After working with agent-LLMs for some years now, I can confirm that they are completely useless for real programming.
They never helped me solve complex problems with low-level libraries. They can not find nontrivial bugs. They don't get the logic of interwoven layers of abstractions.
LLMs pretend to do this with big confidence and fail miserably.
For every problem I need to turn my brain to ON MODE and wake up, the LLM doesn't wake up.
It surprised me how well it solved another task: I told it to set up a website with some SQL database and scripts behind it. When you click here, show some filtered list there. Worked like a charm. A very solved problem and very simple logic, done a zillion times before. But this saved me a day of writing boilerplate.
I agree that there is no indication that LLMs will ever cross the border from simple-boilerplate-land to understanding-complex-problems-land.
>After working with agent-LLMs for some years now, I can confirm that they are completely useless for real programming.
>They never helped me solve complex problems with low-level libraries. They can not find nontrivial bugs. They don't get the logic of interwoven layers of abstractions.
>LLMs pretend to do this with big confidence and fail miserably.
This is true for most developers as well. The mean software developer, especially if you outsource, has failure modes worse than any LLM and round-trip time is not seconds but days.
The promise of LLMs is not that they solve the single most difficult tasks for you instantly, but that they do the easy stuff well enough that they replace offshore teams.
> The promise of LLMs is not that they solve the single most difficult tasks for you instantly, but that they do the easy stuff well enough that they replace offshore teams.
But that's exactly the *promise* of LLMs by the hypepeople behind it.
I really really want this to be true. I want to be relevant. I don’t know what to do if all those predictions are true and there is no need (or very little need) for programmers anymore.
But something tells me “this time is different” is different this time for real.
Coding AIs design software better than me, review code better than me, find hard-to-find bugs better than me, plan long-running projects better than me, make decisions based on research, literature, and also the state of our projects better than me. I’m basically just the conductor of all those processes.
Oh, and don't ask about coding. If you use AI for tasks above, as a result you'll get very well defined coding task definitions which an AI would ace.
I’m still hired, but I feel like I’m doing the work of an entire org that used to need twenty engineers.
More than any other effect they have LLMs breed something called "learned helplessness". You just listed a few things it may stay better than you at, and a few things that it is not better than you at and never will be.
Planning long running projects and deciding are things only you can do well!! Humans manage costs. We look out for our future. We worry. We have excitement, and pride. It wants you to think none of these things matter if course, because it doesn't have them. It says plausible things at random, basically. It can't love, it can't care, it won't persist.
WHATEVER you do don't let it make you forget that it's a bag of words and you are someing almost infinitely more capable
> Coding AIs design software better than me, review code better than me, find hard-to-find bugs better than me, plan long-running projects better than me, make decisions based on research, literature, and also the state of our projects better than me.
That is just not true, assuming you have a modicum of competence (which I assume you do). AIs suck at all these tasks; they are not even as good as an inexperienced human.
For all we know, you both could comparing using a Nokia 3310 and a workstation PC based on the hardware, but you both just say "this computer is better than that computer".
There are a ton of models out there, ran in a ton of different ways, that can be used in different ways with different harnesses, and people use different workflows. There is just so many variables involved, that I don't think it's neither fair nor accurate for anyone to claim "This is obviously better" or "This is obviously impossible".
I've been in situations where I hit my head against some hard to find bug for days, then I put "AI" (but what? No one knows) to it and it solves it in 20 minutes. I've also asked "AI" to do trivial work that it still somehow fucked up, even if I could probably have asked a non-programmer friend to do it and they'd be able to.
The variance is great, and the fact that system/developer/user prompts matter a lot for what the responses you get, makes it even harder to fairly compare things like this without having the actual chat logs in front of you.
LLMs generate the most likely code given the problem they're presented and everything they've been trained on, they don't actually understand how (or even if) it works. I only ever get away with that when I'm writing a parser.
Depends on how he defined "better". If he uses the word "better" to mean "good enough to not fail immediately, and done in 1/10th of the time", then he's correct.
This reads like shilling/advertisement.. Coding AIs are struggling for anything remotely complex, make up crap and present it as research, write tests that are just "return true", and won't ever question a decision you make.
Those twenty engineers must not have produced much.
I think part of what is happening here is that different developers on HN have very different jobs and skill levels. If you are just writing a large volume of code over and over again to do the same sort of things, then LLMs probably could take your job. A lot of people have joined the industry over time, and it seems like the intelligence bar moved lower and lower over time, particularly for people churning out large volumes of boilerplate code. If you are doing relatively novel stuff, at least in the sense that your abstractions are novel and the shape of the abstraction set is different from the standard things that exist in tutorials etc online, then the LLM will probably not work well with your style.
So some people are panicking and they are probably right, and some other people are rolling their eyes and they are probably right too. I think the real risk is that dumping out loads of boilerplate becomes so cheap and reliable that people who can actually fluently design coherent abstractions are no longer as needed. I am skeptical this will happen though, as there doesn’t seem to be a way around the problem of the giant indigestible hairball (I.e as you have more and more boilerplate it becomes harder to remain coherent).
No it doesn’t read like shilling and advertisement, it’s tiring hearing people continually dismiss coding agents as if they have not massively improved and are driving real value despite limitations and they are only just getting started. I’ve done things with Claude I never thought possible for myself to do, and I’ve done things where Claude made the whole effort take twice as long and 3x more of my time. It’s not like people are ignoring the limitations, it’s that people can see how powerful the already are and how much more headroom there is even with existing paradigms not to mention the compute scaling happening in 26-27 and the idea pipeline from the massive hoarding of talent.
I would say while LLMs do improve productivity sometimes, I have to say I flatly cannot believe a claim (at least without direct demonstration or evidence) that one person is doing the work of 20 with them in december 2025 at least.
I mean from the off, people were claiming 10x probably mostly because it's a nice round number, but those claims quickly fell out of the mainstream as people realised it's just not that big a multiplier in practice in the real world.
I don't think we're seeing this in the market, anywhere. Something like 1 engineer doing the job of 20, what you're talking about is basically whole departments at mid sized companies compressing to one person. Think about that, that has implications for all the additional management staff on top of the 20 engineers too.
It'd either be a complete restructure and rethink of the way software orgs work, or we'd be seeing just incredible, crazy deltas in output of software companies this year of the type that couldn't be ignored, they'd be impossible to not notice.
This is just plainly not happening. Look, if it happens, it happens, 26, 27, 28 or 38. It'll be a cool and interesting new world if it does. But it's just... not happened or happening in 25.
My experience is that you get out what you put in. If you have a well-defined foundation, AI can populate the stubs and get it 95% correct. Getting to that point can take a bit of thought, and AI can help with that, too, but if you lean on it too much, you'll get a mess.
And of course, getting to the point where you can write a good foundation has always been the bulk of the work. I don't see that changing anytime soon.
I'd be willing to give you access to the experiment I mentioned in a separate reply (have a github repo), as far as the output that you can get for a complex app buildout.
Will admit It's not great (probably not even good) but it definitely has throughput despite my absolute lack of caring that much [0]. Once I get past a certain stage I am thinking of doing an A-B test where I take an earlier commit and try again while paying more attention... (But I at least want to get where there is a full suite of UOW cases before I do that, for comparison's sake.)
> Those twenty engineers must not have produced much.
I've been considered a 'very fast' engineer at most shops (e.x. at multiple shops, stories assigned to me would have a <1 multiplier for points[1])
20 is a bit bloated, unless we are talking about WITCH tier. I definitely can get done in 2-3 hours what could take me a day. I say it that way because at best it's 1-2 hours but other times it's longer, some folks remember the 'best' rather than median.
[0] - It started as 'prompt only', although after a certain point I did start being more aggressive with personal edits.
[1] - IDK why they did it that way instead of capacity, OTOH that saved me when it came to being assigned Manual Testing stories...
I was a chef in Michelin-starred restaurants for 11 years. One of my favorite positions was washing dishes. The goal was always to keep the machine running on its 5-minute cycle. It was about getting the dishes into racks, rinsing them, and having them ready and waiting for the previous cycle to end—so you could push them into the machine immediately—then getting them dried and put away after the cycle, making sure the quality was there and no spot was missed. If the machine stopped, the goal was to get another batch into it, putting everything else on hold. Keeping the machine running was the only way to prevent dishes from piling up, which would end with the towers falling over and breaking plates. This work requires moving lightning fast with dexterity.
AI coding agents are analogous to the machine. My job is to get the prompts written, and to do quality control and housekeeping after it runs a cycle. Nonetheless, like all automation, humans are still needed... for now.
If it requires an expert engineer/dishwasher to keep the flow running perfectly, the human is the bottleneck in the process. This sounds a lot more like the past before AI to me. What AI does is just give you enough dishes that they don’t need to be washed at all during dinner service. Just let them pile up dirty or throw them away and get new dishes tomorrow it’s so immaterial to replace that washing them doesn’t always make sense. But if for some reason you do want to reuse them, then, it washes and dries them for you too. You just look over things at the end and make sure they pass your quality standards. If they left some muck on a plate or lipstick on a cup, just tell it not to let that happen again and it won’t. So even your QC work gets easier over time. The labor needed to deal with dirty dishes is drastically reduced in any case.
"AI" doesn't have a clue what to do on its own. Humans will always be in the loop, because they have goals, while the AI is designed to placate and not create.
The amount of "AI" garbage I have to sift through to find one single gem is about the same or more work than if I had just coded it myself. Add to that the frustration of dealing with a compulsive liar, and it's just a fucking awful experience for anyone that actually can code.
> I’m basically just the conductor of all those processes.
a car moves faster than you, can last longer than you, and can carry much more than you. But somehow, people don't seem to be scared of cars displacing them(yet)? Perhaps autodriving would in the near future, but there still needs to be someone making decisions on how best to utilize that car - surely, it isn't deciding to go to destination A without someone telling them.
> I feel like I’m doing the work of an entire org that used to need twenty engineers.
and this is great. A combine harvester does the work of what used to be an entire village for a week in a day. More output for less people/resources expended means more wealth produced.
> a car moves faster than you, can last longer than you, and can carry much more than you. But somehow, people don't seem to be scared of cars displacing them(yet)?
People whose life were based around using horses for transportation were very scared of cars replacing them though, and correctly so, because horses for transportation is something people do for leisure today, not necessity. I feel like that's a more apt analogy than comparing cars to any human.
> More output for less people/resources expended means more wealth produced.
This is true, but it probably also means that this "more wealth produced" will be more concentrated, because it's easier to convince one person using AI that you should have half of the wealth they produce, rather than convincing 100 people you should have half of what they produce. From where I'm standing, it seems to have the same effects (but not as widespread or impactful, yet) as industrialization, that induced that side-effect as well.
> a car moves faster than you, can last longer than you, and can carry much more than you. But somehow, people don't seem to be scared of cars displacing them(yet)?
I think I've been using AI wrong. I can't understand testimonies like this. Most times I try to use AI for a task, it is a shitshow, and I have to rewrite everything anyway.
Do you tell AI the patterns/tools/architecture you want? Telling agents to "build me XYZ, make it gud!" is likely to precede a mess, telling it to build a modular monolith using your library/tool list, your preferred folder structure, other patterns/algorithms you use, etc will end you up with something that might have some minor style issues or not be perfectly canonical, but will be approximately correct within a reasonable margin, or is within 1-2 turns of being so.
You have to let go of the code looking exactly a certain way, but having code _work_ a certain way at a coarse level is doable and fairly easy.
I don’t know about right/wrong. You need to use the tools that make you productive. I personally find that in my work there are dozens of little scripts or helper functions that accelerate my work. However I usually don’t write them because I don’t have the time. AI can generate these little scripts very consistently. That accelerates my work. Perhaps just start simple.
how much time/effort have you put in to educate yourself about how they work, what they excel at, what they suck at, what is your responsibility when you use them…? this effort is directly proportional to how well they will serve you
However I'm still finding a trend even in my org; better non-AI developers tend to be better at using AI to develop.
AI still forgets requirements.
I'm currently running an experiment where I try to get a design and then execute on an enterprise 'SAAS-replacement' application [0].
AI can spit forth a completely convincing looking overall project plan [1] that has gaps if anyone, even the AI itself, tries to execute on the plan; this is where a proper, experienced developer can step in at the right steps to help out.
IDK if that's the right way to venture into the brave new world, but I am at least doing my best to be at a forefront of how my org is using the tech.
[0] - I figured it was a good exercise for testing limits of both my skills prompting and the AI's capability. I do not expect success.
They do all those things you've mentioned more efficiently than most of us, but they fall woefully short as soon as novelty is required. Creativity is not in their repertoire. So if you're banging out the same type of thing over and over again, yes, they will make that work light and then scarce. But if you need to create something niche, something one-off, something new, they'll slip off the bleeding edge into the comfortable valley of the familiar at every step.
I choose to look at it as an opportunity to spend more time on the interesting problems, and work at a higher level. We used to worry about pointers and memory allocation. Now we will worry less and less about how the code is written and more about the result it built.
Take food for example. We don't eat food made by computers even though they're capable of making it from start to finish.
Sure we eat carrots probably assisted by machines, but we are not eating dishes like protein bars all day every day.
Our food is still better enjoyed when made by a chef.
Software engineering will be the same. No one will want to use software made by a machine all day every day. There are differences in the execution and implementation.
No one will want to read books entirely dreamed up by AI. Subtle parts of the books make us feel something only a human could have put right there right then.
No one will want to see movies entirely made by AI.
The list goes on.
But you might say "software is different". Yes but no, in the abundance of choice, when there will be a ton of choice for a type of software due to the productivity increase, choice will become more prominent and the human driven software will win.
Even today we pick the best terminal emulation software because we notice the difference between exquisitely crafted and bloated cruft.
> So if you're banging out the same type of thing over and over again, yes, they will make that work light and then scarce.
The same thing over and over again should be a SaaS, some internal tool, or a plugin. Computers are good at doing the same thing over and over again and that's what we've been using them for
> But if you need to create something niche, something one-off, something new, they'll slip off the bleeding edge into the comfortable valley of the familiar at every step.
Even if the high level description of a task may be similar to another, there's always something different in the implementation. A sports car and a sedan have roughly the same components, but they're not engineered the same.
> We used to worry about pointers and memory allocation.
Some still do. It's not in every case you will have a system that handle allocations and a garbage collector. And even in those, you will see memory leaks.
> Now we will worry less and less about how the code is written and more about the result it built.
I think your image of LLMs is a bit outdated. Claude Code with well-configured agents will get entirely novel stuff done pretty well, and that’s only going to get better over time.
I mean, AIs can drop something fast the same way you cannot beat a computer at adding or multiplying.
After that, you find mistakes, false positives, code that does not work fully, and the worse part is the last one: code that does not work fully but also, as a consequence, that you do NOT understand yet.
That is where your time shrinks: now you need to review it.
Also, they do not design systems better. Maybe partial pieces. Give them something complex and they will hallucinate worse solutions than what you already know if you have, let us say, over 10 years of experience programming in a language (or mabye 5).
Now multiply this unreliability problem as the code you "AI-generate" grows.
Now you have a system you do not know if it is reliable and that you do not understand to modify. Congrats...
I use AI moderately for the tasks is good at: generate some scripts, give me this small typical function amd I review it.
Review my code: I will discard part of your mistakes and hallucinations as a person that knows well the language and will find maybe a few valuable things.
Also, when reviewing and found problems in my code I saw that the LLMs really need to hallucinate errors that do not exist to justify their help. This is just something LLMs seem to not be accurate at.
Also, when problems go a bit more atypical or past a level of difficulty, it gets much more unreliable.
All in all: you are going to need humans. I do not know how many, I do not know how much they will improve. I just know that they are not reliable and this "generate-fast-unreliable vs now I do not know the codebase" is a fundamental obstacle that I think it is if not very difficult, impossible to workaround.
This is not how I think about it.
Me and the coding assistant is better then me or the coding assistant separately.
For me its not about me or the coding assistant, its me and the coding assistant. But I'm also not a professional coder, i dont identify as a coder. I've been fiddling with programming my whole life, but never had it as title, I've more worked from product side or from stakeholder side, but always got more involved, as I could speak with the dev team.
This also makes it natural for me to work side-by-side with the coding assistant, compared maybe to pure coders, who are used to keeping the coding side to themselves.
> Coding AIs design software better than me, review code better than me, find hard-to-find bugs better than me, plan long-running projects better than me, make decisions based on research, literature, and also the state of our projects better than me.
They don't do any of that better than me; they do it poorer and faster, but well enough for most of the time.
I'm extremely pro-faster-keyboard, i use the faster keyboards in almost every opportunity i can, i've been amazed by debugging skills (in fairness, i've also been very disappointed many times), i've been bowled over by my faster keyboard's ability to whip out HTML UI's in record time, i've been genuinely impressed by my faster keyboard's ability to flag flaws in PRs i'm reviewing.
All this to say, i see lots of value in faster keyboard's but add all the prompts, skills and hooks you like, explain in as much detail as you like about modularisation, and still "agents" cannot design software as well as a human.
Whatever the underlying mechanism of an LLM (to call it a next token predictor is dismissively underselling its capabilities) it does not have a mechanism to decompose a problem into independently solvable pieces. While that remains true, and i've seen zero precursor of a coming change here - the state of the art today is equiv to having the agent employ a todo list - while this remains true, LLMs cannot design better than humans.
There are many simple CRUD line of business apps where they design well enough (well more accurately stated, the problem is small/simple enough) that it doesn't matter about this lack of design skill in LLMs or agents. But don't confuse that for being able to design software in the more general use case.
Exactly, for the thing that has been done in Github 10000x times over, LLMs are pretty awesome and they speed up your job significantly (it's arguable if you would be better off using some abstraction already built if that's the case).
But try to do something novel and... they become nearly useless. Not like anything particularly difficult, just something that's so niche it's never been done before. It will most likely hallucinate some methods and call it a day.
As a personal anecdote, I was doing some LTSpice simulations and tried to get Claude Sonnet to write a plot expression to convert reactance to apparent capacitance in an AC sweep. It hallucinated pretty much the entire thing, and got the equation wrong (assumed the source was unit intensity, while LTSpice models AC circuits with unit voltage. This surely is on the internet, but apparently has never been written alongside the need to convert an impedance to capacitance!).
As of today NONE of the known AI codebots can
solve correctly ANY of the 50+ programming
exercises we use to interview fresh grads
or summer interns. NONE! Not even level 1
problems that can be solved in fewer than 20
lines of code with a bit of middle school
math.
I promise you that I can show you how to reliably solve any of them using any of the latest OpenAI models. Email me if you want proof; josh.d.griffith at gmail
After 25+ years in this field, having interviewed ~100 people for both my startup and other companies, I'm having a hard time believing this. You're either in an extremely niche field (such as to make your statement irrelevant to 99.9% of the industry), or it's hyperbole, or straight up bs.
Interviewing is an art, and IME "gotcha" types of questions never work. You want to search for real-world capabilities, and like it or not the questions need to match those expectations. If you're hiring summer interns and the SotA models can't solve those questions, then you're doing something wrong. Sorry, but having used these tools for the past three years this is extremely ahrd to believe.
I of course understand if you can't, but sharing even one of those questions would be nice.
Sure LLMs can churn out code, and they sort of work for developers who already understand code and design, but what happens when that junior dev with no hard experience builds their years of experience with LLMs?
Over time those who actually understand what the LLMs are doing and how to correct the output are replaced by developers who've never learned the hard lessons of writing code line by line. The ability to reason about code gets lost.
This points to the hard problem that the article highlights. The hard problem of software is actually knowing how to write it, which usually takes years, sometimes up to a decade of real experience.
Any idiot can churn out code that doesn't work. But working, effective software takes a lot of skill that LLMs will be stripping people of. Leaving a market there for people who have actually put the time in and understand software.
I feel you, it's scary. But the possibilities we're presented with are incredible. I'm revisiting all these projects that I put aside because they were "too big" or "too much for a machine". It's quite exciting
There will be a need. Don't worry. Most people still haven't figured out how to properly read and interpret instructions. So they build things incorrectly - with or without AI
Seriously. The bar is that low. When people say "AI slop" I just chuckle because it's not "AI" it's everyone. That's the general state of the industry.
So all you have to do is stay engaged, ask questions, and understand the requirements. Know what it is you're building and you'll be fine.
Perfect economic substitution in coding doesn't happen for a long time. Meanwhile, AI appears as an amplifier to the human and vice versa. That the work will change is scary, but the change also opens up possibilities, many of them now hard to imagine.
The AI is pretty scary if you think most of software engineering is about authoring individual methods and rubber ducking about colors of paint and brands of tools.
Once you learn that it's mostly about interacting with a customer (sometimes this is yourself), you will realize the AI is pretty awful at handling even the most basic tasks.
Following a product vision, selecting an appropriate architecture and eschewing 3rd party slop are examples of critical areas where these models are either fundamentally incapable or adversely aligned. I find I have to probe ChatGPT very hard to get it to offer a direct implementation of something like a SAML service provider. This isn't a particularly difficult thing to do in a language like C# with all of the built in XML libraries, but the LLM will constantly try to push you to use 3rd party and cloud shit throughout. If you don't have strong internal convictions (vision) about what you really want, it's going to take you for a ride.
One other thing to remember is that our economies are incredibly efficient. The statistical mean of all information in sight of the LLMs likely does not represent much of an arbitrage opportunity at scale. Everyone else has access to the same information. This also means that composing these systems in recursive or agentic styles means you aren't gaining anything. You cannot increase the information content of a system by simply creating another instance of the same system and having it argue with itself. There usually exists some simple prompt that makes a multi agent Rube Goldberg contraption look silly.
> I’m basically just the conductor of all those processes.
"Basically" and "just" are doing some heroic weight lifting here. Effectively conducting all of the things an LLM is good at still requires a lot of experience. Making the constraints live together in one happy place is the hard part. This is why some of us call it "engineering".
I have been using the most recent Claude, ChatGPT and Gemini models for coding for a bit more than a year, on a daily basis.
They are pretty good at writing code *after* I thoroughly described what to do, step by step. If you miss a small detail they get loose and the end result is a complete mess that takes hours to clean up. This still requires years of coding experience, planning ahead in head, you won't be able to spare that, or replace developers with LLMs. They are like autocomplete on steroids, that's pretty much it.
Not because the models are random, but because you are mistaking a massive combinatorial search over seen patterns for genuine reasoning. Taleb point was about confusing luck for skill. Dont confuse interpolation for understanding.
You can read a Rust book after years of Java, then go build software for an industry that did not exist when you started. Ask any LLM to write a driver for hardware that shipped last month, or model a regulatory framework that just passed... It will confidently hallucinate. You will figure it out. That is the difference between pattern matching and understanding.
I've worked with a lot of interns, fresh outs from college, overseas lowest bidders, and mediocre engineers who gave up years ago. All over the course of a ~20 year career.
Not once in all that time has anyone PRed and merged my completely unrelated and unfinished branch into main. Except a few weeks ago. By someone who was using the LLM to make PRs.
He didn't understand when I asked him about it and was baffled as to how it happened.
Really annoying, but I got significantly less concerned about the future of human software engineering after that.
Have you used an LLM specifically trained for tool calling, in Claude Code, Cursor or Aider?
They’re capable of looking up documentation, correcting their errors by compiling and running tests, and when coupled with a linter, hallucinations are a non issue.
I don’t really think it’s possible to dismiss a model that’s been trained with reinforcement learning for both reasoning and tool usage as only doing pattern matching. They’re not at all the same beasts as the old style of LLMs based purely on next token prediction of massive scrapes of web data (with some fine tuning on Q&A pairs and RLHF to pick the best answers).
Why would you expect an LLM or even a human to succeed in these cases? “Write a piece of code for a specification that you can’t possibly know about?” That’s why you have to do context engineering, just like you’d provide a reference to a new document to an engineer writing code.
Stop freaking out. Seriously. You're afraid of something completely ridiculous.
It is certainly more eloquent than you regarding software architecture (which was a scam all along, but conversation for another time).
It will find SOME bugs better than you, that's a given.
Review code better than you? Seriously? What you're using and what you consider code review?
Assume I could identify one change broke production and you reviewed the latest commit. I am pinging you and you better answer. Ok, Claude broke production, now what?
Can you begin to understand the difference between you and the generative technology?
When you hop on the call, you will explain to me with a great deal of details what you know about the system you built, and explain decision making and changes over time. You'll tell about what worked and what didn't. You will tell about the risks, behavior and expectations. About where the code runs, it's dependencies, users, usage patterns, load, CPU usage and memory footprint, you could probably tell what's happening without looking at logs but at metrics.
With Claude I get: you're absolutely right! You asked about what it WAS, but I told you about what it WASN'T! MY BAD.
Knowledge requires a soul to experience and this is why you're paid.
We use code rabbit and it's better than practically any human I've worked with at a number of code review tasks, such as finding vulnerabilities, highlighting configuration issues, bad practices, etc. It's not the greatest at "does this make sense here" type questions, but I'd be the one answering those questions anyway.
Yeah, maybe the people I've worked with suck at code reviews, but that's pretty normal.
Not to say your answer is wrong. I think the gist is accurate. But I think tooling will get better at answering exactly the kind of questions you bring up.
Also, someone has to be responsible. I don't think the industry can continue with this BS "AI broke it." Our jobs might devolve into something more akin to a SDET role and writing the "last mile" of novel code the AI can't produce accurately.
Yes, seriously (not OP). Sometimes it's dumb as rocks, sometimes it's frighteningly astute.
I'm not sure at which point of the technology sigmoid curve we find ourselves (2007 iPhone or 2017 iPhone?) but you're doing yourself a disservice to be so dismissive
> Coding AIs design software better than me, review code better than me, find hard-to-find bugs better than me, plan long-running projects better than me, make decisions based on research, literature, and also the state of our projects better than me
His logic is off and his experience is irrelevant because i doesn’t encompass scale to have been exposed to an actual paradigm shifting event. Civilizations and entire technologies have been overturned so he can’t say it won’t happen this time.
What we do know is this. If AI keeps improving at the current rate it’s improving then it will eventually hit a point where we don’t need software engineers. That’s inevitable. The way for it to not happen is for this technology to hit an impenetrable wall.
This wave of AI came so fast that there are still stubborn people who think it’s a stochastic parrot. They missed the boat.
Yeah, it makes me wonder whether I should start learning to be a carpenter or something. Those who either support AI or thinks "it's all bullshit" cite a lack of evidence for humans truly being replaced in the engineering process, but that's just the thing; the unprecedented levels of uncertainty make it very difficult to invest one's self in the present, intellectually and emotionally. With the current state of things, I don't think it's silly to wonder "what's the point" if another 5 years of this trajectory is going to mean not getting hired as a software dev again unless you have a PhD and want to work for an AI company.
What doesn't help is that the current state of AI adoption is heavily top-down. What I mean is the buy-in is coming from the leadership class and the shareholder class, both of whom have the incentive to remove the necessary evil of human beings from their processes. Ironically, these classes are perhaps the least qualified to decide whether generative AI can replace swathes of their workforce without serious unforeseen consequences. To make matters worse, those consequences might be as distal as too many NEETs in the system such that no one can afford to buy their crap anymore; good luck getting anyone focused on making it to the next financial quarter to give a shit about that. And that's really all that matters at the end of the day; what leadership believes, whether or not they are in touch with reality.
Where the hell was all this fear when the push for open source everything got fully underway? When entire websites were being spawned and scaffolded with just a couple lines of code? Do we not remember all those impressive tech demos of developers doing massive complex thing with "just one line of code"? How did we not just write software for every kind of software problem that could exist by now?
How has free code, developed by humans, become more available than ever and yet somehow we have had to employ more and more developers? Why didn't we trend toward less developers?
It just doesn't make sense. AI is nothing but a snippet generator, a static analyzer, a linter, a compiler, an LSP, a google search, a copy paste from stackoverflow, all technologies we've had for a long time, all things developers used to have to go without at some point in history.
I agree the ELIZA effect is strong, additionally I think it is some kind of natural selection.
I feel like LLM's are specifically selected to impress people that have a lot of influence. People like investors and CEO's. Because a "AI" that does not impress this section of the population does not get adopted widely.
This is one of the reasons I think AI will never really be an expert as it does not need to be. It only needs to adopt a skill (for example coding) to pass the examination of the groups that decide if it is to be used. It needs to be "good enough to pass".
I read a book called "Blood in the machine". It's the history of the Luddites.
It really put everything into perspective to where we are now.
Pre-industrial revolution whole towns and families built clothing and had techniques to make quality clothes.
When the machines came out it wasn't overnight but it wiped out nearly all cottage industries.
The clothing it made wasn't to the same level of quality, but you could churn it out faster and cheaper. There was also the novelty of having clothes from a machine which later normalised it.
We are at the beginning of the end of the cottage industry for developers.
Luddism arose in response to weaving machines, not garment-making machines. The machines could weave a piece of cloth that still had to be cut and sewn by hand into a garment. Weaving the cloth was by far the most time consuming part of making the clothing.
Writing code is not at all the most time consuming part of software development.
If you used the car as an analogy instead, it would make more sense to me. There were car craftsmen in Europe that Toyota displaced almost completely. And software is more similar to cars in that it needs maintenance and if it breaks down, large consequences like death and destruction and/or financial loss follows.
If llms can churn out software like Toyota churns out cars, AND do maintenance on it, then the craftsmen (developers of today) are going to be displaced.
We had "free clothes" for years, decades now. I don't mean cheap I mean literally free, as in $0.0 software. Cheaper software isn't new.
Also there are still clothe designers, fashion runways, and expensive Patagonia vests today. The clothing industry is radically different from back then but it's definitely not gone.
It didn't kill everything. Some survived but not to the extent that it was.
> The clothing industry is radically different from back then but it's definitely not gone.
Small towns had generations of people who had learned skills in making clothing / yarn. To do the work you needed years of experience and that's all you knew.
Once the industrial revolution hit they hired low skilled workers that could be dumped at a moments notice. It made whole villages destitute. Some survived, but the far majority became poor.
That was one industry. We now have AI at a point to wipe out multiple industries to a similar scale.
I posted elsewhere, but you are looking at the wrong part of the chain.
We have cheap (or free) software for large markets, and certain small markets where software developers with hobbies have made something. If every niche that will never be able to afford a large 6-figure custom software could get slop software for an affordable price, then that establishes a foot-hold for working its way up the quality ladder.
Just like the pro-AI articles, it reads to me like a sales pitch. And the ending only adds to it: the author invites to hire companies to contract him for training.
I would only be happy if in the end the author turns out to be right.
But as the things stand right now, I can see a significant boost to my own productivity, which leads me to believe that fewer people are going to be needed.
When coal powered engines became more efficient, demand for coal went UP. It went up because vastly more things could now be cost effectively be coal-powered.
I can see a future where software development goes the same way. My wife works in science and I see all kinds of things in a casual observation of her work that could be made more efficient with good software support. But not by enough to pay six-figures per year for multiple devs to create it. So it doesn’t get done and her work and the work of tens of thousands like her around the world is less efficient as a result.
In a world where development is even half as expensive, many such tasks become approachable. If it becomes a third or quarter as expensive, even more applications are now profitable.
I think far more people will be doing something that creates the outcomes that are today created by SWEs manually coding. I doubt it will be quite as lucrative for the median person doing it, but I think it will still be well above the median wage and there will be a lot of it.
Many HN users may point to Jevons paradox, I would like to point out that it may very well work up until the point that it doesn't. After all a chicken has always seen the farmer as benevolent provider of food, shelter and safety, that is until of course THAT day when he decides he doesn't.
>WYSIWYG, drag-and-drop editors like Visual Basic and Delphi were going to end the need for programmers.
VB6 and Delphi were the best possible cognitive impedance match available for domain experts to be able to whip up something that could get a job done. We haven't had anything nearly as productive in the decades since, as far as just letting a normie get something done with a computer.
You'd then hire an actual programmer to come in and take care of corner cases, and make things actually reliable, and usable by others. We're facing a very similar situation now, the AI might be able to generate a brittle and barely functional program, but you're still going to have to have real programmers make it stable and usable.
> Edgar Dijkstra called it nearly 50 years ago: we will never be programming in English, or French, or Spanish. Natural languages have not evolved to be precise enough and unambiguous enough. Semantic ambiguity and language entropy will always defeat this ambition.
This is the most important quote for any AI coding discussion.
Anyone that doesn't understand how the tools they use came to be is doomed to reinvent them.
> The folly of many people now claiming that “prompts are the new source code”,
These are the same people that create applications in MS Excel.
In aviation safety, there is a concept of "Swiss cheese" model, where each successful layer of safety may not be 100% perfect, but has a different set of holes, so overlapping layers create a net gain in safety metrics.
One can treat current LLMs as a layer of "cheese" for any software development or deployment pipeline, so the goal of adding them should be an improvement for a measurable metric (code quality, uptime, development cost, successful transactions, etc).
Of course, one has to understand the chosen LLM behaviour for each specific scenario - are they like Swiss cheese (small numbers of large holes) or more like Havarti cheese (large number of small holes), and treat them accordingly.
Interesting concept, but as of now we don't apply this technologies as a new compounding layer.
We are not using them after the fact we constructed the initial solution. We are not ingesting the code to compare against specs. We are not using them to curate and analyze current hand written tests(prompt: is this test any good? assistant: it is hot garbage, you are inferring that expected result equals your mocked result).
We are not really at this phase yet. Not in general, not intelligently.
But when the "safe and effective" crowd leave technology we will find good use cases for it, I am certain (unlike uml, VB and Delphi)
LLMs are Kraft Singles. Stuff that only kind of looks like cheese. Once you know it's in there, someone has to inspect, and sign-off on, the entire wheel for any credible semblance of safety.
> One can treat current LLMs as a layer of "cheese" for any software development or deployment pipeline
It's another interesting attempt at normalising the bullshit output by LLMs, but NO. Even with the entshittified Boeing, the aviation industry safety and reliability records, are far far far above deterministic software (know for a lot of un-reliability itself), and deterministic, B2C software to LLMs in turn is what Boeing and Airbus software and hardware reliablity are for the B2C software...So you cannot even begin to apply aviation industry paradigms to the shit machines, please.
I understand the frustration, but factually it is not true.
Engines are reliable to about 1 anomaly per million flight hours or so, current flight software is more reliable, on order of 1 fault per billion hours. In-flight engine shutdowns are fairly common, while major software anomalies are much rarer.
I used LLMs for coding and troubleshooting, and while they can definitely "hit" and "miss", they don't only "miss".
The way I see it, the problem with LLMs is the same as with self-driving cars: trust.
You can ask an LLM to implement a feature, but unless you're pretty technical yourself, how will you know that it actually did what you wanted? How will you know that it didn't catastrophically misunderstand what you wanted, making something that works for your manual test cases, but then doesn't generalize to what you _actually_ want to do?
People have been saying we'll have self-driving cars in five years for fifteen years now. And even if it looks like it might be finally happening now, it's going glacially slow, and it's one run-over baby away from being pushed back another ten years.
People used to brush away this argument with plain statistics. Supposedly, if the death statistics is below the average human, you are supposed to lean back and relax. I never bought this one. Its like saying LLMs write better texts then the average huamn can, so you are supposed to use it, no matter how much you bring to the table.
This time it actually is different.
HN might not think so, but HN is really skewed towards more senior devs, so I think they're out of touch with what new grads are going through.
It's awful.
What is it that new grads are going through? If you are referring to difficulty finding a job, keep in mind that there is both an economic downturn and an over-hiring correction happening in the industry right now. I imagine the AI industry is indeed having an impact in how management is behaving, but I would not yet bet on AI actually replacing developers jobs holistically.
Here’s how I see it. Writing code or building software well requires knowledge of logic, data structures, reliable messaging, and separation of concerns.
You can learn a foreign language just fine, but if you mangle the pronunciation, no one will talk to you. Same thing with hacking at software without understanding the above elements. Your software will be mangled and no one will use it.
the quality of how maintainable the source code is has no bearing on how a user perceives the software's usefulness.
If the software serves a good function for the user, they will use it, regardless of how badly the datastructures are. Of course, good function also means reliability from the POV of the user. If your software is so bad that you lose data, obviously no one will use it.
But you are conflating the maintainability and sensibilities of clean tools, clean code and clean workspaces, with output.
A messy carpentry workshop can still produce great furniture.
As someone having watched AI systems being good enough to replace jobs like content creation on CMS, this is being in denial.
Yes software developer are still going to be need, except much fewer of us, exactly like fully automated factories still need a few humans around, to control and build the factory in first place.
Am puzzled why so many on HN cannot see this. I guess most users on HN are employed? Your employers - let me tell you - are positively salivating at the prospect of firing you. The better LLM's get the fewer of you will be needed.
Denial, like those factory workers at the first visit from the automation company, each one hoping they are the ones elected to stay and overwatch the robots.
I have seen projects where translator teams got reduced, asset creation teams, devops head count, support teams on phone lines,...
It is all about how to do more with less, now with AI help as well.
I see it as pure deterministic logic being contaminated by probabilistic logic at higher layers where human interaction happens. Seeking for human comfort by forcing computers to adapt to the human languages. Building adapters that can allow humans to stay in their comfort zone instead of dealing with the sharp-edged computer interfaces.
At the end, I don't see it going beyond being a glorified form-assistant who can search internet for answers and summarize. That boils down to chat bots that will remain and become part of every software component that ever need to interface with humans.
Agent stuff is just a fluff that is providing hype-cushion around chat bots and will go away with hype cycle.
> The hard part of computer programming isn't expressing what we want the machine to do in code. The hard part is turning human thinking -- with all its wooliness and ambiguity and contradictions -- into computational thinking that is logically precise and unambiguous, and that can then be expressed formally in the syntax of a programming language.
> That was the hard part when programmers were punching holes in cards. It was the hard part when they were typing COBOL code. It was the hard part when they were bringing Visual Basic GUIs to life (presumably to track the killer's IP address). And it's the hard part when they're prompting language models to predict plausible-looking Python.
> The hard part has always been – and likely will continue to be for many years to come – knowing exactly what to ask for.
I don't agree with this:
> To folks who say this technology isn’t going anywhere, I would remind them of just how expensive these models are to build and what massive losses they’re incurring. Yes, you could carry on using your local instance of some small model distilled from a hyper-scale model trained today. But as the years roll by, you may find not being able to move on from the programming language and library versions it was trained on a tad constraining.
Some of the best Chinese models (which are genuinely competitive with the frontier models from OpenAI / Anthropic / Gemini) claim to have been trained for single-digit millions of dollars. I'm not at all worried that the bubble will burst and new models will stop being trained and the existing ones will lose their utility - I think what we have now is a permanent baseline for what will be available in the future.
> claim to have been trained for single-digit millions of dollars
Weren't these smaller models trained by distillation from larger ones, which therefore have to exist in order to do it? Are there examples of near state of the art foundation models being trained from scratch in low millions of dollars? (This is a genuine question, not arguing. I'm not knowledgeable in this area.)
The first part is surely true if you change it to "the hardEST part," (I'm a huge believer in "Programming as Theory Building"), but there are plenty of other hard or just downright tedious/expensive aspects of software development. I'm still not fully bought in on some of the AI stuff—I haven't had a chance to really apply an agentic flow to anything professional, I pretty much always get errors even when one-shotting, and who knows if even the productive stuff is big-picture economical—but I've already done some professional "mini projects" that just would not have gotten done without an AI. Simple example is I converted a C# UI to Java Swing in less than a day, few thousand lines of code, simple utility but important to my current project for <reasons>. Assuming tasks like these can be done economically over time, I don't see any reason why small and medium difficulty programming tasks can't be achieved efficiently with these tools.
maybe not the MOST valuable part of prompting an LLM during a task, but one of them, is defining the exact problem in precise language. i dont just blindly turn to an LLM without understanding the problem first, but i do find Claude is better than a cardboard cutout of a dog
Indeed, while DeepSeek 3.2 or GLM 4.7 are not Opus 4.5 quality, they are close enough that I could _get by_ because they're not that far off, and are about where I was with Sonnet 3.5 or Sonnet 4 a few months ago.
I'm not convinced DeepSeek is making money hosting these, but it's not that far off from it I suspect. They could triple their prices and still be cheaper than Anthropic is now.
There is a guaranteed cap on how far LLM based AI models can go. Models improve by being trained on better data. LLMs being used to generate millions of lines of sloppy code will substantially dilute the pool of good training data. Developers moving over to AI based development will cease to grow and learn - producing less novel code.
The massive increase in slop code and loss of innovation in code will establish an unavoidable limit on LLMs.
Maybe we'll train the llms in our ways of using them, and the next generation of coding assistants will be another layer inbetween us and the code. You talk to the chief engineer llm who in turn talks to its cadre of claude code instances running in virtual tmux. \hj?
But they're not just training off code and its use, but off a corpus general human knowledge in written form.
I mean, in general not only do they have all of the crappy PHP code in existence in their corpus but they also have Principia Mathematica, or probably The Art of Computer Programming. And it has become increasingly clear to me that the models have bridged the gap between "autocomplete based on code I've seen" to some sort of distillation of first order logic based on them just reading a lot of language... and some fuzzy attempt at reasoning that came out of it.
Plus the agentic tools driving them are increasingly ruthless at wringing out good results.
That said -- I think there is a natural cap on what they can get at as pure coding machines. They're pretty much there IMHO. The results are usually -- I get what I asked for, almost 100%, and it tends to "just do the right thing."
I think the next step is actually to actually make it scale and make it profitable but also...
fix the tools -- they're not what I want as an engineer. They try to take over, and they don't put me in control, and they create a very difficult review and maintenance problem. Not because they make bad code but because they make code that nobody feels responsible for.
That is a naive assumption. Or rather multiple naive assumptions: Developers mostly don’t move over to AI development, but integrate it into their workflow. Many of them will stay intellectually curious and thus focus their attention elsewhere; I’m not convinced they will just suddenly all stagnate.
Also, training data isn’t just crawled text from the internet anymore, but also sourced from interactions of millions of developers with coding agents, manually provided sample sessions, deliberately generated code, and more—there is a massive amount of money and research involved here, so that’s another bet I wouldn’t be willing to make.
In past cases of automation, quantity was the foot-in-the-door and quality followed. Early manufactured items were in many cases inferior to hand-built items, but one was affordable and the other not.
Software is incredibly expensive and has made up for it with low marginal costs. Many small markets could potentially be served by slop software, and it's better than what they would have otherwise gotten (which is nothing).
This blurb is the whole axiom on which the author built their theory. In my opinion it is not accurate, to say the least. And I say this as someone who is still underwhelmed by current AI for coding.
Totally delusional. The article does not even try to figure out why any of this happened.
In all of the cases the main prediction that was made came true. The cost, especially the human cost, of developing some piece of software dramatically decreased. The only reason why the amount of programmers needed still rose was because the amount of software needed rose faster.
Clearly that trend will not hold forever.
>The hard part of computer programming isn’t expressing what we want the machine to do in code. The hard part is turning human thinking – with all its wooliness and ambiguity and contradictions – into computational thinking that is logically precise and unambiguous, and that can then be expressed formally in the syntax of a programming language.
And there is exactly one single technology which has ever been able to do this task, which is LLMs. Not addressing the elephant in the room, which is that an LLM can actually take such instructions and produce something meaningful with it, just makes the whole article worthless.
Everything in this article is just inverse special pleading. Since the last N times, the most enthusiastic predictions did not come true, this time only minor changes can happen. If LLMs are only a revolution on the scale of fast interpreted languages (which have significantly impacted what a small team is capable of delivering in terms of complexity), then they will drastically impact most of the software industry.
If these changes happen, and simultaneously the rate at which software is demanded does not also increase (why would it?), then the implications will be extremely serious. Especially if you are not a developer in an established position.
After working with agent-LLMs for some years now, I can confirm that they are completely useless for real programming.
They never helped me solve complex problems with low-level libraries. They can not find nontrivial bugs. They don't get the logic of interwoven layers of abstractions.
LLMs pretend to do this with big confidence and fail miserably.
For every problem I need to turn my brain to ON MODE and wake up, the LLM doesn't wake up.
It surprised me how well it solved another task: I told it to set up a website with some SQL database and scripts behind it. When you click here, show some filtered list there. Worked like a charm. A very solved problem and very simple logic, done a zillion times before. But this saved me a day of writing boilerplate.
I agree that there is no indication that LLMs will ever cross the border from simple-boilerplate-land to understanding-complex-problems-land.
People are saying Codex 5.2 fullsolved crypto challenges in 39C3 CTF last weekend.
Three months ago I would have agreed with you, but anecdotal evidence says Codex 5.2 and Opus 4.5 are finally there.
>After working with agent-LLMs for some years now, I can confirm that they are completely useless for real programming.
>They never helped me solve complex problems with low-level libraries. They can not find nontrivial bugs. They don't get the logic of interwoven layers of abstractions.
>LLMs pretend to do this with big confidence and fail miserably.
This is true for most developers as well. The mean software developer, especially if you outsource, has failure modes worse than any LLM and round-trip time is not seconds but days.
The promise of LLMs is not that they solve the single most difficult tasks for you instantly, but that they do the easy stuff well enough that they replace offshore teams.
> The promise of LLMs is not that they solve the single most difficult tasks for you instantly, but that they do the easy stuff well enough that they replace offshore teams.
But that's exactly the *promise* of LLMs by the hypepeople behind it.
1 reply →
I really really want this to be true. I want to be relevant. I don’t know what to do if all those predictions are true and there is no need (or very little need) for programmers anymore.
But something tells me “this time is different” is different this time for real.
Coding AIs design software better than me, review code better than me, find hard-to-find bugs better than me, plan long-running projects better than me, make decisions based on research, literature, and also the state of our projects better than me. I’m basically just the conductor of all those processes.
Oh, and don't ask about coding. If you use AI for tasks above, as a result you'll get very well defined coding task definitions which an AI would ace.
I’m still hired, but I feel like I’m doing the work of an entire org that used to need twenty engineers.
From where I’m standing, it’s scary.
More than any other effect they have LLMs breed something called "learned helplessness". You just listed a few things it may stay better than you at, and a few things that it is not better than you at and never will be.
Planning long running projects and deciding are things only you can do well!! Humans manage costs. We look out for our future. We worry. We have excitement, and pride. It wants you to think none of these things matter if course, because it doesn't have them. It says plausible things at random, basically. It can't love, it can't care, it won't persist.
WHATEVER you do don't let it make you forget that it's a bag of words and you are someing almost infinitely more capable
> Coding AIs design software better than me, review code better than me, find hard-to-find bugs better than me, plan long-running projects better than me, make decisions based on research, literature, and also the state of our projects better than me.
That is just not true, assuming you have a modicum of competence (which I assume you do). AIs suck at all these tasks; they are not even as good as an inexperienced human.
For all we know, you both could comparing using a Nokia 3310 and a workstation PC based on the hardware, but you both just say "this computer is better than that computer".
There are a ton of models out there, ran in a ton of different ways, that can be used in different ways with different harnesses, and people use different workflows. There is just so many variables involved, that I don't think it's neither fair nor accurate for anyone to claim "This is obviously better" or "This is obviously impossible".
I've been in situations where I hit my head against some hard to find bug for days, then I put "AI" (but what? No one knows) to it and it solves it in 20 minutes. I've also asked "AI" to do trivial work that it still somehow fucked up, even if I could probably have asked a non-programmer friend to do it and they'd be able to.
The variance is great, and the fact that system/developer/user prompts matter a lot for what the responses you get, makes it even harder to fairly compare things like this without having the actual chat logs in front of you.
LLMs generate the most likely code given the problem they're presented and everything they've been trained on, they don't actually understand how (or even if) it works. I only ever get away with that when I'm writing a parser.
5 replies →
Depends on how he defined "better". If he uses the word "better" to mean "good enough to not fail immediately, and done in 1/10th of the time", then he's correct.
This reads like shilling/advertisement.. Coding AIs are struggling for anything remotely complex, make up crap and present it as research, write tests that are just "return true", and won't ever question a decision you make.
Those twenty engineers must not have produced much.
I think part of what is happening here is that different developers on HN have very different jobs and skill levels. If you are just writing a large volume of code over and over again to do the same sort of things, then LLMs probably could take your job. A lot of people have joined the industry over time, and it seems like the intelligence bar moved lower and lower over time, particularly for people churning out large volumes of boilerplate code. If you are doing relatively novel stuff, at least in the sense that your abstractions are novel and the shape of the abstraction set is different from the standard things that exist in tutorials etc online, then the LLM will probably not work well with your style.
So some people are panicking and they are probably right, and some other people are rolling their eyes and they are probably right too. I think the real risk is that dumping out loads of boilerplate becomes so cheap and reliable that people who can actually fluently design coherent abstractions are no longer as needed. I am skeptical this will happen though, as there doesn’t seem to be a way around the problem of the giant indigestible hairball (I.e as you have more and more boilerplate it becomes harder to remain coherent).
17 replies →
No it doesn’t read like shilling and advertisement, it’s tiring hearing people continually dismiss coding agents as if they have not massively improved and are driving real value despite limitations and they are only just getting started. I’ve done things with Claude I never thought possible for myself to do, and I’ve done things where Claude made the whole effort take twice as long and 3x more of my time. It’s not like people are ignoring the limitations, it’s that people can see how powerful the already are and how much more headroom there is even with existing paradigms not to mention the compute scaling happening in 26-27 and the idea pipeline from the massive hoarding of talent.
51 replies →
I would say while LLMs do improve productivity sometimes, I have to say I flatly cannot believe a claim (at least without direct demonstration or evidence) that one person is doing the work of 20 with them in december 2025 at least.
I mean from the off, people were claiming 10x probably mostly because it's a nice round number, but those claims quickly fell out of the mainstream as people realised it's just not that big a multiplier in practice in the real world.
I don't think we're seeing this in the market, anywhere. Something like 1 engineer doing the job of 20, what you're talking about is basically whole departments at mid sized companies compressing to one person. Think about that, that has implications for all the additional management staff on top of the 20 engineers too.
It'd either be a complete restructure and rethink of the way software orgs work, or we'd be seeing just incredible, crazy deltas in output of software companies this year of the type that couldn't be ignored, they'd be impossible to not notice.
This is just plainly not happening. Look, if it happens, it happens, 26, 27, 28 or 38. It'll be a cool and interesting new world if it does. But it's just... not happened or happening in 25.
7 replies →
My experience is that you get out what you put in. If you have a well-defined foundation, AI can populate the stubs and get it 95% correct. Getting to that point can take a bit of thought, and AI can help with that, too, but if you lean on it too much, you'll get a mess.
And of course, getting to the point where you can write a good foundation has always been the bulk of the work. I don't see that changing anytime soon.
Ok, let's say the 20 devs claim is false [1]. What if it's 2? I'd still learn and use the tech. Wouldn't you?
[1] I actually think it might be true for certain kinds of jobs.
2 replies →
I'd be willing to give you access to the experiment I mentioned in a separate reply (have a github repo), as far as the output that you can get for a complex app buildout.
Will admit It's not great (probably not even good) but it definitely has throughput despite my absolute lack of caring that much [0]. Once I get past a certain stage I am thinking of doing an A-B test where I take an earlier commit and try again while paying more attention... (But I at least want to get where there is a full suite of UOW cases before I do that, for comparison's sake.)
> Those twenty engineers must not have produced much.
I've been considered a 'very fast' engineer at most shops (e.x. at multiple shops, stories assigned to me would have a <1 multiplier for points[1])
20 is a bit bloated, unless we are talking about WITCH tier. I definitely can get done in 2-3 hours what could take me a day. I say it that way because at best it's 1-2 hours but other times it's longer, some folks remember the 'best' rather than median.
[0] - It started as 'prompt only', although after a certain point I did start being more aggressive with personal edits.
[1] - IDK why they did it that way instead of capacity, OTOH that saved me when it came to being assigned Manual Testing stories...
2 replies →
Post model
I was a chef in Michelin-starred restaurants for 11 years. One of my favorite positions was washing dishes. The goal was always to keep the machine running on its 5-minute cycle. It was about getting the dishes into racks, rinsing them, and having them ready and waiting for the previous cycle to end—so you could push them into the machine immediately—then getting them dried and put away after the cycle, making sure the quality was there and no spot was missed. If the machine stopped, the goal was to get another batch into it, putting everything else on hold. Keeping the machine running was the only way to prevent dishes from piling up, which would end with the towers falling over and breaking plates. This work requires moving lightning fast with dexterity.
AI coding agents are analogous to the machine. My job is to get the prompts written, and to do quality control and housekeeping after it runs a cycle. Nonetheless, like all automation, humans are still needed... for now.
If it requires an expert engineer/dishwasher to keep the flow running perfectly, the human is the bottleneck in the process. This sounds a lot more like the past before AI to me. What AI does is just give you enough dishes that they don’t need to be washed at all during dinner service. Just let them pile up dirty or throw them away and get new dishes tomorrow it’s so immaterial to replace that washing them doesn’t always make sense. But if for some reason you do want to reuse them, then, it washes and dries them for you too. You just look over things at the end and make sure they pass your quality standards. If they left some muck on a plate or lipstick on a cup, just tell it not to let that happen again and it won’t. So even your QC work gets easier over time. The labor needed to deal with dirty dishes is drastically reduced in any case.
Humans are still needed, but they just got down-skilled.
1 reply →
> humans are still needed... for now
"AI" doesn't have a clue what to do on its own. Humans will always be in the loop, because they have goals, while the AI is designed to placate and not create.
The amount of "AI" garbage I have to sift through to find one single gem is about the same or more work than if I had just coded it myself. Add to that the frustration of dealing with a compulsive liar, and it's just a fucking awful experience for anyone that actually can code.
> I’m basically just the conductor of all those processes.
a car moves faster than you, can last longer than you, and can carry much more than you. But somehow, people don't seem to be scared of cars displacing them(yet)? Perhaps autodriving would in the near future, but there still needs to be someone making decisions on how best to utilize that car - surely, it isn't deciding to go to destination A without someone telling them.
> I feel like I’m doing the work of an entire org that used to need twenty engineers.
and this is great. A combine harvester does the work of what used to be an entire village for a week in a day. More output for less people/resources expended means more wealth produced.
> a car moves faster than you, can last longer than you, and can carry much more than you. But somehow, people don't seem to be scared of cars displacing them(yet)?
People whose life were based around using horses for transportation were very scared of cars replacing them though, and correctly so, because horses for transportation is something people do for leisure today, not necessity. I feel like that's a more apt analogy than comparing cars to any human.
> More output for less people/resources expended means more wealth produced.
This is true, but it probably also means that this "more wealth produced" will be more concentrated, because it's easier to convince one person using AI that you should have half of the wealth they produce, rather than convincing 100 people you should have half of what they produce. From where I'm standing, it seems to have the same effects (but not as widespread or impactful, yet) as industrialization, that induced that side-effect as well.
1 reply →
And parent is scared of being made redundant by AI because they need their job to pay for their car, insurance, gas and repairs.
> a car moves faster than you, can last longer than you, and can carry much more than you. But somehow, people don't seem to be scared of cars displacing them(yet)?
???
Cars replaced horses, not people.
In this scenario you are the horse.
2 replies →
I think I've been using AI wrong. I can't understand testimonies like this. Most times I try to use AI for a task, it is a shitshow, and I have to rewrite everything anyway.
Have you tried Opus 4.5 (or similar recent models)? With Claude code 2, it's actually harder to mess things up IMO
1 reply →
Try Claude. And partner with it on building something complex.
Same. Seems to be the never ending theme of AI.
Do you tell AI the patterns/tools/architecture you want? Telling agents to "build me XYZ, make it gud!" is likely to precede a mess, telling it to build a modular monolith using your library/tool list, your preferred folder structure, other patterns/algorithms you use, etc will end you up with something that might have some minor style issues or not be perfectly canonical, but will be approximately correct within a reasonable margin, or is within 1-2 turns of being so.
You have to let go of the code looking exactly a certain way, but having code _work_ a certain way at a coarse level is doable and fairly easy.
2 replies →
I don’t know about right/wrong. You need to use the tools that make you productive. I personally find that in my work there are dozens of little scripts or helper functions that accelerate my work. However I usually don’t write them because I don’t have the time. AI can generate these little scripts very consistently. That accelerates my work. Perhaps just start simple.
1 reply →
how much time/effort have you put in to educate yourself about how they work, what they excel at, what they suck at, what is your responsibility when you use them…? this effort is directly proportional to how well they will serve you
It's definitely scary in a way.
However I'm still finding a trend even in my org; better non-AI developers tend to be better at using AI to develop.
AI still forgets requirements.
I'm currently running an experiment where I try to get a design and then execute on an enterprise 'SAAS-replacement' application [0].
AI can spit forth a completely convincing looking overall project plan [1] that has gaps if anyone, even the AI itself, tries to execute on the plan; this is where a proper, experienced developer can step in at the right steps to help out.
IDK if that's the right way to venture into the brave new world, but I am at least doing my best to be at a forefront of how my org is using the tech.
[0] - I figured it was a good exercise for testing limits of both my skills prompting and the AI's capability. I do not expect success.
They do all those things you've mentioned more efficiently than most of us, but they fall woefully short as soon as novelty is required. Creativity is not in their repertoire. So if you're banging out the same type of thing over and over again, yes, they will make that work light and then scarce. But if you need to create something niche, something one-off, something new, they'll slip off the bleeding edge into the comfortable valley of the familiar at every step.
I choose to look at it as an opportunity to spend more time on the interesting problems, and work at a higher level. We used to worry about pointers and memory allocation. Now we will worry less and less about how the code is written and more about the result it built.
Take food for example. We don't eat food made by computers even though they're capable of making it from start to finish.
Sure we eat carrots probably assisted by machines, but we are not eating dishes like protein bars all day every day.
Our food is still better enjoyed when made by a chef.
Software engineering will be the same. No one will want to use software made by a machine all day every day. There are differences in the execution and implementation.
No one will want to read books entirely dreamed up by AI. Subtle parts of the books make us feel something only a human could have put right there right then.
No one will want to see movies entirely made by AI.
The list goes on.
But you might say "software is different". Yes but no, in the abundance of choice, when there will be a ton of choice for a type of software due to the productivity increase, choice will become more prominent and the human driven software will win.
Even today we pick the best terminal emulation software because we notice the difference between exquisitely crafted and bloated cruft.
5 replies →
> So if you're banging out the same type of thing over and over again, yes, they will make that work light and then scarce.
The same thing over and over again should be a SaaS, some internal tool, or a plugin. Computers are good at doing the same thing over and over again and that's what we've been using them for
> But if you need to create something niche, something one-off, something new, they'll slip off the bleeding edge into the comfortable valley of the familiar at every step.
Even if the high level description of a task may be similar to another, there's always something different in the implementation. A sports car and a sedan have roughly the same components, but they're not engineered the same.
> We used to worry about pointers and memory allocation.
Some still do. It's not in every case you will have a system that handle allocations and a garbage collector. And even in those, you will see memory leaks.
> Now we will worry less and less about how the code is written and more about the result it built.
Wasn't that Dreamweaver?
I think your image of LLMs is a bit outdated. Claude Code with well-configured agents will get entirely novel stuff done pretty well, and that’s only going to get better over time.
I wouldn’t want to bet my career on that anyway.
1 reply →
My experience with these tools is far and away no where close to this.
If you're really able to do the work of a 20 man org on your own, start a business.
I am sorry to say you are not a good programmer.
I mean, AIs can drop something fast the same way you cannot beat a computer at adding or multiplying.
After that, you find mistakes, false positives, code that does not work fully, and the worse part is the last one: code that does not work fully but also, as a consequence, that you do NOT understand yet.
That is where your time shrinks: now you need to review it.
Also, they do not design systems better. Maybe partial pieces. Give them something complex and they will hallucinate worse solutions than what you already know if you have, let us say, over 10 years of experience programming in a language (or mabye 5).
Now multiply this unreliability problem as the code you "AI-generate" grows.
Now you have a system you do not know if it is reliable and that you do not understand to modify. Congrats...
I use AI moderately for the tasks is good at: generate some scripts, give me this small typical function amd I review it.
Review my code: I will discard part of your mistakes and hallucinations as a person that knows well the language and will find maybe a few valuable things.
Also, when reviewing and found problems in my code I saw that the LLMs really need to hallucinate errors that do not exist to justify their help. This is just something LLMs seem to not be accurate at.
Also, when problems go a bit more atypical or past a level of difficulty, it gets much more unreliable.
All in all: you are going to need humans. I do not know how many, I do not know how much they will improve. I just know that they are not reliable and this "generate-fast-unreliable vs now I do not know the codebase" is a fundamental obstacle that I think it is if not very difficult, impossible to workaround.
This is not how I think about it. Me and the coding assistant is better then me or the coding assistant separately.
For me its not about me or the coding assistant, its me and the coding assistant. But I'm also not a professional coder, i dont identify as a coder. I've been fiddling with programming my whole life, but never had it as title, I've more worked from product side or from stakeholder side, but always got more involved, as I could speak with the dev team.
This also makes it natural for me to work side-by-side with the coding assistant, compared maybe to pure coders, who are used to keeping the coding side to themselves.
Notice who makes these predictions that programmers will become irrelevant.
> Coding AIs design software better than me, review code better than me, find hard-to-find bugs better than me, plan long-running projects better than me, make decisions based on research, literature, and also the state of our projects better than me.
They don't do any of that better than me; they do it poorer and faster, but well enough for most of the time.
>> Coding AIs design software better than me
Absolutely flat out not true.
I'm extremely pro-faster-keyboard, i use the faster keyboards in almost every opportunity i can, i've been amazed by debugging skills (in fairness, i've also been very disappointed many times), i've been bowled over by my faster keyboard's ability to whip out HTML UI's in record time, i've been genuinely impressed by my faster keyboard's ability to flag flaws in PRs i'm reviewing.
All this to say, i see lots of value in faster keyboard's but add all the prompts, skills and hooks you like, explain in as much detail as you like about modularisation, and still "agents" cannot design software as well as a human.
Whatever the underlying mechanism of an LLM (to call it a next token predictor is dismissively underselling its capabilities) it does not have a mechanism to decompose a problem into independently solvable pieces. While that remains true, and i've seen zero precursor of a coming change here - the state of the art today is equiv to having the agent employ a todo list - while this remains true, LLMs cannot design better than humans.
There are many simple CRUD line of business apps where they design well enough (well more accurately stated, the problem is small/simple enough) that it doesn't matter about this lack of design skill in LLMs or agents. But don't confuse that for being able to design software in the more general use case.
Exactly, for the thing that has been done in Github 10000x times over, LLMs are pretty awesome and they speed up your job significantly (it's arguable if you would be better off using some abstraction already built if that's the case).
But try to do something novel and... they become nearly useless. Not like anything particularly difficult, just something that's so niche it's never been done before. It will most likely hallucinate some methods and call it a day.
As a personal anecdote, I was doing some LTSpice simulations and tried to get Claude Sonnet to write a plot expression to convert reactance to apparent capacitance in an AC sweep. It hallucinated pretty much the entire thing, and got the equation wrong (assumed the source was unit intensity, while LTSpice models AC circuits with unit voltage. This surely is on the internet, but apparently has never been written alongside the need to convert an impedance to capacitance!).
As of today NONE of the known AI codebots can solve correctly ANY of the 50+ programming exercises we use to interview fresh grads or summer interns. NONE! Not even level 1 problems that can be solved in fewer than 20 lines of code with a bit of middle school math.
I promise you that I can show you how to reliably solve any of them using any of the latest OpenAI models. Email me if you want proof; josh.d.griffith at gmail
2 replies →
After 25+ years in this field, having interviewed ~100 people for both my startup and other companies, I'm having a hard time believing this. You're either in an extremely niche field (such as to make your statement irrelevant to 99.9% of the industry), or it's hyperbole, or straight up bs.
Interviewing is an art, and IME "gotcha" types of questions never work. You want to search for real-world capabilities, and like it or not the questions need to match those expectations. If you're hiring summer interns and the SotA models can't solve those questions, then you're doing something wrong. Sorry, but having used these tools for the past three years this is extremely ahrd to believe.
I of course understand if you can't, but sharing even one of those questions would be nice.
That's kind of the point of the article, though.
Sure LLMs can churn out code, and they sort of work for developers who already understand code and design, but what happens when that junior dev with no hard experience builds their years of experience with LLMs?
Over time those who actually understand what the LLMs are doing and how to correct the output are replaced by developers who've never learned the hard lessons of writing code line by line. The ability to reason about code gets lost.
This points to the hard problem that the article highlights. The hard problem of software is actually knowing how to write it, which usually takes years, sometimes up to a decade of real experience.
Any idiot can churn out code that doesn't work. But working, effective software takes a lot of skill that LLMs will be stripping people of. Leaving a market there for people who have actually put the time in and understand software.
I feel you, it's scary. But the possibilities we're presented with are incredible. I'm revisiting all these projects that I put aside because they were "too big" or "too much for a machine". It's quite exciting
Try have your engineers pick up some product work. Clients do NOT want to talk to bots.
There will be a need. Don't worry. Most people still haven't figured out how to properly read and interpret instructions. So they build things incorrectly - with or without AI
Seriously. The bar is that low. When people say "AI slop" I just chuckle because it's not "AI" it's everyone. That's the general state of the industry.
So all you have to do is stay engaged, ask questions, and understand the requirements. Know what it is you're building and you'll be fine.
> I’m basically just the conductor of all those processes.
Orchestrating harmony is no mean feat.
Perfect economic substitution in coding doesn't happen for a long time. Meanwhile, AI appears as an amplifier to the human and vice versa. That the work will change is scary, but the change also opens up possibilities, many of them now hard to imagine.
The AI is pretty scary if you think most of software engineering is about authoring individual methods and rubber ducking about colors of paint and brands of tools.
Once you learn that it's mostly about interacting with a customer (sometimes this is yourself), you will realize the AI is pretty awful at handling even the most basic tasks.
Following a product vision, selecting an appropriate architecture and eschewing 3rd party slop are examples of critical areas where these models are either fundamentally incapable or adversely aligned. I find I have to probe ChatGPT very hard to get it to offer a direct implementation of something like a SAML service provider. This isn't a particularly difficult thing to do in a language like C# with all of the built in XML libraries, but the LLM will constantly try to push you to use 3rd party and cloud shit throughout. If you don't have strong internal convictions (vision) about what you really want, it's going to take you for a ride.
One other thing to remember is that our economies are incredibly efficient. The statistical mean of all information in sight of the LLMs likely does not represent much of an arbitrage opportunity at scale. Everyone else has access to the same information. This also means that composing these systems in recursive or agentic styles means you aren't gaining anything. You cannot increase the information content of a system by simply creating another instance of the same system and having it argue with itself. There usually exists some simple prompt that makes a multi agent Rube Goldberg contraption look silly.
> I’m basically just the conductor of all those processes.
"Basically" and "just" are doing some heroic weight lifting here. Effectively conducting all of the things an LLM is good at still requires a lot of experience. Making the constraints live together in one happy place is the hard part. This is why some of us call it "engineering".
I have been using the most recent Claude, ChatGPT and Gemini models for coding for a bit more than a year, on a daily basis.
They are pretty good at writing code *after* I thoroughly described what to do, step by step. If you miss a small detail they get loose and the end result is a complete mess that takes hours to clean up. This still requires years of coding experience, planning ahead in head, you won't be able to spare that, or replace developers with LLMs. They are like autocomplete on steroids, that's pretty much it.
>> From where I’m standing, it’s scary.
You are being fooled by randomness [1]
Not because the models are random, but because you are mistaking a massive combinatorial search over seen patterns for genuine reasoning. Taleb point was about confusing luck for skill. Dont confuse interpolation for understanding.
You can read a Rust book after years of Java, then go build software for an industry that did not exist when you started. Ask any LLM to write a driver for hardware that shipped last month, or model a regulatory framework that just passed... It will confidently hallucinate. You will figure it out. That is the difference between pattern matching and understanding.
[1] https://en.wikipedia.org/wiki/Fooled_by_Randomness
I've worked with a lot of interns, fresh outs from college, overseas lowest bidders, and mediocre engineers who gave up years ago. All over the course of a ~20 year career.
Not once in all that time has anyone PRed and merged my completely unrelated and unfinished branch into main. Except a few weeks ago. By someone who was using the LLM to make PRs.
He didn't understand when I asked him about it and was baffled as to how it happened.
Really annoying, but I got significantly less concerned about the future of human software engineering after that.
Have you used an LLM specifically trained for tool calling, in Claude Code, Cursor or Aider?
They’re capable of looking up documentation, correcting their errors by compiling and running tests, and when coupled with a linter, hallucinations are a non issue.
I don’t really think it’s possible to dismiss a model that’s been trained with reinforcement learning for both reasoning and tool usage as only doing pattern matching. They’re not at all the same beasts as the old style of LLMs based purely on next token prediction of massive scrapes of web data (with some fine tuning on Q&A pairs and RLHF to pick the best answers).
11 replies →
This is exactly what happened to me: novel or uncommon = hallucinate or invent wrong.
It is ok for getting snippets for example and saying (I did it). Please make this MVVM style. It is not perfect, but saves time.
For very broad or novel reasoning, as of today... forget it.
Why would you expect an LLM or even a human to succeed in these cases? “Write a piece of code for a specification that you can’t possibly know about?” That’s why you have to do context engineering, just like you’d provide a reference to a new document to an engineer writing code.
Stop freaking out. Seriously. You're afraid of something completely ridiculous.
It is certainly more eloquent than you regarding software architecture (which was a scam all along, but conversation for another time). It will find SOME bugs better than you, that's a given.
Review code better than you? Seriously? What you're using and what you consider code review? Assume I could identify one change broke production and you reviewed the latest commit. I am pinging you and you better answer. Ok, Claude broke production, now what? Can you begin to understand the difference between you and the generative technology? When you hop on the call, you will explain to me with a great deal of details what you know about the system you built, and explain decision making and changes over time. You'll tell about what worked and what didn't. You will tell about the risks, behavior and expectations. About where the code runs, it's dependencies, users, usage patterns, load, CPU usage and memory footprint, you could probably tell what's happening without looking at logs but at metrics. With Claude I get: you're absolutely right! You asked about what it WAS, but I told you about what it WASN'T! MY BAD.
Knowledge requires a soul to experience and this is why you're paid.
We use code rabbit and it's better than practically any human I've worked with at a number of code review tasks, such as finding vulnerabilities, highlighting configuration issues, bad practices, etc. It's not the greatest at "does this make sense here" type questions, but I'd be the one answering those questions anyway.
Yeah, maybe the people I've worked with suck at code reviews, but that's pretty normal.
Not to say your answer is wrong. I think the gist is accurate. But I think tooling will get better at answering exactly the kind of questions you bring up.
Also, someone has to be responsible. I don't think the industry can continue with this BS "AI broke it." Our jobs might devolve into something more akin to a SDET role and writing the "last mile" of novel code the AI can't produce accurately.
> Review code better than you? Seriously?
Yes, seriously (not OP). Sometimes it's dumb as rocks, sometimes it's frighteningly astute.
I'm not sure at which point of the technology sigmoid curve we find ourselves (2007 iPhone or 2017 iPhone?) but you're doing yourself a disservice to be so dismissive
1 reply →
> Coding AIs design software better than me, review code better than me, find hard-to-find bugs better than me, plan long-running projects better than me, make decisions based on research, literature, and also the state of our projects better than me
ChatGPT, is that you?
His logic is off and his experience is irrelevant because i doesn’t encompass scale to have been exposed to an actual paradigm shifting event. Civilizations and entire technologies have been overturned so he can’t say it won’t happen this time.
What we do know is this. If AI keeps improving at the current rate it’s improving then it will eventually hit a point where we don’t need software engineers. That’s inevitable. The way for it to not happen is for this technology to hit an impenetrable wall.
This wave of AI came so fast that there are still stubborn people who think it’s a stochastic parrot. They missed the boat.
AI is absolutely rock-bottom shit at all that.
Yeah, it makes me wonder whether I should start learning to be a carpenter or something. Those who either support AI or thinks "it's all bullshit" cite a lack of evidence for humans truly being replaced in the engineering process, but that's just the thing; the unprecedented levels of uncertainty make it very difficult to invest one's self in the present, intellectually and emotionally. With the current state of things, I don't think it's silly to wonder "what's the point" if another 5 years of this trajectory is going to mean not getting hired as a software dev again unless you have a PhD and want to work for an AI company.
What doesn't help is that the current state of AI adoption is heavily top-down. What I mean is the buy-in is coming from the leadership class and the shareholder class, both of whom have the incentive to remove the necessary evil of human beings from their processes. Ironically, these classes are perhaps the least qualified to decide whether generative AI can replace swathes of their workforce without serious unforeseen consequences. To make matters worse, those consequences might be as distal as too many NEETs in the system such that no one can afford to buy their crap anymore; good luck getting anyone focused on making it to the next financial quarter to give a shit about that. And that's really all that matters at the end of the day; what leadership believes, whether or not they are in touch with reality.
Where the hell was all this fear when the push for open source everything got fully underway? When entire websites were being spawned and scaffolded with just a couple lines of code? Do we not remember all those impressive tech demos of developers doing massive complex thing with "just one line of code"? How did we not just write software for every kind of software problem that could exist by now?
How has free code, developed by humans, become more available than ever and yet somehow we have had to employ more and more developers? Why didn't we trend toward less developers?
It just doesn't make sense. AI is nothing but a snippet generator, a static analyzer, a linter, a compiler, an LSP, a google search, a copy paste from stackoverflow, all technologies we've had for a long time, all things developers used to have to go without at some point in history.
I don't have the answers.
It is just the Eliza effect on a massive scale: https://en.wikipedia.org/wiki/ELIZA_effect
Reading Weizenbaum today is eye opening: https://en.wikipedia.org/wiki/Computer_Power_and_Human_Reaso...
I agree the ELIZA effect is strong, additionally I think it is some kind of natural selection.
I feel like LLM's are specifically selected to impress people that have a lot of influence. People like investors and CEO's. Because a "AI" that does not impress this section of the population does not get adopted widely.
This is one of the reasons I think AI will never really be an expert as it does not need to be. It only needs to adopt a skill (for example coding) to pass the examination of the groups that decide if it is to be used. It needs to be "good enough to pass".
I read a book called "Blood in the machine". It's the history of the Luddites.
It really put everything into perspective to where we are now.
Pre-industrial revolution whole towns and families built clothing and had techniques to make quality clothes.
When the machines came out it wasn't overnight but it wiped out nearly all cottage industries.
The clothing it made wasn't to the same level of quality, but you could churn it out faster and cheaper. There was also the novelty of having clothes from a machine which later normalised it.
We are at the beginning of the end of the cottage industry for developers.
Luddism arose in response to weaving machines, not garment-making machines. The machines could weave a piece of cloth that still had to be cut and sewn by hand into a garment. Weaving the cloth was by far the most time consuming part of making the clothing.
Writing code is not at all the most time consuming part of software development.
If you used the car as an analogy instead, it would make more sense to me. There were car craftsmen in Europe that Toyota displaced almost completely. And software is more similar to cars in that it needs maintenance and if it breaks down, large consequences like death and destruction and/or financial loss follows.
If llms can churn out software like Toyota churns out cars, AND do maintenance on it, then the craftsmen (developers of today) are going to be displaced.
Does the analogy hold though?
We had "free clothes" for years, decades now. I don't mean cheap I mean literally free, as in $0.0 software. Cheaper software isn't new.
Also there are still clothe designers, fashion runways, and expensive Patagonia vests today. The clothing industry is radically different from back then but it's definitely not gone.
It didn't kill everything. Some survived but not to the extent that it was.
> The clothing industry is radically different from back then but it's definitely not gone.
Small towns had generations of people who had learned skills in making clothing / yarn. To do the work you needed years of experience and that's all you knew.
Once the industrial revolution hit they hired low skilled workers that could be dumped at a moments notice. It made whole villages destitute. Some survived, but the far majority became poor.
That was one industry. We now have AI at a point to wipe out multiple industries to a similar scale.
I posted elsewhere, but you are looking at the wrong part of the chain.
We have cheap (or free) software for large markets, and certain small markets where software developers with hobbies have made something. If every niche that will never be able to afford a large 6-figure custom software could get slop software for an affordable price, then that establishes a foot-hold for working its way up the quality ladder.
Just like the pro-AI articles, it reads to me like a sales pitch. And the ending only adds to it: the author invites to hire companies to contract him for training.
I would only be happy if in the end the author turns out to be right.
But as the things stand right now, I can see a significant boost to my own productivity, which leads me to believe that fewer people are going to be needed.
When coal powered engines became more efficient, demand for coal went UP. It went up because vastly more things could now be cost effectively be coal-powered.
I can see a future where software development goes the same way. My wife works in science and I see all kinds of things in a casual observation of her work that could be made more efficient with good software support. But not by enough to pay six-figures per year for multiple devs to create it. So it doesn’t get done and her work and the work of tens of thousands like her around the world is less efficient as a result.
In a world where development is even half as expensive, many such tasks become approachable. If it becomes a third or quarter as expensive, even more applications are now profitable.
I think far more people will be doing something that creates the outcomes that are today created by SWEs manually coding. I doubt it will be quite as lucrative for the median person doing it, but I think it will still be well above the median wage and there will be a lot of it.
See: https://en.wikipedia.org/wiki/Jevons_paradox
Many HN users may point to Jevons paradox, I would like to point out that it may very well work up until the point that it doesn't. After all a chicken has always seen the farmer as benevolent provider of food, shelter and safety, that is until of course THAT day when he decides he doesn't.
1 reply →
>WYSIWYG, drag-and-drop editors like Visual Basic and Delphi were going to end the need for programmers.
VB6 and Delphi were the best possible cognitive impedance match available for domain experts to be able to whip up something that could get a job done. We haven't had anything nearly as productive in the decades since, as far as just letting a normie get something done with a computer.
You'd then hire an actual programmer to come in and take care of corner cases, and make things actually reliable, and usable by others. We're facing a very similar situation now, the AI might be able to generate a brittle and barely functional program, but you're still going to have to have real programmers make it stable and usable.
> Edgar Dijkstra called it nearly 50 years ago: we will never be programming in English, or French, or Spanish. Natural languages have not evolved to be precise enough and unambiguous enough. Semantic ambiguity and language entropy will always defeat this ambition.
This is the most important quote for any AI coding discussion.
Anyone that doesn't understand how the tools they use came to be is doomed to reinvent them.
> The folly of many people now claiming that “prompts are the new source code”,
These are the same people that create applications in MS Excel.
In aviation safety, there is a concept of "Swiss cheese" model, where each successful layer of safety may not be 100% perfect, but has a different set of holes, so overlapping layers create a net gain in safety metrics.
One can treat current LLMs as a layer of "cheese" for any software development or deployment pipeline, so the goal of adding them should be an improvement for a measurable metric (code quality, uptime, development cost, successful transactions, etc).
Of course, one has to understand the chosen LLM behaviour for each specific scenario - are they like Swiss cheese (small numbers of large holes) or more like Havarti cheese (large number of small holes), and treat them accordingly.
Interesting concept, but as of now we don't apply this technologies as a new compounding layer. We are not using them after the fact we constructed the initial solution. We are not ingesting the code to compare against specs. We are not using them to curate and analyze current hand written tests(prompt: is this test any good? assistant: it is hot garbage, you are inferring that expected result equals your mocked result). We are not really at this phase yet. Not in general, not intelligently. But when the "safe and effective" crowd leave technology we will find good use cases for it, I am certain (unlike uml, VB and Delphi)
LLMs are Kraft Singles. Stuff that only kind of looks like cheese. Once you know it's in there, someone has to inspect, and sign-off on, the entire wheel for any credible semblance of safety.
how sure are you that an llm won't be better at reviewing code for safety than most humans, and eventually, most experts?
1 reply →
> One can treat current LLMs as a layer of "cheese" for any software development or deployment pipeline
It's another interesting attempt at normalising the bullshit output by LLMs, but NO. Even with the entshittified Boeing, the aviation industry safety and reliability records, are far far far above deterministic software (know for a lot of un-reliability itself), and deterministic, B2C software to LLMs in turn is what Boeing and Airbus software and hardware reliablity are for the B2C software...So you cannot even begin to apply aviation industry paradigms to the shit machines, please.
I understand the frustration, but factually it is not true.
Engines are reliable to about 1 anomaly per million flight hours or so, current flight software is more reliable, on order of 1 fault per billion hours. In-flight engine shutdowns are fairly common, while major software anomalies are much rarer.
I used LLMs for coding and troubleshooting, and while they can definitely "hit" and "miss", they don't only "miss".
4 replies →
The way I see it, the problem with LLMs is the same as with self-driving cars: trust. You can ask an LLM to implement a feature, but unless you're pretty technical yourself, how will you know that it actually did what you wanted? How will you know that it didn't catastrophically misunderstand what you wanted, making something that works for your manual test cases, but then doesn't generalize to what you _actually_ want to do? People have been saying we'll have self-driving cars in five years for fifteen years now. And even if it looks like it might be finally happening now, it's going glacially slow, and it's one run-over baby away from being pushed back another ten years.
People used to brush away this argument with plain statistics. Supposedly, if the death statistics is below the average human, you are supposed to lean back and relax. I never bought this one. Its like saying LLMs write better texts then the average huamn can, so you are supposed to use it, no matter how much you bring to the table.
This time it actually is different. HN might not think so, but HN is really skewed towards more senior devs, so I think they're out of touch with what new grads are going through. It's awful.
What is it that new grads are going through? If you are referring to difficulty finding a job, keep in mind that there is both an economic downturn and an over-hiring correction happening in the industry right now. I imagine the AI industry is indeed having an impact in how management is behaving, but I would not yet bet on AI actually replacing developers jobs holistically.
"We really could produce working software faster with VB or with Microsoft Access"
Press X to doubt.
Here’s how I see it. Writing code or building software well requires knowledge of logic, data structures, reliable messaging, and separation of concerns.
You can learn a foreign language just fine, but if you mangle the pronunciation, no one will talk to you. Same thing with hacking at software without understanding the above elements. Your software will be mangled and no one will use it.
> Your software will be mangled
the quality of how maintainable the source code is has no bearing on how a user perceives the software's usefulness.
If the software serves a good function for the user, they will use it, regardless of how badly the datastructures are. Of course, good function also means reliability from the POV of the user. If your software is so bad that you lose data, obviously no one will use it.
But you are conflating the maintainability and sensibilities of clean tools, clean code and clean workspaces, with output.
A messy carpentry workshop can still produce great furniture.
This is bean counter mentality. Personally just don’t believe this is how it works.
The intention/perspective of development is something on its own and doesn’t correspond to the end result directly.
This is such a complex issue that everything comes down to what someone believes
Software doesn't have to be good academically speaking. It just needs to furnish a useful function to be economically viable.
LLM's may not generate the best code but they need only to generate useful code to warrant their use.
As someone having watched AI systems being good enough to replace jobs like content creation on CMS, this is being in denial.
Yes software developer are still going to be need, except much fewer of us, exactly like fully automated factories still need a few humans around, to control and build the factory in first place.
I concur with your sentiments.
Am puzzled why so many on HN cannot see this. I guess most users on HN are employed? Your employers - let me tell you - are positively salivating at the prospect of firing you. The better LLM's get the fewer of you will be needed.
Denial, like those factory workers at the first visit from the automation company, each one hoping they are the ones elected to stay and overwatch the robots.
I have seen projects where translator teams got reduced, asset creation teams, devops head count, support teams on phone lines,...
It is all about how to do more with less, now with AI help as well.
I see it as pure deterministic logic being contaminated by probabilistic logic at higher layers where human interaction happens. Seeking for human comfort by forcing computers to adapt to the human languages. Building adapters that can allow humans to stay in their comfort zone instead of dealing with the sharp-edged computer interfaces.
At the end, I don't see it going beyond being a glorified form-assistant who can search internet for answers and summarize. That boils down to chat bots that will remain and become part of every software component that ever need to interface with humans.
Agent stuff is just a fluff that is providing hype-cushion around chat bots and will go away with hype cycle.
I nodded furiously at this bit:
> The hard part of computer programming isn't expressing what we want the machine to do in code. The hard part is turning human thinking -- with all its wooliness and ambiguity and contradictions -- into computational thinking that is logically precise and unambiguous, and that can then be expressed formally in the syntax of a programming language.
> That was the hard part when programmers were punching holes in cards. It was the hard part when they were typing COBOL code. It was the hard part when they were bringing Visual Basic GUIs to life (presumably to track the killer's IP address). And it's the hard part when they're prompting language models to predict plausible-looking Python.
> The hard part has always been – and likely will continue to be for many years to come – knowing exactly what to ask for.
I don't agree with this:
> To folks who say this technology isn’t going anywhere, I would remind them of just how expensive these models are to build and what massive losses they’re incurring. Yes, you could carry on using your local instance of some small model distilled from a hyper-scale model trained today. But as the years roll by, you may find not being able to move on from the programming language and library versions it was trained on a tad constraining.
Some of the best Chinese models (which are genuinely competitive with the frontier models from OpenAI / Anthropic / Gemini) claim to have been trained for single-digit millions of dollars. I'm not at all worried that the bubble will burst and new models will stop being trained and the existing ones will lose their utility - I think what we have now is a permanent baseline for what will be available in the future.
> claim to have been trained for single-digit millions of dollars
Weren't these smaller models trained by distillation from larger ones, which therefore have to exist in order to do it? Are there examples of near state of the art foundation models being trained from scratch in low millions of dollars? (This is a genuine question, not arguing. I'm not knowledgeable in this area.)
The first part is surely true if you change it to "the hardEST part," (I'm a huge believer in "Programming as Theory Building"), but there are plenty of other hard or just downright tedious/expensive aspects of software development. I'm still not fully bought in on some of the AI stuff—I haven't had a chance to really apply an agentic flow to anything professional, I pretty much always get errors even when one-shotting, and who knows if even the productive stuff is big-picture economical—but I've already done some professional "mini projects" that just would not have gotten done without an AI. Simple example is I converted a C# UI to Java Swing in less than a day, few thousand lines of code, simple utility but important to my current project for <reasons>. Assuming tasks like these can be done economically over time, I don't see any reason why small and medium difficulty programming tasks can't be achieved efficiently with these tools.
Hardest part of programming is knowing wtf all the existing code does and why.
And that is the super power of LLMs. In my experience LLMs are better a reading code than writing it. Have it annotate some code for you.
2 replies →
maybe not the MOST valuable part of prompting an LLM during a task, but one of them, is defining the exact problem in precise language. i dont just blindly turn to an LLM without understanding the problem first, but i do find Claude is better than a cardboard cutout of a dog
Aren't they also losing money on the marginal inference job?
I think it is very unlikely that they are charging less money for tokens than it costs them to serve those tokens.
If they are then they're in trouble, because the more paying customers they get the more money they lose!
2 replies →
Indeed, while DeepSeek 3.2 or GLM 4.7 are not Opus 4.5 quality, they are close enough that I could _get by_ because they're not that far off, and are about where I was with Sonnet 3.5 or Sonnet 4 a few months ago.
I'm not convinced DeepSeek is making money hosting these, but it's not that far off from it I suspect. They could triple their prices and still be cheaper than Anthropic is now.
The future of problem solving is problem solvers
The future of weaving is automated looms, but sheep will still be needed to provide the raw materials.
There is a guaranteed cap on how far LLM based AI models can go. Models improve by being trained on better data. LLMs being used to generate millions of lines of sloppy code will substantially dilute the pool of good training data. Developers moving over to AI based development will cease to grow and learn - producing less novel code.
The massive increase in slop code and loss of innovation in code will establish an unavoidable limit on LLMs.
Maybe we'll train the llms in our ways of using them, and the next generation of coding assistants will be another layer inbetween us and the code. You talk to the chief engineer llm who in turn talks to its cadre of claude code instances running in virtual tmux. \hj?
I think most of the progress is training by reinforcement learning on automated assessments of the code produced. So data is not really an issue.
But they're not just training off code and its use, but off a corpus general human knowledge in written form.
I mean, in general not only do they have all of the crappy PHP code in existence in their corpus but they also have Principia Mathematica, or probably The Art of Computer Programming. And it has become increasingly clear to me that the models have bridged the gap between "autocomplete based on code I've seen" to some sort of distillation of first order logic based on them just reading a lot of language... and some fuzzy attempt at reasoning that came out of it.
Plus the agentic tools driving them are increasingly ruthless at wringing out good results.
That said -- I think there is a natural cap on what they can get at as pure coding machines. They're pretty much there IMHO. The results are usually -- I get what I asked for, almost 100%, and it tends to "just do the right thing."
I think the next step is actually to actually make it scale and make it profitable but also...
fix the tools -- they're not what I want as an engineer. They try to take over, and they don't put me in control, and they create a very difficult review and maintenance problem. Not because they make bad code but because they make code that nobody feels responsible for.
That is a naive assumption. Or rather multiple naive assumptions: Developers mostly don’t move over to AI development, but integrate it into their workflow. Many of them will stay intellectually curious and thus focus their attention elsewhere; I’m not convinced they will just suddenly all stagnate.
Also, training data isn’t just crawled text from the internet anymore, but also sourced from interactions of millions of developers with coding agents, manually provided sample sessions, deliberately generated code, and more—there is a massive amount of money and research involved here, so that’s another bet I wouldn’t be willing to make.
In past cases of automation, quantity was the foot-in-the-door and quality followed. Early manufactured items were in many cases inferior to hand-built items, but one was affordable and the other not.
Software is incredibly expensive and has made up for it with low marginal costs. Many small markets could potentially be served by slop software, and it's better than what they would have otherwise gotten (which is nothing).
>AGI seem as far away as it’s always been
This blurb is the whole axiom on which the author built their theory. In my opinion it is not accurate, to say the least. And I say this as someone who is still underwhelmed by current AI for coding.
Totally delusional. The article does not even try to figure out why any of this happened.
In all of the cases the main prediction that was made came true. The cost, especially the human cost, of developing some piece of software dramatically decreased. The only reason why the amount of programmers needed still rose was because the amount of software needed rose faster.
Clearly that trend will not hold forever.
>The hard part of computer programming isn’t expressing what we want the machine to do in code. The hard part is turning human thinking – with all its wooliness and ambiguity and contradictions – into computational thinking that is logically precise and unambiguous, and that can then be expressed formally in the syntax of a programming language.
And there is exactly one single technology which has ever been able to do this task, which is LLMs. Not addressing the elephant in the room, which is that an LLM can actually take such instructions and produce something meaningful with it, just makes the whole article worthless.
Everything in this article is just inverse special pleading. Since the last N times, the most enthusiastic predictions did not come true, this time only minor changes can happen. If LLMs are only a revolution on the scale of fast interpreted languages (which have significantly impacted what a small team is capable of delivering in terms of complexity), then they will drastically impact most of the software industry.
If these changes happen, and simultaneously the rate at which software is demanded does not also increase (why would it?), then the implications will be extremely serious. Especially if you are not a developer in an established position.
[dead]