I've seen some discussions and I'd say there's lots of people who are really against the hyped expectations from the AI marketing materials, not necessarily against the AI itself. Things that people are against that would seem to be against AI, but are not directly against AI itself:
- Being forced to use AI at work
- Being told you need to be 2x, 5x or 10x more efficient now
- Seeing your coworkers fired
- Seeing hiring freeze because business think no more devs are needed
- Seeing business people make a mock UI with AI and boasting how programming is easy
- Seeing those people ask you to deliver in impossible timelines
- Frontend people hearing from backend how their job is useless now
- Backend people hearing from ML Engineers how their job is useless now
- etc
When I dig a bit about this "anti-AI" trend I find it's one of those and not actually against the AI itself.
The most credible argument against AI is really the expense involved in querying frontier models. If you want to strengthen the case for AI-assisted coding, try to come up with ways of doing that effectively with a cheap "mini"-class model, or even something that runs locally. "You can spend $20k in tokens and have AI write a full C compiler in a week!" is not a very sensible argument for anything.
If you keep digging, you will also find that there's a small but vocal sock puppet army who will doggedly insist that any claims to productivity gains are in fact just hallucinations by people who must not be talented enough developers to know the difference.
It's exhausting.
There are legitimate and nuanced conversations that we should be having! For example, one entirely legitimate critique is that LLMs do not tell LLM users that they are using libraries who are seeking sponsorship. This is something we could be proactive about fixing in a tangible way. Frankly, I'd be thrilled if agents could present a list of projects that we could consider clicking a button to toss a few bucks to. That would be awesome.
But instead, it's just the same tired arguments about how LLMs are only capable of regurgitating what's been scraped and that we're stupid and lazy for trusting them to do anything real.
> I wonder if the people who are against it haven't even used it properly.
I swear this is the reason people are against AI output (there are genuine reasons to be against AI without using it: environmental impact, hardware prices, social/copyright issues, CSAM (like X/Grok))
It feels like a lot of people hear the negatives, and try it and are cynical of the result. Things like 2 r's in Strawberry and the 6-10 fingers on one hand led to multiple misinterpretations of the actual AI benefit: "Oh, if AI can't even count the number of letters in a word, then all its answers are incorrect" is simply not true.
> It's so intriguing, I wonder if the people who are against it haven't even used it properly.
I feel like this is a common refrain that sets an impossible bar for detractors to clear. You can simply hand wave away any critique with “you’re just not using it right.”
If countless people are “using it wrong” then maybe there’s something wrong with the tool.
> If countless people are “using it wrong” then maybe there’s something wrong with the tool.
Not really. Every tool in existence has people that use it incorrectly. The fact that countless people find value in the tool means it probably is valuable.
When it comes to new emerging technologies everyone is searching the space of possibilities, exploring new ways to use said technologies, and seeing where it applies and creates value. In situations such as this, a positive sign is worth way more than a negative. The chances of many people not using it the right way are much much higher when no one really knows what the “right” way is.
It then shows hubris and a lack of imagination for someone in such a situation to think they can apply their negative results to extrapolate to the situation at large. Especially when so many are claiming to be seeing positive utility.
I had Claude read a 2k LOC module on my codebase for a bug that was annoying me for a while. It found it in seconds, a one line fix. I had forgotten to account for translation in one single line.
That's objectively valuable. People who argue it has no value or that it only helps normies who can't code or that sooner or later it will backfire are burying their heads in the sand.
I'm similarly bemused by those who don't understand where the anti-AI sentiment could come from, and "they must be doing it wrong" should usually be a bit of a "code smell". (Not to mention that I don't believe this post addresses any of the concrete concerns the article calls out, and makes it sound like much more of a strawman than it was to my reading.)
To preempt that on my end, and emphasize I'm not saying "it's useless" so much as "I think there's some truth to what the OP says", as I'm typing this I'm finishing up a 90% LLM coded tool to automate a regular process I have to do for work, and it's been a very successful experience.
From my perspective, a tool (LLMs) has more impact than how you yourself directly use it. We talk a lot about pits of success and pits of failure from a code and product architecture standpoint, and right now, as you acknowledge yourself in the last sentence, there's a big footgun waiting for any dev who turns their head off too hard. In my mind, _this is the hard part_ of engineering; keeping a codebase structured, guardrailed, well constrained, even with many contributors over a long period of time. I do think LLMs make this harder, since they make writing code "cheaper" but not necessarily "safer", which flies in the face of mantras such as "the best line of code is the one you don't need to write." (I do feel the article brushes against this where it nods to trust, growth, and ownership) This is not a hypothetical as well, but something I've already seen in practice in a professional context, and I don't think we've figured out silver bullets for yet.
While I could also gesture at some patterns I've seen where there's a level of semantic complexity these models simply can't handle at the moment, and no matter how well architected you make a codebase after N million lines you WILL be above that threshold, even that is less of a concern in my mind than the former pattern. (And again the article touches on this re: vibe coding having a ceiling, but I think if anything they weaken their argument by limiting it to vibe coding.)
To take a bit of a tangent with this comment though: I have come to agree with a post I saw a few months back, that at this point LLMs have become this cycle's tech-religious-war, and it's very hard to have evenhanded debate in that context, and as a sister post calls out, I also suspect this is where some of the distaste comes from as well.
HN has a huge anti AI crowd that is just as vocal and active as its pro AI crowd. My guess that this is true of the industry today and won’t be true of the industry 5 years from now: one of the crowds will have won the argument and the other will be out of the tech industry.
Vibe coding and slop strawmen are still strawmen. The quality of the debate is obviously a problem
I don’t understand why people are so resistant to the idea that use cases actually matter here. If someone says “you’re an idiot because you aren’t writing good, structured prompts,” or “you’re too big of an idiot to realize that your AI-generated code sucks” before knowing anything about what the other person was trying to do, they’re either speaking entirely from an ideological bias, or don’t realize that other people’s coding jobs might look a whole lot more different than theirs do.
What we call AI at the heart of coding agents, is the averaged “echo” of what people have published on the web that has (often illegitimately) ended up in training data. Yes it probably can spit out some trivial snippets but nothing near what’s needed for genuine software engineering.
Also, now that StackOverflow is no longer a thing, good luck meaningfully improving those code agents.
Coding agents are getting most meaningful improvements in coding ability from RLVR now, with priors formed by ingesting open source code and manuals directly, not SO, as the basis. The former doesn't rely on resources external to the AI companies at all, and can be scaled up as much as they like, while the latter will likely continue to expand, and they don't really need more of it if it doesn't. Not to mention that curated synthetic data has been shown to be very effective at training models, so they could generate their own textbooks based on open codebases or new languages or whatever and use that. Model collapse only happens when it's exclusively, and fully un-curated, model output that's being trained on.
Exactly this. Everything I've seen online is generally "I had a problem that could be solved in a few dozen lines of code and I asked the AI do it for me and it worked great!"
But what they asked the AI to do is something people have done a hundred times over, on existing platform tech, and will likely have little to no capability to solve problems that come up 5-10 years from now.
The reason AI is so good at coding right now is due to the 2nd Dot Com tech bubble that occurred between the simultaneous release of mobile platforms and the massive expansion of cloud technology. But now that the platforms that existed during that time will no longer exist, because it's no longer profitable to put something out there--the AI platforms will be less and less relevant.
Sure, sites like reddit will probably still exist where people will begin to ask more and more information that the AI can't help with, and subsequently the AI will train off of that information; but the rate of that information is going to go down dramatically.
In short, at some point the AI models will be worthless and I suspect that'll be whenever the next big "tech revolution" happens.
tomhow|21 days ago
https://news.ycombinator.com/newsguidelines.html
dredmorbius|19 days ago
Italic
*Escaped asterisks*
\*Double-Escaped asterisks\*
(tomhow seems to have goofed his escapes above. As I've done many times myself...)franciscop|21 days ago
- Being forced to use AI at work
- Being told you need to be 2x, 5x or 10x more efficient now
- Seeing your coworkers fired
- Seeing hiring freeze because business think no more devs are needed
- Seeing business people make a mock UI with AI and boasting how programming is easy
- Seeing those people ask you to deliver in impossible timelines
- Frontend people hearing from backend how their job is useless now
- Backend people hearing from ML Engineers how their job is useless now
- etc
When I dig a bit about this "anti-AI" trend I find it's one of those and not actually against the AI itself.
zozbot234|21 days ago
peteforde|21 days ago
It's exhausting.
There are legitimate and nuanced conversations that we should be having! For example, one entirely legitimate critique is that LLMs do not tell LLM users that they are using libraries who are seeking sponsorship. This is something we could be proactive about fixing in a tangible way. Frankly, I'd be thrilled if agents could present a list of projects that we could consider clicking a button to toss a few bucks to. That would be awesome.
But instead, it's just the same tired arguments about how LLMs are only capable of regurgitating what's been scraped and that we're stupid and lazy for trusting them to do anything real.
zythyx|21 days ago
I swear this is the reason people are against AI output (there are genuine reasons to be against AI without using it: environmental impact, hardware prices, social/copyright issues, CSAM (like X/Grok))
It feels like a lot of people hear the negatives, and try it and are cynical of the result. Things like 2 r's in Strawberry and the 6-10 fingers on one hand led to multiple misinterpretations of the actual AI benefit: "Oh, if AI can't even count the number of letters in a word, then all its answers are incorrect" is simply not true.
Forgeties79|21 days ago
I feel like this is a common refrain that sets an impossible bar for detractors to clear. You can simply hand wave away any critique with “you’re just not using it right.”
If countless people are “using it wrong” then maybe there’s something wrong with the tool.
hippo22|21 days ago
Not really. Every tool in existence has people that use it incorrectly. The fact that countless people find value in the tool means it probably is valuable.
dwallin|21 days ago
It then shows hubris and a lack of imagination for someone in such a situation to think they can apply their negative results to extrapolate to the situation at large. Especially when so many are claiming to be seeing positive utility.
airstrike|21 days ago
I had Claude read a 2k LOC module on my codebase for a bug that was annoying me for a while. It found it in seconds, a one line fix. I had forgotten to account for translation in one single line.
That's objectively valuable. People who argue it has no value or that it only helps normies who can't code or that sooner or later it will backfire are burying their heads in the sand.
seanmcdirmid|21 days ago
potsandpans|21 days ago
Doesn't mean the hammers are bad, no matter how many people join the community.
You need to learn how to use the tools.
existencebox|21 days ago
To preempt that on my end, and emphasize I'm not saying "it's useless" so much as "I think there's some truth to what the OP says", as I'm typing this I'm finishing up a 90% LLM coded tool to automate a regular process I have to do for work, and it's been a very successful experience.
From my perspective, a tool (LLMs) has more impact than how you yourself directly use it. We talk a lot about pits of success and pits of failure from a code and product architecture standpoint, and right now, as you acknowledge yourself in the last sentence, there's a big footgun waiting for any dev who turns their head off too hard. In my mind, _this is the hard part_ of engineering; keeping a codebase structured, guardrailed, well constrained, even with many contributors over a long period of time. I do think LLMs make this harder, since they make writing code "cheaper" but not necessarily "safer", which flies in the face of mantras such as "the best line of code is the one you don't need to write." (I do feel the article brushes against this where it nods to trust, growth, and ownership) This is not a hypothetical as well, but something I've already seen in practice in a professional context, and I don't think we've figured out silver bullets for yet.
While I could also gesture at some patterns I've seen where there's a level of semantic complexity these models simply can't handle at the moment, and no matter how well architected you make a codebase after N million lines you WILL be above that threshold, even that is less of a concern in my mind than the former pattern. (And again the article touches on this re: vibe coding having a ceiling, but I think if anything they weaken their argument by limiting it to vibe coding.)
To take a bit of a tangent with this comment though: I have come to agree with a post I saw a few months back, that at this point LLMs have become this cycle's tech-religious-war, and it's very hard to have evenhanded debate in that context, and as a sister post calls out, I also suspect this is where some of the distaste comes from as well.
seanmcdirmid|21 days ago
Vibe coding and slop strawmen are still strawmen. The quality of the debate is obviously a problem
DrewADesign|21 days ago
piskov|21 days ago
If only there were things called comments, clean-code, and what have you
isodev|21 days ago
Also, now that StackOverflow is no longer a thing, good luck meaningfully improving those code agents.
logicprog|21 days ago
blackcatsec|21 days ago
But what they asked the AI to do is something people have done a hundred times over, on existing platform tech, and will likely have little to no capability to solve problems that come up 5-10 years from now.
The reason AI is so good at coding right now is due to the 2nd Dot Com tech bubble that occurred between the simultaneous release of mobile platforms and the massive expansion of cloud technology. But now that the platforms that existed during that time will no longer exist, because it's no longer profitable to put something out there--the AI platforms will be less and less relevant.
Sure, sites like reddit will probably still exist where people will begin to ask more and more information that the AI can't help with, and subsequently the AI will train off of that information; but the rate of that information is going to go down dramatically.
In short, at some point the AI models will be worthless and I suspect that'll be whenever the next big "tech revolution" happens.