Do not forgive them. We already have a description for them:
"A bunch of mindless jerks who'll be the first against the wall when the revolution comes."
"A bunch of mindless jerks who'll be the first against the wall when the revolution comes."
The thing about hype cycles (including AI) is that the marketing department manages to convince the purchases to do their job for them.
You see yourselves as the disenfranchised proletariats of tech, crusading righteously against AI companies and myopic, trend-chasing managers, resentful of their apparent success at replacing your hard-earned skill with an API call.
It’s an emotional argument, born of tribalism. I’d find it easier to believe many claims on this site that AI is all a big scam and such if it weren’t so obvious that this underlies your very motivated reasoning. It is a big mirage of angst that causes people on here to clamor with perfunctory praise around every blog post claiming that AI companies are unprofitable, AI is useless, etc.
Think about why you believe the things you believe. Are you motivated by reason, or resentment?
The two types of responses to AI I see are your very defensive type, and people saying "I don't get it".
Their claim following that is that because there hasn't been an exponential growth in App store releases, domain name registrations or Steam games, that, beyond just AI producing shoddy code, AI has led to no increase in the amount of software at all, or none that could be called remarkable or even notable in proportion to the claims made by those at AI companies.
I think this ignores the obvious signs of growth in AI companies which providing software engineering and adjacent services via AI. These companies' revenues aren't emerging from nothing. People aren't paying them billions unless there is value in the product.
These trends include
1. The rapid growth of revenue of AI model companies, OpenAI, Anthropic, etc. 2. The massive growth in revenue of companies that use AI including Cursor, replit, loveable etc 3. The massive valuation of these companies
Anecdotally, with AI I can make shovelware apps very easily, spin them up effortlessly and fix issues I don't have the expertise or time to do myself. I don't know why the author of TFA claims that he can't make a bunch of one-off apps with capabilities avaliable today when it's clear that many many people can, have done so, have documented doing so, have made money selling those apps, etc.
You can't use growth of AI companies as evidence to refute the article. The premise is that it's a bubble. The growth IS the bubble, according to the claim.
> I don't know why the author of TFA claims that he can't make a bunch of one-off apps
I agree... One-off apps seem like a place where AI can do OK. Not that I care about it. I want AI that can build and maintain my enterprise B2B app just as well as I can in a fraction of the time, and that's not what has been delivered.
> I want AI that can build and maintain my enterprise B2B app just as well as I can in a fraction of the time, and that's not what has been delivered.
AI isn't at that level yet but it is making fast strides in subsets of it. I can't imagine systems of models and the models themselves won't reach there in a couple years given how bad AI coding tools were just a couple years ago.
Oh, of course not. Just like people weren't paying vast sums of money for beanie babies and dotcoms in the late 1990s and mortgage CDOs in the late 2000s [EDIT] unless there was value in the product.
People paid a lot for beanie babies and various speculative securities on the assumption that they could be sold for more in the future. They were assets people aimed to resell at a profit. They had no value by themselves.
The source of revenue for AI companies has inherent value but is not a resell-able asset. You can't resell API calls you buy from an AI company at some indefinite later date. There is no "market" for reselling anything you purchase from a company that offers use of a web app and API calls.
I think the article's premise is basically correct - if we had a 10x explosion of productivity where is the evidence? I would think some is potentially hidden in corporate / internal apps but despite everyone at my current employer using these tools we don't seem to be going any faster.
I will admit that my initial thoughts on Copilot were that "yes this is faster" but that was back when I was only using it for rote / boilerplate work. I've not had a lot of success trying to get it to do higher level work and that's also the experience of my co-workers.
I can certainly see why a particular subset of programmers find the tools particularly compelling, if their job was producing boilerplate then AI is perfect.
The fundamental difference of opinion people have here though is some people see current AI capabilities as a floor, while others see it as a ceiling. I’d agree with arguments that AI companies are overvalued if current models are as capable as AI will ever be for the rest of time, but clearly that is not the case, and very likely, as they have been every few months over the past few years, they will keep getting better.
It's not ONE person. I agree that it's not "every single human being" either, but more of a preliminary result, but I don't understand why you discount results you dislike. I thought you were completely rational?
https://www.theregister.com/2025/07/11/ai_code_tools_slow_do...
Mousing implies things are visible and you merely point to them. Keyboard implies things are non-visible and you recall commands from memory. These two must have a principal difference. Many animals use tools: inanimate objects lying around that can be employed for some gain. Yet no animal makes a tool. Making a tool is different from using it because to make a tool one must foresee the need for it. And this implies a mental model of the world and the future, i.e. a very big change compared to simply using a suitable object on the spot. (The simplest "making" could be just carrying an object when there is no immediate need for it, e.g. a sufficiently long distance. Looks very simple and I myself do not know if any animals exhibit such behavior, it seems to be on the fence. It would be telling if they don't.)
I think the difference between mousing and keying is about as big as of using a tool and making a tool. Of course, if we use the same app all day long, then its keys become motor movements, but this skill remains confined to the app.
Also AI has been basically useless every time I tried it except converting some struct definitions across languages or similar tasks, it seems very unlikely that it would boost productivity by more than 10% let alone 400%.
FWIW, my own experiences with AI have ranged from mediocre to downright abysmal. And, no, I don't know which models the tools were using. I'm rather annoyed that it seems to be impossible to express a negative opinion about the value of AI without having to have a thoroughly documented experiment that inevitably invites the response that obviously some parameter was chosen incorrectly, while the people claiming how good it is get to be all offended when someone asks them to maybe show their work a little bit.
It’s like saying “I drove a car and it was horrible, cars suck” without clarifying what car, the age, the make, how much experience that person had driving, etc. Of course its more difficult to provide specifics than just say it was good or bad, but there is little value in claims that AI is altogether bad when you don’t offer any details about what it is specifically bad at and how.
That's an interesting comparison. That kind of statement can be reasonably inferred to be made by someone just learning to drive who doesn't like the experience of driving. And if I were a motorhead trying to convert that person to like driving, my first questions wouldn't be those questions, trying to interrogate them on their exact scenario to invalidate their results, but instead to question what aspect of driving they don't like to see if I could work out a fix for them that would meaningfully change their experience (and not being a motorhead, the only thing I can think of is maybe automatic versus manual transmission).
> there is little value in claims that AI is altogether bad when you don’t offer any details about what it is specifically bad at and how.
Also, do remember that this holds true when you s/bad/good/g.
But if all we have to go on is "I used it and it sucked" or "I used it and it was great", like, okay, good for you?
"Damn, these relational databases really suck, I don't know why anyone would use them, some of the data by my users had emojis in them and it totally it! Furthermore, I have some bits of data that have about 100-200 columns and the database doesn't work well at all, that's horrible!"
In some cases knowing more details could help, for example in the database example a person historically using MySQL 5.5 could have had a pretty bad experience, in which case telling them to use something more recent or PostgreSQL would have been pretty good.
In other cases, they're literally just holding it wrong, for example trying to use a RDBMS for something where a column store would be a bit better.
Replace the DB example with AI, same principles are at play. It is equally annoying to hear people blaming all of the tools when some are clearly better/worse than others, as well as making broad statements that cannot really be proven or disproven with the given information, as it is people always asking for more details. I honestly believe that all of these AI discussions should be had with as much data present as possible - both the bad and good experiences.
> If your experience makes you believe that certain tools are particularly good--or particularly bad--for the tasks at hand, you can just volunteer those specifics.
My personal experience:
* most self-hosted models kind of suck, use cloud ones unless you can get really beefy hardware (e.g. waste a lot of money on them)
* most free models also aren't very good, nor have that much context space
* some paid models also suck, the likes of Mistral (like what they're doing, just not very good at it), or most mini/flash models
* around Gemini 2.5 Pro and Claude Sonnet 4 they start getting somewhat decent, GPT 5 feels a bit slow and like it "thinks" too much
* regardless of what you do, you still have to babysit them a lot of the time, they might take some of the cognitive load off, but won't make you 10x faster usually, the gains might definitely be there from reduced development friction (esp. when starting new work items)
* regardless of what you do, they will still screw up quite a bit, much like a lot of human devs do out there - having a loop of tests will be pretty much mandatory, e.g. scripts that run the test suite and also the compilation
* agentic tools like RooCode feel like they make them less useless, as do good descriptions of what you want to do - references to existing files and patterns etc., normally throwing some developer documentation and ADRs at them should be enough but most places straight up don't have any of that, so feeding in a bunch of code is a must
* expect usage of around 100-200 USD per month for API calls if the rate limits of regular subscriptions are too limiting
And if they don't, then you'd understand the anger surely. You can't say "well obviously everybody should benefit" and then also scold the people who are mad that everybody isn't benefiting.
I’m not concerned for my job, in fact I’d be very happy if real AGI would be achieved. It would probably be the crowning tech achievement of the human race so far. Not only would I not have to work anymore, the majority of the world wouldn’t have to. We’d suddenly be living in a completely different world.
But I don’t believe that’s where we’re headed. I don’t believe LLMs in their current state can get us there. This is exactly like the web3 hype when the blockchain was the new hip tech on the block. We invent something moderately useful, with niche applications and grifters find a way to sell it to non technical people for major profit. It’s a bubble and anyone who spends enough time in the space knows that.
I agree that there are lots of limitations to current LLM's, but it seems somewhat naive to ignore the rapid pace of improvement over the last 5 years, the emergent properties of AI at scale, especially in doing things claimed to be impossible only years prior (remember when people said LLM's could never do math, or that image models could never get hands or text right?).
Nobody understands with greater clarity or specificity the limitations of current LLM's than the people working in labs right now to make them better. The AGI prognostications aren't suppositions pulled out of the realm of wishful thinking, they exist because of fundamental revelations that have occurred in the development of AI as it has scaled up over the past decade.
I know I claimed that HN's hatred of AI was an emotional one, but there is an element to their reasoning too that leads them down the wrong path. By seeing more flaws than the average person in these AI systems, and seeing the tact with which companies describe their AI offerings to make them seem more impressive (currently) than they are, you extrapolate that sense of "figuring things out" to a robust model of how AI is and must really be. In doing so, you pattern match AI hype to web3 hype and assume that since the hype is similar in certain ways, that it must also be a bubble/scam just waiting to pop and all the lies are revealed. This is the same pattern-matching trap that people accuse AI of making, and see through the flaws of an LLM output while it claims to have solved a problem correctly.
And that´s actually quite useful - given that most of this material is paywalled or blocked from search engines. It´s less useful when you look at code examples that mix different versions of python, and have comments referring to figures on the previous page. I´m afraid it becomes very obvious when you look under the hood at the training sets themselves, just how this is all being achieved.
All intelligence is pattern matching, just at different scales. AI is doing the same thing human brains do.
Hard not to respond to that sarcastically. If you take the time to learn anything about neuroscience you'll realise what a profoundly ignorant statement it is.
But even if it's not a lot, it's more than the number of LLMs that can invent new meaning which is a grand total of 0.
If tomorrow, all LLMs ceased to exist, humans would carry on just fine, and likely build LLMs all over again, next time even better.
LLMs are not anything like Web3, not "exactly like". Web3 is in no way whatsoever "something moderately useful", and if you ever thought it was, you were fooled by the same grifters when they were yapping about Web3, who have now switched to yapping about LLMs.
The fact that those exact same grifters who fooled you about Web3 have moved onto AI has nothing to do with how useful what they're yapping about actually is. Do you actually think those same people wouldn't be yapping about AI if there was something to it? Yappers gonna yap.
But Web3 is 100% useless bullshit, and AI isn't: they're not "exactly alike".
Please don't make false equivalences between them like claiming they're "exactly like" each other, or parrot the grifters by calling Web3 "moderately useful".
I think most people are motivated by values. Reason and emotion are merely tools one can use in service of those.
My experience that people who hew too strongly to the former tend to be more oblivious to what's going on in their psychology than most.
But I also really care about the quality of our code, and so far my experiments with AI have been disappointing. The empirical results described in this article ring true to me.
AI definitely has some utility, just as the last "game changer" - blockchain - does. But both technologies have been massively oversold, and there will be many, many tears before bedtime.
Please, enlighten me with your gigantic hyper-rational brain.
AI stans don’t become AI stans for no reason. They see the many enormous technological leaps and also see where progress is going. The many PhDs currently making millions at labs also have the right idea.
Just look at ChatGPT’s growth alone. No product in history compares, and it’s not an accident.
Bad framing and worse argument. It's emotional.
Every engineer here is evaluating what ai claims it can do as pronounced by ceos and managers (not expert in software dev) v reality. Follow the money.
Yeah, it's frustrating to see someone opine "critics are motivated by resentment rather than facts" as if it were street-smart savvy psychoanalysis... while completely ignoring how many influential voices boosting the concept have a bajillions of dollars in motive to speak as credulously and optimistically as possible.
I'd widen the frame a bit. People scared of losing their jobs might underestimate the usefulness of AI. Makes sense to me, it's the comforting belief. Worth keeping in mind while reading articles sceptical of AI.
But there's another side to this conversation: the people whose writing is pro AI. What's motivating them? What's worth keeping in mind while reading that writing?
But I am not claiming that AI is useless. It is useful, but I would rather destroy every data center that enjoy strengthening of techno-feudalism.
Yeah so the thing is the "success" is only "apparent". Having actually tried to use this garbage to do work, as someone who has been deeply interested in ML for decades, I've found the tools to be approximately useless. The "apparent success" is not due to any utility, it's due entirely to marketing.
I don't fear I'm missing out on anything. I've tried it, it didn't work. So why are my bosses a half dozen rungs up on the corporate ladder losing their entire minds over it? It's insanity. Delusional.
Damn, when did it become wrong for me to advocate in my best interests while my boss is trying to do the same by shoving broken and useless AI tools up my ass?
If the self checkout scanner at the supermarket started bickering with me for entering the wrong produce code, that would wrap up the whole Turing Test thing for me.