life’s either-or questions, we often struggle when the answer is: yes. That is to say, two things can hold true at the same time: 1) LLMs can result in job redundancies, and 2) LLMs hallucinate results.

But if we just stopped the analysis there, we wouldn’t have learned anything. To use this reality to terminate any additional critical thinking is, IMO, wholly inappropriate for solving modern challenges, and so we must look into the exact contours of how true these statements are.

To wit, LLM-induced job redundancies could come from skills which have been displayed by the things LLMs can do well. For example, typists lost their jobs when businesspeople were expected to operate a typewriter in their own. And when word processing software came into existence for the personal computer, a lot of typewriter companies folded or were consolidated. In the case of LLMs, consider that people do use them to proofread letters for spelling and grammar.

Technologically, we’ve had spell-check software for a while, but grammar was harder. In turn, an industry appeared somewhere in the late 2000s or early 2010s to develop grammar software. Imagine how the software devs at these companies (eg Grammarly) might be in a precarious situation, if an LLM can do the same work. At least with grammar checking, even the best grammar software still struggles with some of the more English sentence constructions, so if an LLM isn’t 100% perfect, that’s still acceptable. I can absolutely see the fortunes of grammar software companies suffering due to LLMs, and that means those software devs are indeed threatened by what LLMs can do.

For the second statement, it is trivial to find examples of LLMs hallucinating, sometimes spectacularly or seemingly ironic (although an LLM would be hard-pressed to simulate the intention of irony, I would think). In some fields, such hallucinations are career-limiting moves for the user, such as if an LLM was used to advise on pharmaceutical dosage, or used to draft a bogus legal appeal and the judge is not amused. This is very much a FAFO situation, where somehow the AI/LLM companies burden none of the risk and all of the upside. It’s like how autonomous driving automotive companies are somehow allowed to do public road tests of their beta-quality designs, but the liability for crashes still befalls the poor sod behind the wheel. They just keep yapping about how those crashes are all “human error” and “an autonomous car is still safer”.

But I digress.

My point is that LLMs have quite a lot of capabilities, and people make a serious mistake when they assume its incompetence in one capacity reflects its competency in another. This is not unlike how humans assess other humans, such as how a record-setting F1 driver would probably be a very good chauffeur for a limousine company. But whereas humans have patterns that suggest they might be good (or bad) at something, LLMs are a creature unlike anything else.

I personally am not bullish on additional LLM improvements, and think the next big push will require additional academic research, being nowhere near commercialization. But even I have to recognize that some very specific tasks are decent using an LLM. I just don’t think that’s good enough for me to use them, given their subscription costs, the possibility of becoming dependent, and being too niche.

AI hallucinates constantly, that’s why you still have a job - someone has to know what they’re doing to sort out the wheat from the chaff.

It’s also taking a ton of our entry-level jobs, because you can do the work you used to do and the work of the junior devs you used to have without breaking a sweat.

Based on my own experience of using Claude for AI coding, and using the Whisper model on my phone for dictation, for the most part AI tools can be very useful. Yet there is nearly always mistakes, even if they are quite minor at times, which is why I am sceptical of AI taking my job.

Perhaps the biggest reason AI won’t take my job is it has no accountability. For example, if an AI coding tool introduces a major bug into the codebase, I doubt you’d be able to make OpenAI or Anthropic accountable. However if you have a human developer supervising it, that person is very much accountable. This is something that Cory Doctorow talks about in his reverse-centaur article.

“And if the AI misses a tumor, this will be the human radiologist’s fault, because they are the ‘human in the loop.’ It’s their signature on the diagnosis.”

This is a reverse centaur, and it’s a specific kind of reverse-centaur: it’s what Dan Davies calls an “accountability sink.” The radiologist’s job isn’t really to oversee the AI’s work, it’s to take the blame for the AI’s mistakes.

Oh you mean like how AI for driving changed cars so nobody drives themselves any more?

AI/LLM for coding assistance is the shit for grunt work and beyond. It is excellent at summaries, information retrieval, etc. Logic and reasoning have strides to make yet. “Generarive AI” for images, video, voice work, plenty of valid reasons to hate AI there. Not enough individuals differentiate the two, it’s easier to blanket the advent and just bandwagon on “AI bad”.

Since I deal with this first hand with clients I will tell you it doesn’t have to be good to be embraced. as far as the managers and CEOs know, they don’t know. LLMs with vibe coders CAN and routinely DO produce something now if that something is good and works is another thing and in most cases it doesn’t work in the long term.

Managers and up only see the short term and in the short term vibe coding and LLMs work. in the long term they don’t. they break, they don’t scale, they’re full of exploits. But short term? saving money in the short term? that’s all they care about right now until they don’t.

The key is how you use LLMs and which LLMs you use for what.

If you know how to make use of them properly, know their strengths, weaknesses, and limitations, LLMs are an incredibly useful tool that sucks up productivity from other people (and their jobs) and focus productivity on you, so to speak.

If you do not know how to make use of them – then yes, they suck. For you.

It’s not really that much different from any other tool. Know how to use version control? If not it does not make you a bad dev per se. If yes, it probably makes you a bit more organized.

Same with IDEs, using search engines, being able to read documentation properly. All of that is not required but knowing how to make use of such tools and having the skills add up.

Same with LLMs.

I’m perplexed as to why there’s so much advertising and pushing for AI. If it was so good it would sell itself. Instead it’s just sort of a bit shit. Not completely useless but in need of babysitting.

If I ask it to do something there’s about a 30% chance that it made up the method/specifics of an API call based on lots of other similar things. No, .toxml() doesn’t exist for this object. No, I know that .toXml() exists but it works differently from other libraries.

I can make it just about muddle through but mostly I find it handy for time intensive grunt work (convert this variable to the format used by another language, add another argparser argument for the function’s new argument, etc..).

It’s just a bit naff. It cannot be relied on to deliver consistent results and if a computer can’t be consistent then what bloody good is it?

that’s you doing exactly the thing told in the ‘reverse centaur’ stage talk?

It’s because the “AI bad” crowd on Lemmy are dumb and annoying, but they are vocal. AI is an incredibly useful tool. As a senior dev I also use the shit out of LLMs to speed up my work. They do not hallucinate, at least during coding sessions. And I believe they will take junior level front end developers out of the market. Senior developers, who know how to review the code that is generated, will be safe.

I think hallucination happens most often if context or knowledge is missing. I have seen coding assistants write code that made no sense, I then helped them along go get back on the right path by providing context.

I also extensively use AI to code in a similar way you do (tbf I am, to this day, not sure if I am actually faster and how it affects my ability to code).

Overall I think the answer is somewhere in the middle, they hallucinate and need some help when they do. But with proper context they work quite well.

Both are true.
1. Yes, they hallucinate. For coding, especially when they don’t have the latest documentation, they just invent APIs and methods that don’t exist.
2. They also take jobs. They pretty much eliminate entry-level programmers (making the same mistakes while being cheaper and faster).
3. AI-generated code bases are not maintainable in the long run. They don’t reliably reuse methods, only fix the surface bugs, not fundamental problems, causing code base bloating and, as we all know, more code == more bugs.
4. Management uses Claude code for their small projects and is convinced that it can replace all programmers for all projects, which is a bias they don’t recognize.

Is it a bubble? Yes. Is it a fluke? Welllllllll, not entirely. It does increase productivity, given enough training, learning its advantages and limitations.

Its the typical “the enemy is both weak and strong” contradiction, which nazi propaganda often ran into since their ideology was unscientific and illogical

both are true.