Submitted 3 weeks ago by SparroHawc@lemmy.zip to technology@lemmy.world
https://futurism.com/artificial-intelligence/new-findings-ai-coding-overhyped
“No Duh,” say senior developers everywhere.
The article explains that vibe code often is close, but not quite, functional, requiring developers to go in and find where the problems are - resulting in a net slowdown of development rather than productivity gains.
I mean… At best it’s a stack overflow/google replacement.
There’s some real perks to using AI to code - it helps a ton with templated repetitive code and setting up the tedious tasks. I hate doing that by hand, being able to pass it off to copilot is great. But we already had tools that gave us 90% of the functionality copilot adds there, so it’s not super novel, and I’ve never had it handle anything properly complicated at all successfully (asking GPT-5 to do your dynamic SQL calls is inviting disaster, for example…)
But we already had tools that gave us 90%
More reliable ones.
Fair, I’ve used it recently to translate a translations.ts file to Spanish.
But for repetitive code, I feel like it is kind of a slow down sometimes. I should have refactored instead.
Similarly I find it very useful for if I’ve written a tool script and really don’t want to write the command line interface for it.
“Here’s a well-documented function - write an argparser for it”
…then I fix its rubbish assumptions and mistakes. It’s probably not drastically quicker but it doesn’t require as much effort from me, meaning I can go harder on the actual function (rather than keeping some effort in reserve to get over the final hump).
I’ve had plenty of success using it to build things like docker compose yamls and the like, but for anything functional, it does often take a few tries to get it right. I never use its raw for anything in production. Only as a leaping off point to structure things.
(asking GPT-5 to do your dynamic SQL calls is inviting disaster, for example. Requires hours of reworking just to get close.)
Maybe it’s the dynamic SQL calls themselves that are inviting disaster?
For the missing 10% : the folder with copies of the code you have already wrote doing that.
So much of the AI hype has been pointing to ten year old technology repackaged in a slick new interface.
AI is the iPod to the Zune of yesteryear.
I found that it only does well if the task is already well covered by the usual sources. Ask for anything novel and it shits the bed.
That's because it doesn't understand anything and is just vomiting forth output based on the code that wad fed into it.
At absolute best.
My experience is it’s the bottom stack overflow answers. Making up bullshit and nonexistent commands, etc.
They should make it more like SO and have it chastise you for asking a stupid question you should already know the answer to lol
If you know what you want, its automatic code completion can save you some typing in those cases where it gets it right (for repetitive or trivial code that doesn’t require much thought). It’s useful if you use it sparingly and can see through its bullshit.
For junior coders, though, it could be absolute poison.
In the beginning there were manufacturer’s manuals, spec sheets, etc.
Then there were magazines, like Byte, InfoWorld, Compute! that showed you a bit more than just the specs
Then there were books, including the X for Dummies series that purported to
Then there was Google / Stack Overflow and friends
Somewhere along there, where depends a lot on your age, there were school / University courses
Now we have “AI mode”
Each step along that road has offered a significant speedup, connecting ideas to theory to practice.
I agree, all the “magic bullet” AI hype is far overblown. However, with AI something I new I can do is, interactively, develop a specification and a program. Throw out the code several times while the spec gets refined, re-implemented, tried in different languages with different libraries. It’s still only good for “small” projects, but less than a year ago “small” meant less than 1000 lines of code. These days I’m seeing 300 lines of specification turn into 1500-3000 lines of code and have it running successfully within half a day.
I don’t know if we’re going to face a Kurzweilian singularity where these things start improving themselves at exponential rates, or if we’ll hit another 30 year plateau like neural nets did back in the 1990s… As things are, Claude helps me make small projects several times faster than I could ever do with Google and Stack Overflow. And you can build significant systems out of cooperating small projects.
At least when I’m cleaning up after shit devs who used Stack Overflow, I can usually search using a fragment of their code and find where they swiped it from and get some clue what the hell they were thinking. Now that they’re all using AI chatbots, there’s no trace.
“No Duh,” say senior developers everywhere.
I’m so glad this was your first line in the post
No duh, says a layman who never wrote code in his life.
And many between “seniour developers everywhere” and “a layman who never wrote code in his life”.
Like me, I’m saying it too. A big ol “No duh”.
Disbelieve the hype.
If not to editorialize, what else is the text box for? :)
You mean relying blindly on a statistical prediction engine to produce sophisticated software without any understanding of the underlying principles or concepts doesn’t magically replace years of actual study and real-world experience?
But trust me, bro, the singularity is imminent, LLMs are the future of human evolution, true AGI is nigh!
I can’t wait for this idiotic “AI” bubble to burst.
AI coding is the stupidest thing I’ve seen since someone decided it was a good idea to measure the code by the amount of lines written.
More code is better, obviously! Why else would a website to see a restaurant menu be 80Mb? It’s all that good, excellent code.
It did solve my impostor syndrome though. Turns out a bunch of people I saw to be my betters were faking it all along.
The most immediately understandable example I heard of this was from a senior developer who pointed out that LLM generated code will build a different code block every time it has to do the same thing. So if that function fails, you have to look at multiple incarnations of the same function, rather than saying “oh, let’s fix that function in the library we built.”
Yeah, code bloat with LLMs is fucking monstrous. If you use them, get used to immediately scouring your code for duplications.
Yeah if I use it and it generatse more than 5 lines of code, now I just immediately cancel it out because I know it’s not worth even reading. So bad at repeating itself and falling to reasonably break things down in logical pieces…
With that I only have to read some of it’s suggestions, still throw out probably 80% entirely, and fix up another 15%, and actually use 5% without modification.
Might be there someday, but right now it’s basically a substitute for me googling some shit.
If I let it go ham, and code everything, it mutates into insanity in a very short period of time.
I’m honestly doubting it will get there someday, at least with the current use of LLMs. There just isn’t true comprehension in them, no space for consideration in any novel dimension. If it takes incredible resources for companies to achieve sometimes-kinda-not-dogshit, I think we might need a new paradigm.
A crazy number of devs weren’t even using EXISTING code assistant tooling.
Enterprise grade IDEs already had tons of tooling to generate classes and perform refactoring in a sane and algorithmic way. In a way that was deterministic.
So many use cases people have tried to sell me on (boilerplate handling) and im like “you have that now and don’t even use it!”.
I think there is probably a way to use llms to try and extract intention and then call real dependable tools to actually perform the actions. This cult of purity where the llm must actually be generating the tokens themselves… why?
I’m all for coding tools. I love them. They have to actually work though. Paradigm is completely wrong right now. I don’t need it to “appear” good, i need it to BE good.
They are statistical prediction machines. The more they output, the larger the portion of their “context window” (statistical prior) becomes the very output they generated. It’s a fundamental property of the current LLM design that the snake will eventually eat enough of it’s tail to puke garbage code.
I think we’ve tapped most of the mileage we can get from the current science, the AI bros conveniently forget there have been multiple AI winters, I suspect we’ll see at least one more before “AGI” (if we ever get there).
Imagine if we did “vibe city infrastructure”. Just throw up a fucking suspension bridge and we’ll hire some temps to come in later to find the bad welds and missing cables.
Glad someone paid a bunch of worthless McKinsey consultants what I could’ve told you myself
It is not worthless. My understanding is that management only trusts sources that are expensive.
Yep, going through that at work, they hired several consultant companies and near as I can tell, they just asked employees how the company was screwing up, we largely said the same things we always say to executives, they repeated them verbatim, and executives are now praising the insight on how to fix our business…
it’s slowing you down. The solution to that is to use it in even more places!
Wtf was up with that conclusion?
I don’t think it’s meant to be a conclusion. The article serves as a recap of several reports and studies about the effectivity of LLMs with coding, and the final quote from Bain & Company was a counterpoint to the previous ones asserting that productivity gains are minimal at best, but also that measuring productivity is a grey area.
It remains to be seen whether the advent of “agentic AIs,” designed to autonomously execute a series of tasks, will change the situation.
“Agentic AI is already reshaping the enterprise, and only those that move decisively — redesigning their architecture, teams, and ways of working — will unlock its full value,” the report reads.
“Devs are slower with and don’t trust LLM based tools. Surely, letting these tools off the leash will somehow manifest their value instead of exacerbating their problems.”
Absolute madness.
How are you interpreting it that way. Did you miss a sentence or something in the quote?
It’s not interpretation, it’s extrapolation.
I code with LLMs every day as a senior developer but agents are mostly a big lie. LLMs are great for information index and rubber duck chats which already is incredible feaute of the century but agents are fundamentally bad. Even for Python they are intern-level bad. I was just trying the new Claude and instead of using Python’s pathlib.Path it reinvented its own file system path utils and pathlib is not even some new Python feature - it has been de facto way to manage paths for at least 3 years now.
That being said when prompted in great detail with exact instructions agents can be useful but thats not what being sold here.
Oh yes. The Great pathlib. The Blessed pathlib. Hallowed be it and all it does.
I’m a Ruby girl. A couple of years ago I was super worried about my decision to finally start learning Python seriously. But once I ran into pathlib, I knew for sure that everything will be fine. Take an everyday headache problem. Solve it forever. Boom. This is how standard libraries should be designed.
I disagree. Take a routine problem and invent a new language for it. Then split it into various incompatible dialects, and make sure in all cases it requires computing power that no one really has.
Pathlib is very nice indeed, but I can understand why a lot of languages don’t do similar things. There are major challenges implementing something like that. Cross-platform functionality is a big one, for example. File permissions between Unix systems and Windows do not map perfectly from one system to another which can be a maintenance burden.
But I do agree. As a user, it feels great to have. And yes, also in general, the things Python does with its standard library are definitely the way things should be done, from a user’s point of view at least.
If it wasn’t for all the AI hype that it’s going to do everyone’s job, LLMs would be widely considered an amazing advancement in computer-human interaction and human assistance. They are so much better than using a search engine to parse web forums and stack overflow, but that’s not going to pay for investing hundreds of billions into building them out. My experience is like yours - I use AI chat as a huge information index mainly, and helpful sounding board occasionally, but it isn’t much good beyond that.
They are so much better than using a search engine to parse web forums and stack overflow,
The hallucinations (more accurately bullshitting) and the fact they have to get new training data but are discouraging people from engaging in the structures that do so make this highly debatable
I will concur with the whole ‘llm keeps suggesting to reinvent the wheel’
And poorly. Not only did it not use a pretty basic standard library to do something, it’s implementation is generally crap. For example it offered up a solution that was hard coded to IPv4, and the context was very ipv6 heavy
I have a theory that it’s partly because a bunch of older StackOverflow answers have more votes than newer ones using new features. More referring to not using relatively new features as much as it should.
I miss the days when machine learning was fun. Poking together useless RNN models with a small dataset to make a digital Trump that talked about banging his daughter, end endless nipples flowing into America. Exploring the latent space between concepts.
Senior Management in much of Corporate America is like a kind of modern Nobility in which looking and sounding the part is more important than strong competence in the field. It’s why buzzwords catch like wildfire.
Lmao calling nobility would imply we can’t vote our senior management and it often ends up being whoever “the king” wants or one of their children.
Wait…
Are you trying to tell me that the people want to sell me their universal panacea for all human endeavours were… lying…? Say it ain’t so.
I mean, originally they thought they had come upon a magic bullet. Turns out it wasn’t the case, and now they’re going to suffer for it.
You’re assuming honesty and they’ve earned the opposite.
I have been vibe coding a whole game in JavaScript to try it out. So far I have gotten a pretty ok game out of it. It’s just a simple match three bubble pop type of thing so nothing crazy but I made a design and I am trying to implement it using mostly vibe coding.
That being said the code is awful. So many bad choices and spaghetti code. It also took longer than if I had written it myself.
So now I have a game that’s kind of hard to modify haha. I may try to setup some unit tests and have it refactor using those.
Sounds like vibecoders will have to relearn the lessons of the past 40 years of software engineering.
As with every profession every generation… only this time on their own because every company forgot what employee training is and expects every to be born with 5 years of experience.
Wait, are you blaming AI for this, or yourself?
Blaming? I mean it wrote pretty much all of the code. I definitely wouldn’t tell people I wrote it that way haha.
I use AI as an entryway to learning or for finding the name or technique that I’m thinking of but can’t remember or know it’s name so then i can look elsewhere for proper documentation. I would never have it just blindly writing code.
Sadly search engines getting shitter has sort of made me have to use it to replace them.
Then it’s also good to quickly parse an error for anything obviously wrong.
This article sums up a Stanford study of AI and developer productivity. TL;DR - net productivity boost is 15-20%, which tracks with my own experience as a dev.
LLMs work great to ask about tons of documentation and learn more about high-level concepts. It’s a good search engine.
The code they produce have basically always disappointed me.
On proprietary products, they are awful. So many hallucinations that waste hours. A manager used one on a code review of mine and only admitted it after I spent the afternoon chasing it.
Those happen so often. I’ve taken to stop calling them hallucinations anymore (that’s anthropomorphising and over-selling what LLMs do imho). They are statistical prediction machines, and either they hit their practical limits of predicting useful output, or we just call it broken.
I think the next 10 years are going to be all about learning what LLMs are actually good for, and what they are fundamentally limited at no matter how much GPU ram we throw at it.
Not even proprietary, just niche things. In other words anything that’s rarely used in open source code, because there’s nothing to train the models on.
I sometimes get up to five lines of viable code. Then upon occasion what should have been a one liner tries to vomit all over my codebase. The best feature about AI enabled IDE is the button to decline the mess that was just inflicted.
In the past week I had two cases I thought would be “vibe coding” fodder, blazingly obvious just tedious. One time it just totally screwed up and I had to scrap it all. The other one generated about 4 functions in one go and was salvageable, though still off in weird ways. One of those was functional, just nonsensical. It had a function to check whether a certain condition was present or not, but instead of returning a boolean, it passed a pointer to a string and set the string to “” to indicate false… So damn bizarre, hard to follow and needlessly more lines of code, which is another theme of LLM generated code.
I’d be inclined to try using it if it was smart enough to write my unit tests properly, but it’s great at double inserting the same mock and have 0 working unit tests.
I might try using it to generate some javadoc though… then when my org inevitably starts polling how much ai I use I won’t be in the gutter lol
I personally think unit tests are the worst application of AI. Tests are there to ensure the code is correct, so ideally the dev would write the tests to verify that the AI-generated code is correct.
I personally don’t use AI to write code, since writing code is the easiest and quickest part of my job. I instead use it to generate examples of using a new library, give me comparisons of different options, etc, and then I write the code after that. Basically, I use it as a replacement for a search engine/blog posts.
Ideally, there are requirements before anything, and some TDD types argue that the tests should come before the code as well.
Ideally, the customer is well represented during requirements development - ideally, not by the code developer.
Ideally, the code developer is not the same person that develops the unit tests.
Ideally, someone other than the test developer reviews the tests to assure that the tests do in-fact provide requirements coverage.
Ideally, the modules that come together to make the system function have similarly tight requirements and unit-tests and reviews, and the whole thing runs CI/CD to notify developers of any regressions/bugs within minutes of code check in.
In reality, some portion of that process (often, most of it) is short-cut for one or many reasons. Replacing the missing bits with AI is better than not having them at all.
To preface I don’t actually use ai for anything at my job, which might be a bad metric but my workflow is 10x slower if i even try using ai
That said, I want AI to be able to do unit tests in the sense that I can write some starting ones, then it be able to infer what branches aren’t covered and help me fill the rest
Obviously it’s not smart enough, and honestly I highly doubt it will ever be because that’s the nature of llm, but my peeve with unit test is that testing branches usually entail just copying the exact same test but changing one field to be an invalid value, or a dependency to throw. It’s not hard, just tedious. Branching coverage is already enforced, so you should know when you forgot to test a case.
I also think you should treat ai code as a pull request and actually review what it writes. My coworkers that do use it don’t really proofread, so it ends up having some bad practices and code smells.
The reason tests are a good candidate is that there is a lot of boilerplate and no complicated business logic. It can be quite a time saver. You probably know some untested code in some project - you could get an llm to write some tests that would at least poke some key code paths, which is better than nothing. If the tests are wrong, it’s barely worse than having no tests.
What model are you using? I’ve had such a radically different experience but I’ve only bothered with the latest models. The old ones weren’t even worth trying with
I’ve seen it generate working unit tests plenty. In the sense that they pass.
…they do not actually test the functionality.
Of course that function returns what you’re asserting - you overwrote its actual output and checked against that!
One of the guys at my old job submitted a PR with tests that basically just mocked everything, tested nothing. Like,
with patch("something.whatever", return_value=True): assert whatever(0) is True assert whatever(1) is True
Except for a few dozen lines, with names that made it look like they were doing useful.
He used AI to generate them, of course. Pretty useless.
For most large projects, writing the code is the easy part anyway.
Writing new code is easier than editing someone else’s code so the idea of a tool that essentially has you edit the entire code from start to the end is just funny.
It turns every prototyping exercise into a debugging exercise. Even talented coders often suck ass at debugging.
I work adjacent to software developers, and I have been hearing a lot of the same sentiments. What I don’t understand, though, is the magnitude of this bubble then.
Typically, bubbles seem to form around some new market phenomenon or technology that threatens to upset the old paradigm and usher in a new boom. Those market phenomena then eventually take their place in the world based on their real value, which is nowhere near the level of the hype, but still substantial.
In this case, I am struggling to find examples of the real benefits of a lot of these AI assistant technologies. I know that there are a lot of successes in the AI realm, but not a single one I know of involves an LLM.
So, I guess my question is, “What specific LLM tools are generating profits or productivity at a substantial level well exceeding their operating costs?” If there really are none, or if the gains are only incremental, then my question becomes an incredulous, “Is this biggest in history tech bubble really composed entirely of unfounded hype?”
From what I’ve seen and heard, there are a few factors to this.
One is that the tech industry right now is built on venture capital. In order to survive, they need to act like they’re at the forefront of the Next Big Thing in order to keep bringing investment money in.
Another is that LLMs are uniquely suited to extending the honeymoon period.
The initial impression you get from an LLM chatbot is significant. This is a chatbot that actually talks like a person. A VC mogul sitting down to have a conversation with ChatGPT, when it was new, was a mind-blowing experience. This is a computer program that, at first blush, appears to be able to do most things humans can do, as long as those things primarily consist of reading things and typing things out - which a VC, and mid/upper management, does a lot of. This gives the impression that AI is capable of automating a lot of things that previously needed a live, thinking person - which means a lot of savings for companies who can shed expensive knowledge workers.
The problem is that the limits of LLMs are STILL poorly understood by most people. Despite constructing huge data centers and gobbling up vast amounts of electricity, LLMs still are bad at actually being reliable. This makes LLMs worse at practically any knowledge work than the lowest, greenest intern - because at least the intern can be taught to say they don’t know something instead of feeding you BS.
It was also assumed that bigger, hungrier LLMs would provide better results. Although they do, the gains are getting harder and harder to reach. There needs to be an efficiency breakthrough (and a training breakthrough) before the wonderful world of AI can actually come to pass because as it stands, prompts are still getting more expensive to run for higher-quality results. It took a while to make that discovery, so the hype train was able to continue to build steam for the last couple years.
Now, tech companies are doing their level best to hide these shortcomings from their customers (and possibly even themselves). The longer they keep the wool over everyone’s eyes, the more money continues to roll in. So, the bubble keeps building.
This struck upon one of the greatest wishes of all corporations. A way to get work without having to pay people for it.
AI is a financial scam. Basically companies that are already mature promise great future profits thanks to this new technological miracle, which makes their stock more valuable than it otherwise would be. Cory Doctorow has written eloquently about this.
About that “net slowdown”. I think it’s true, but only in specific cases. I the user already knows well how to write code, an LLM might be only marginally useful or even useless.
However, there are ways to make it useful, but it requires specific circumstances. For example, you can’t be bothered to write a simple loop, you can use and LLM to do it. Give the boring routine to an LLM, and you can focus on naming the variables in a fitting way or adjusting the finer details to your liking.
Can’t be bothered to look up the exact syntax for a function you use only twice a year? Let and LLM handle that, and tweak the details. Now, you didn’t spend 15 minutes reading stack overflow posts that don’t answer the exact question you had in mind. Instead, you spent 5 minutes on the whole thing, and that includes the tweaking and troubleshooting parts.
If you have zero programming experience, you can use and LLM to write some code for you, but prepare to spend the whole day troubleshooting something that is essentially a black box to you. Alternatively, you could ask a human to write the same thing in 5-15 minutes depending on the method they choose.
The biggest value I get from AI in this space is when I get handed a pile of spagehtti and ask for an initial overview.
I am jack’s complete lack of surprise.
I always need to laugh when I read “Agentic AI”
I have not seen an AI generated code which is correct. Not once. I’ve certainly seen it broadly correct and used it for the gist of something. But normally it fucks something up - imports, dependencies, logic, API calls, or a combination of all them.
I sure as hell wouldn’t trust to use it without reviewing it thoroughly. And anyone stupid enough to use it blindly through “vibe” programming deserves everything they get. And most likely that will be a massive bill and code which is horribly broken in some serious and subtle way.
But will something be done about it?
NOooOoOoOoOoo. As long as it is still the new shiny toy for techbros and executive-bros to tinker with, it'll continue.
z3rOR0ne@lemmy.ml 3 weeks ago
Even though this shot was apparent from day fucking 1, at least the Tech Billionaires were able to cause mass layoffs, destroy an entire generation of new programmers’ careers, introduce an endless amount of tech debt and security vulnerabilities, all while grifting investors/businesses and making billions off of all of it.
Sad excuses for sacks of shit, all of them.
Prove_your_argument@piefed.social 2 weeks ago
Look on the bright side, in a couple of years they will come crawling back to us, desperate for new things to be built so their profit machines keep profiting.
Current ML techniques literally cannot replace developers for anything but the most rudimentary of tasks.
I wish we had true apprenticeships out there for development and other tech roles.