Technology

A lot of the criticism comes with AI results being wrong a lot of the time, while sounding convincingly correct. In software, things that appear to be correct but are subtly wrong leads to errors that can be difficult to decipher.

Imagine that your AI was trained on StackOverflow results. It learns from the questions as well as the answers, but the questions will often include snippets of code that just don't work.

The workflow of using AI resembles something like the relationship between a junior and senior developer. The junior/AI generates code from a spec/prompt, and then the senior/prompter inspects the code for errors. If we remove the junior from the equation to replace with AI, then entry level developer jobs are slashed, and at the same time people aren't getting the experience required to get to the senior level.

Generally speaking, programmers like to program (many do it just for fun), and many dislike review. AI removes the programming from the equation in favour of review.

Another argument would be that if I generate code that I have to take time to review and figure out what might be wrong with it, it might just be quicker and easier to write it correctly the first time

Business often doesn't understand these subtleties. There's a ton of money being shovelled into AI right now. Not only for developing new models, but for marketing AI as a solution to business problems. A greedy executive that's only looking at the bottom line and doesn't understand the solution might be eager to implement AI in order to cut jobs. Everyone suffers when jobs are eliminated this way, and the product rarely improves.

As a cybersecurity guy, it's things like this study, which said:

Overall, we find that participants who had access to an AI assistant based on OpenAI’s codex-davinci-002 model wrote significantly less secure code than those without access. Additionally, participants with access to an AI assistant were more likely to believe they wrote secure code than those without access to the AI assistant.

If you’re a seasoned developer who’s using it to boilerplate / template something and you’re confident you can go in after it and fix anything wrong with it, it’s fine.

The problem is it’s used often by beginners or people who aren’t experienced in whatever language they’re writing, to the point that they won’t even understand what’s wrong with it.

If you’re trying to learn to code or code in a new language, would you try to learn from somebody who has only half a clue what he’s doing and will confidently tell you things that are objectively wrong? Thats much worse than just learning to do it properly yourself.

The other day we were going over some SQL query with a younger colleague and I went “wait, what was the function for the length of a string in SQL Server?”, so he typed the whole question into chatgpt, which replied (extremely slowly) with some unrelated garbage.

I asked him to let me take the keyboard, typed “sql server string length” into google, saw LEN in the except from the first result, and went on to do what I'd wanted to do, while in another tab chatgpt was still spewing nonsense.

LLMs are slower, several orders of magnitude less accurate, and harder to use than existing alternatives, but they're extremely good at convincing their users that they know what they're doing and what they're talking about.

That causes the people using them to blindly copy their useless buggy code (that even if it worked and wasn't incomplete and full of bugs would be intended to solve a completely different problem, since users are incapable of properly asking what they want and LLMs would produce the wrong code most of the time even if asked properly), wasting everyone's time and learning nothing.

Not that blindly copying from stack overflow is any better, of course, but stack overflow or reddit answers come with comments and alternative answers that if you read them will go a long way to telling you whether the code you're copying will work for your particular situation or not.

LLMs give you none of that context, and are fundamentally incapable of doing the reasoning (and learning) that you'd do given different commented answers.

They'll just very convincingly tell you that their code is right, correct, and adequate to your requirements, and leave it to you (or whoever has to deal with your pull requests) to find out without any hints why it's not.

People who use LLMs to write code (incorrectly) perceived their code to be more secure than code written by expert humans.

https://arxiv.org/abs/2211.03622

• AI Code suggestions will guide you to making less secure code, not to mention often being lower quality in other ways.
• AI code is designed to look like it fits, not be correct. Sometimes it is correct. Sometimes it’s close but has small errors. Sometimes it looks right but is significantly wrong. Personally I’ve never gotten ChatGPT to write code without significant errors for more than trivially small test cases.
• You aren’t learning as much when you have ChatGPT do it for you, and what you do learn is “this is what chat gpt did and it worked last time” and not “this is what the problem is and last time this is the solution I came up with and this is why that worked”. In the second case you are far better equipped to tackle future problems, which won’t be exactly the same.

All that being said, I do think there is a place for chat GPT in simple queries like asking about syntax for a language you don’t know. But take every answer it gives you with a grain of salt. And if you can find documentation I’d trust that a lot more.

It gives a false sense of security to beginner programmers and doesn't offer a more tailored solution that a more practiced programmer might create. This can lead to a reduction in code quality and can introduce bugs and security holes over time. If you don't know the syntax of a language how do you know it didn't offer you something dangerous? I have copilot at work and the only thing I actually accept its suggestions for now are writing log statements and populating argument lists. While those both still require review they are generally faster than me typing them out. Most of the rest of what it gives me is undesired: it's either too verbose, too hard to read, or just does something else entirely.

When it comes to writing code, there is a huge difference between code that works and code that works *well." Lets say you're tasked with writing a function that takes an array of RGB values and converts them to grayscale. ChatGPT is probably going to give you two nested loops that iterate over the X and Y values, applying a grayscale transformation to each pixel. This will get the job done, but it's slow, inefficient, and generally not well-suited for production code. An experienced programmer is going to take into account possible edge cases (what if a color is out of the 0-255 bounds), apply SIMD functions and parallel algorithms, factor in memory management (do we need a new array or can we write back to the input array), etc.

ChatGPT is great for experienced programmers to get new ideas; I use it as a modern version of "rubber ducky" debugging. The problem is that corporations think that LLMs can replace experienced programmers, and that's just not true. Sure, ChatGPT can produce code that "works," but it will fail at edge cases and will generally be inefficient and slow.

If the AI was trained on code that people permitted it to be freely shared then go ahead. Taking code and ignoring the software license is largely considered a dick-move, even by people who use AI.

Some people choose a copyleft software license to ensure users have software freedom, and this AI (a math process) circumvents that. [A copyleft license makes it so that you can use the code if you agree to use the same license for the rest of the program - therefore users get the same rights you did]

That is the general reason, i use llms to help myself with everything including coding too, even though i know why it's bad

I agree AI is a godsend for non coders and amateur programmers who need a quick and dirty script. As a professional, the quality of code is oftentimes 💩 and I can write it myself in less time than it takes to describe it to an AI.

For me it's because if the AI does all the work the person "coding" won't learn anything. Thus when a problem does arise (i.e. the AI not being able to fix a simple mistake it made) no one involved has the means of fixing it.

• issues with model training sources
• business sending their whole codebase to third party (copilot etc.) instead of local models
• time gain is not that substantial in most case, as the actual "writing code" part is not the part that takes most time, thinking and checking it is
• "chatting" in natural language to describe something that have a precise spec is less efficient than just writing code for most tasks as long as you're half-competent. We've known that since customer/developer meetings have existed.
• the dev have to actually be competent enough to review the changes/output. In a way, "peer reviewing" becomes mandatory; it's long, can be fastidious, and generated code really needs to be double checked at every corner (talking from experience here; even a generated one-liner can have issues)
• some business thinking that LLM outputs are "good enough", firing/moving away people that can actually do said review, leading to more issues down the line
• actual debugging of non-trivial problems ends up sending me in a lot of directions, getting a useful output is unreliable at best
• making new things will sometimes confuse LLM, making them a time loss at best, and producing even worst code sometimes
• using code chatbot to help with common, menial tasks is irrelevant, as these tasks have already been done and sort of "optimized out" in library and reusable code. At best you could pull some of this in your own codebase, making it worst to maintain in the long term

Those are the downside I can think of on the top of my head, for having used AI coding assistance (mostly local solutions for privacy reasons). There are upsides too:

• sometimes, it does produce useful output in which I only have to edit a few parts to make it works
• local autocomplete is sometimes almost as useful as the regular contextual autocomplete
• the chatbot turning short code into longer "natural language" explanations can sometimes act as a rubber duck in aiding for debugging

Note the "sometimes". I don't have actual numbers because tracking that would be like, hell, but the times it does something actually impressive are rare enough that I still bother my coworker with it when it happens. For most of the downside, it's not even a matter of the tool becoming better, it's the usefulness to begin with that's uncertain. It does, however, come at a large cost (money, privacy in some cases, time, and apparently ecological too) that is not at all outweighed by the rare "gains".

If you're not an experienced developer, it could be used as a crutch rather than actually learning how to write the code.

The real reason? People are just fed up with AI in general (that has no real-world use to most people) being crammed down their throats and having their personal code (and other data) being used to train models for megacorps.

My objections:

1. It doesn't adequately indicate "confidence". It could return "foo" or "!foo" just as easily, and if that's one term in a nested structure, you could spend hours chasing it.
2. So many hallucinations-- inventing methods and fields from nowhere, even in an IDE where they're tagged and searchable.

Instead of writing the code now, you end up having to review and debug it, which is more work IMO.

One point that stands out to me is that when you ask it for code it will give you an isolated block of code to do what you want.

In most real world use cases though you are plugging code into larger code bases with design patterns and paradigms throughout that need to be followed.

An experienced dev can take an isolated code block that does X and refactor it into something that fits in with the current code base etc, we already do this daily with Stackoverflow.

An inexperienced dev will just take the code block and try to ram it into the existing code in the easiest way possible without thinking about if the code could use existing dependencies, if its testable etc.

So anyway I don't see a problem with the tool, it's just like using Stackoverflow, but as we have seen businesses and inexperienced devs seem to think it's more than this and can do their job for them.

I've found it to be extremely helpful in coding. Instead of trying to read huge documentation pages, I can just have a chatbot read it and tell me the answer. My coworker has been wanting to learn Powershell. Using a chatbot, his understanding of the language has greatly improved. A chatbot can not only give you the answer, but it can break down how it reached that conclusion. It can be a very useful learning tool.

We built a Durable task workflow engine to manage infrastructure and we asked a new hire to add a small feature to it.

I checked on them later and they expressed they were stuck on an aspect of the change.

I could tell the code was ChatGPT. I asked "you wrote this with ChatGPT didn't you?" And they asked how I could tell.

I explained that ChatGPT doesn't have the full context and will send you on tangents like it has here.

I gave them the docs to the engine and to the integration point and said "try using only these and ask me questions if you're stuck for more than 40min.

They went on to become a very strong contributor and no longer uses ChatGPT or copilot.

I've tried it myself and it gives me the wrong answers 90% of the time. It could be useful though. If they changed ChatGPT to find and link you docs it finds relevant I would love it but it never does even when asked.

Lots of good comments here. I think there's many reasons, but AI in general is being quite hated on. It's sad to me - pre-GPT I literally researched how AI can be used to help people be more creative and support human workflows, but our pipelines around the AI are lacking right now. As for the hate, here's a few perspectives:

• Training data is questionable/debatable ethics,
• Amateur programmers don't build up the same "code muscle memory",
• It's being treated as a sole author (generate all of this code for me) instead of like a ping-pong pair programmer,
• The time saved writing code isn't being used to review and test the code more carefully than it was before,
• The AI is being used for problem solving, where it's not ideal, as opposed to code-from-spec where it's much better,
• Non-Local AI is scraping your (often confidential) data,
• Environmental impact of the use of massive remote LLMs,
• Can be used (according to execs, anyways) to replace entry level developers,
• Devs can have too much faith in the output because they have weak code review skills compared to their code writing skills,
• New programmers can bypass their learning and get an unrealistic perspective of their understanding; this one is most egregious to me as a CS professor, where students and new programmers often think the final answer is what's important and don't see the skills they strengthen along the way to the answer.

I like coding with local LLMs and asking occasional questions to larger ones, but the code on larger code bases (with these small, local models) is often pretty non-sensical, but improves with the right approach. Provide it documented functions, examples of a strong and consistent code style, write your test cases in advance so you can verify the outputs, use it as an extension of IDE capabilities (like generating repetitive lines) rather than replacing your problem solving.

I think there is a lot of reasons to hate on it, but I think it's because the reasons to use it effectively are still being figured out.

Some of my academic colleagues still hate IDEs because tab completion, fast compilers, in-line documentation, and automated code linting (to them) means you don't really need to know anything or follow any good practices, your editor will do it all for you, so you should just use vim or notepad. It'll take time to adopt and adapt.

Two reasons:

1. my company doesn't allow it - my boss is worried about our IP getting leaked
2. I find them more work than they're worth - I'm a senior dev, and it would take longer for me to write the prompt than just write the code

I just dont know anything about bash’s syntax

That probably won't be the last time you write Bash, so do you really want to go through AI every time you need to write a Bash script? Bash syntax is pretty simple, especially if you understand the basic concept that everything is a command (i.e. syntax is <command> [arguments...]; like if <condition> where <condition> can be [ <special syntax> ] or [[ <test syntax> ]]), which explains some of the weird corners of the syntax.

AI sucks for anything that needs to be maintained. If it's a one-off, sure, use AI. But if you're writing a script others on your team will use, it's worth taking the time to actually understand what it's doing (instead of just briefly reading through the output). You never know if it'll fail on another machine if it has a different set of dependencies or something.

What negative experience do you have with AI chatbots that made you hate them?

I just find dealing with them to take more time than just doing the work myself. I've done a lot of Bash in my career (>10 years), so I can generally get 90% of the way there by just brain-dumping what I want to do and maybe looking up 1-2 commands. As such, I think it's worth it for any dev to take the time to learn their tools properly so the next time will be that much faster. If you rely on AI too much, it'll become a crutch and you'll be functionally useless w/o it.

I did an interview with a candidate who asked if they could use AI, and we allowed it. They ended up making (and missing) the same mistake twice in the same interview because they didn't seem to actually understand what the AI output. I've messed around with code chatbots, and my experience is that I generally have to spend quite a bit of time to get what I want, and then I still need to modify and debug it. Why would I do that when I can spend the same amount of time and just write the code myself? I'd understand the code better if I did it myself, which would make debugging way easier.

Anyway, I just don't find it actually helpful. It can feel helpful because it gets you from 0 to a bunch of code really quickly, but that code will probably need quite a bit of modification anyway. I'd rather just DIY and not faff about with AI.

It doesn't pass judgment. It just knows what "looks" correct. You need a trained person to discern that. It's like describing symptoms to WebMD. If you had a junior doctor using WebMD, how comfortable would you be with their assessment?

Basically this: Flying Too High: AI and Air France Flight 447

Description

Panic has erupted in the cockpit of Air France Flight 447. The pilots are convinced they’ve lost control of the plane. It’s lurching violently. Then, it begins plummeting from the sky at breakneck speed, careening towards catastrophe. The pilots are sure they’re done-for.

Only, they haven’t lost control of the aircraft at all: one simple manoeuvre could avoid disaster…

In the age of artificial intelligence, we often compare humans and computers, asking ourselves which is “better”. But is this even the right question? The case of Air France Flight 447 suggests it isn't - and that the consequences of asking the wrong question are disastrous.

Personally, I've found AI is wrong about 80% of the time for questions I ask it.

It's essentially just a search engine with cleverbot. If the problem you're dealing with is esoteric and therefore not easily searchable, AI won't fare any better.

I think AI would be a lot more useful if it gave a percentage indicating how confident it is in its answers, too. It's very useless to have it constantly give wrong information as though it is correct.

Many lazy programmers may just copy paste without thinking too much about the quality of generated code. The other group of person who oppose it are those who think it will kill the programmer job

I use ai, but whenever I do I have to modify it, whether it's because it gives me errors, is slow, doesn't fit my current implementation or is going off the wrong foot.

It's a tool just like everything else, but people are just now sobering up after all the hype that it's incredibly wrong a lot.

Sounds like it's just another tool in a coding arsenal! As long as you take care to verify things like you did, I can't see why it'd be a bad idea. It's when you blindly trust that things go wrong.

A lot of people are very reactionary when it comes to LLMs and any of the other "AI" technologies.

For myself, I definitely roll my eyes at some of the "let's shoehorn 'AI' into this!" marketing, and I definitely have reservations about some datasets stealing/profiting from user data, and part of me worries about the other knock-on effects of AI (e.g. recently it was found that some foraging books on Amazon were AI generated and, if followed, would've led to people being poisoned. That's pretty fucking bad).

...but it can also be a great tool, too. My sister is blind, and honestly, AI-assisted screen readers will be a game changer. AI describing images online that haven't been properly tagged for blind people (most of them, btw!) is huge too. This is a thing that is making my little sister's life better in a massive way.

It's been useful for me in terms of translation (Google translate is bad), in terms of making templates that take a lot of the tedious legwork out of programming, effortlessly clearing up some audio clarity issues for some voluntary voice acting "work" I've done for a huge game mod, and for quickly spotting programming or grammar mistakes that a human could easily miss.

I wish people could just have rational, adult discussions about AI tech without it just descending into some kind of almost religious shouting match.

I have a coworker who is essentially building a custom program in Sheets using AppScript, and has been using CGPT/Gemini the whole way.

While this person has a basic grasp of the fundamentals, there's a lot of missing information that gets filled in by the bots. Ultimately after enough fiddling, it will spit out usable code that works how it's supposed to, but honestly it ends up taking significantly longer to guide the bot into making just the right solution for a given problem. Not to mention the code is just a mess - even though it works there's no real consistency since it's built across prompts.

I'm confident that in this case and likely in plenty of other cases like it, the amount of time it takes to learn how to ask the bot the right questions in totality would be better spent just reading the documentation for whatever language is being used. At that point it might be worth it to spit out simple code that can be easily debugged.

Ultimately, it just feels like you're offloading complexity from one layer to the next, and in so doing quickly acquiring tech debt.