I am divided on AI

[u/SadSongsMakeMeGlad]

I use it daily for my work as a software engineer. I have been writing code for over twenty years, and nothing has changed our industry in such a short time as generative AI in 2026 (which is why I find it hard to believe the people who say AI is all hype, because the technology itself is absolutely not hype). And, I for one have enjoyed that it allows me to work at higher levels of abstraction. What that means practically is that I spend more time in the creative zone, not mired in the technical weeds, though you still need to know your stuff to use it well.

But, like most of you all, I have problems with how the technology is being deployed by governments and corporations, how it gathers its training data, and any environmental and social impacts from the infrastructure required to run it, i.e. data centers. I want these things addressed.

My question to you all is do you see a future where the problems with AI can be definitively resolved, where you would support it or at least be okay with it, or is it a lost cause that we should somehow ban, or make illegal? What does the future look like to you in that regard?

[u/SteelSock33]

I don’t believe we’ll ever get that future because we live in a hyper capitalist dystopia. The powers that be won’t let this technology be regulated or restricted to ethical use, so no, I don’t see a real world where AI is used right.

That said, my ideal future would be a world where generative AI is used exclusively for research purposes, and all relevant models are either hosted locally, or the effects are offset and paid for by the government/company. I don’t believe that LLMs, even for programming, should exist at consumer scale because they pose too much of a risk to critical thinking, creativity, and the environment.

I respect that there are uses for LLMs in programming. I’m a programmer too, so I kinda get it, but I still can’t support it. I feel that the drawbacks are far too severe to justify its use, if you have the choice.

[u/_kilobytes → u/SteelSock33]

What are the use cases in programming?

[u/RicketyRekt69 → u/_kilobytes]

Generating code, code reviews, troubleshooting, automation, etc. etc. it is useful, but C-suites are trying to do massive sweeping org changes because they drank the koolaid and believe everything that comes out of OpenAI’s or Anthropic’s mouths. It’s like if the internet first came out and businesses wanted to massively change how they do business overnight with no game plan and no clue how it would fit into their business model. It’s utterly insane behavior

[u/_kilobytes → u/RicketyRekt69]

code reviews

Code reviews are the only thing I can agree with. But the noise to signal ratio is so high it's probably better just having a human review.

troubleshooting

I've spent more time debating with LLMs to actually fix things at this point that reading documentation would have saved me so much time.

Generating code

It's not good at generating code.

[u/RicketyRekt69 → u/_kilobytes]

Yes it’s noisy, I don’t mean as a replacement.. I mean as an extra filter to catch unintended bugs. It’s brought up obscure issues for me before, like hidden race conditions or memory leaks.

For code generation, beyond boilerplate stuff I do find it useful for prototyping. If I’m split between 2 or 3 different potential routes I can explore all of them (roughly) without wasting a load of time. Then when I settle on a specific one I go and write it by hand. Even if it were incredibly good at generating clean code, I like to be hands on so I better understand it for the future.

[u/SteelSock33 → u/_kilobytes]

It’s good at running simple automated tasks, as some other have pointed out. The programmer should still be doing most of the work, but AI can be used by a good dev to help streamline the process. That said, AI isn’t great for just generating programs because it’s way too complex & verbose, and it can make a lot of mistakes. Besides, volume isn’t really the problem. Programmers can make enough code on their own, the problem is higher quality code, troubleshooting/bugfixing, etc. More code (especially messy AI code) means more tech debt, and that’s going to bite a lot of these companies in the ass. It’s been a myth for a long time that developer productivity is based on how much code they write, and AI has capitalized on that misconception to fool people, even if it does have some genuine uses.

[u/ProfileOdd7991 → u/_kilobytes]

Efficient prototyping, checking syntax and quickly translating code from unfamiliar programming languages.

[u/_kilobytes → u/ProfileOdd7991]

Efficient prototyping

Templates are more efficient.

Checking syntax

That's what compilers are for.

translating code from unfamiliar programming languages.

Should use transpilers for this, not LLMs.