Its worth mentioning that this essay has some signs of being either partially AI generated or heavily edited through an LLM. Some of the signs are there (It's not X, it's Y), With the blog having gone from nearly zero activity between 2015 and 2025 to have it explode in posts and text output since then also raises an eyebrow.
My current policy on this is that if text expresses opinions or has "I" pronouns attached to it then it's written by me. I don't let LLMs speak for me in this way.
I'll let an LLM update code documentation or even write a README for my project but I'll edit that to ensure it doesn't express opinions or say things like "This is designed to help make code easier to maintain" - because that's an expression of a rationale that the LLM just made up.
I think it is very fair to say that in the same way that LLM's have given english majors access to programming, LLMs have also given engineers access to clear communication.
I'm not shy to admit that LLMs even from 2 years ago could communicate ideas much better than me, especially for a general audience.
It’s not “clear communication” though. The prose that comes out of LLMs is awful - long, vapid paragraphs with distracting tropes. You can ask them to be concise but then they file down all the wrong bits of the sentence and lose meaning. There’s a reason people bother clocking it and complaining about it, it’s *bad*
It’s like everything else that AI can do - looks fine at a glance, or to the inexperienced, but collapses under scrutiny. (By your own admission you’re not a great communicator… how can you tell then?)
> LLMs have also given engineers access to clear communication.
A lot of the time, the inability to express an idea clearly hints at some problem with the underlying idea, or in one's conceptualisation of that idea. Writing is a fantastic way to grapple with those issues, and iron out better and clearer iterations of ideas (or one's understanding thereof).
An LLM, on the other hand, will happily spit out a coherent piece of writing defending any nonsense idea you throw at it. Nothing is learnt, nothing is gained from such "writing" (for either the author or the audience).
Recently read a tweet suggesting to ask an llm to defend a position you know to be false. It's quite eye opening. I mean, it shouldn't be, if you did debate club etc. Or know how lawyers and politicians work. But it's quite revealing how it can piece together a good defense, selectively quoting real facts, embuing them with undue weight etc to make the thesis stand quite well.
It's often warping the message or "snapping it to grid", taking off the edge, the unique insight. A lack of clear communication is much more a symptom of unclarity about the intended message, audience, prioritization etc. I don't doubt that you internally have a clear idea but sharing it requires thinking about the intended audience and the diff of their current state of knowledge and doubt and where you want to move their thinking. This is a much bigger part than knowing eloquent vocab and grammar tricks.
It doesn't come naturally to the more introverted type of person who cares about the object level problem and not whatever anyone else may know or doubt, I'll admit this. But slapping LLMs on it is not a great solution.
As someone who has written a few deeply personal articles with LLM assistance, I see the signs and I'm almost certain this was generated off a few bullet points. The repetition and cadence strongly resembles the LLM output. Its the kind of fluff that I remove from a piece, because it lacks humanity and offers little substance.
The comments as well. I won't give away the tells but HN is less and less pleasant to read. Now is the time to cherish your pockets of small scale high quality forums that's not flooded by this stuff yet.
I guess talking to people and making friends helps. Online, maybe seek out discords and befriend people and they may tell you. Not unlike how you find cool underground clubs.
Even the linkedin profile has a studio-ghibli-style avatar. People are going to assume that he is just an "analog interface" to an LLM. Which is sad, because he might be a good programmer. In fact, I tend to see a lot of english-as-second-language people embrace LLMs as a kind of "equalizer", not realizing that in 2026 it is the opposite (not saying that it's right either way, just pointing out that it is becoming a kind of anti-marketing, like showing up to a conference without any clothing, and getting banned from the conference permanently).
We should probably normalize publishing things in our native languages, and expecting the audience to run it through a translator. (I have been toying with the idea of writing everything in Esperanto (not my native language, but a favorite) and just posting links to auto-translated English versions where the translation is good enough).
EDIT: as someone with friends and family from Eastern Europe, I can tell you that the prevailing attitude is: "everything is bullshit anyway" (which, to be fair, has a lot of truth to it), and so it is no surprise that people would enthusiastically embrace a pocket-sized bullshit factory, hook it up to a fire-hose, and start spraying. We saw it with spam, and we see it now with slop. It won't stop unless the system stops rewarding it.
This was my thought after getting through a few paragraphs as well. At first, I was thinking, this is interesting, maybe worth sharing with colleagues. But then it became too obvious it was AI written or "assisted". Can't take that seriously.
LLMs write this way because people write this way. Maybe not everyone, but enough for it to train the models to do it. Much of my writing reads like an LLM wrote it, but that doesn't make me an LLM.
No, this statement is not true for anything except a base model. Benchmaxxing during RL phase is how you get the advertisement style "punchy" writing, because even though people don't usually write that way it is eye catching and people will vote for the bullet-point emdash slop. I wonder if some lab will be bold enough to do "anti rlhf", lmarena score be damned.
Yes and no. LLMs take all the writing on the Internet (good and bad) and average it out. It's similar to the way generative AI images always have an identifiable, artificial "look". They've averaged out the personality and thereby erased the individuality that went into the efforts the original artists used to create them.
Why is this sentiment expressed so often ("It was written/edited by AI"?
It seems to bother people, perhaps since it may have been low-effort.
Doesn't it not matter as long as the content is good? Otherwise, it seems to be no different than a standard low-quality post.
The formulaic style/cadence/structure/tone is annoying, for one due to its LLM-induced prevalence, but also because it is padded and stretched without adding substance while being dyed in superficialities, and has a weird tendency of meandering through its thematic territory, like the author was slightly distracted or is writing the same thing for the 20th time, or is missing a good editor. Pre-LLM, it might have been an okay-ish, but not great, article. Now it’s just grating and makes you feel like you’re wasting your time reading it.
When I want to read Ai writing (which is not never), I chat with it myself and I prompt it better and get more interesting stuff than these generic insight blogspam.
It is almost 90% generated using AI text. So many paragraphs to say basically nothing at all.
Like look at this paragraph:
> Junior engineers have traditionally learned by doing the simpler, more task-oriented work. Fixing small bugs. Writing straightforward features. Implementing well-defined tickets. This hands-on work built the foundational understanding that eventually allowed them to take on more complex challenges.
The first sentence was enough to convey everything you needed to know, but it kept on adding words in that AI cadence. The entire post is filled with this style of writing, which, even if it is not AI, is extremely annoying to read.
My point is that there's nothing to be written there "instead", it just is not needed text that is added to make the text longer, typical of AI writing that parrots the same points over and over to make up for word count.
Here's another example from the blog:
> Here is something that gets lost in all the excitement about AI productivity: most software engineers became engineers because they love writing code.
> Not managing code. Not reviewing code. Not supervising systems that produce code. Writing it. The act of thinking through a problem, designing a solution, and expressing it precisely in a language that makes a machine do exactly what you intended. That is what drew most of us to this profession. It is a creative act, a form of craftsmanship, and for many engineers, the most satisfying part of their day.
can just be:
> Most software engineers became engineers because they love writing code. It is a creative act, a form of craftsmanship, and for many engineers, the most satisfying part of their day.
Clarity is something that is taught in every writing class but AI generated text always seems to have this weird cadance as follows: The sound is loud. Not a whimper, not a roar, a simple sound that is very loud. And that's why... blah blah blah.
You have to care about your readers if you're writing something seriously. Throwing just a bunch of text that all mean the same thing in your writing is one of the bigger sins you can do, and that's why most people hate reading AI writing.
The part you'd like to remove ("Not managing code...") may be not required to convey the objective meaning of the sentence, but humans have emotions, too. I could have written stuff like that. To build up a bigger emotional picture.
> The act of thinking through a problem, designing a solution, and expressing it precisely in a language that makes a machine do exactly what you intended.
This sentence may not be relevant for whatever you experience to be the relevant message of the text. But it still says something the remaining paragraph does not. And also something I can relate to.
Also, as LLMs are statistical models, one has to assume that they write like this because their training data tells them to. Because humans write like this. Not when they do professional writing maybe, but when they just ramble. Not all blogs are written by professionals. I'd say most aren't. LLM training data consists mostly of humans rambling.
I also sometimes write long comments on the internet. And while I have no example to check, I feel like I do write such sentences, expanding on details to express more emotional context. Because I'm not a robot and I like writing a lot. I think it's a perfectly human thing to do. I find it sad that "writing more than absolutely needed" is now regarded as a sign of AI writing.
> Because humans write like this. Not when they do professional writing maybe, but when they just ramble.
I keep seeing this assertion and I keep responding "Please, point to the volume of writing with this specific cadence that has a date prior to 2024" and I keep getting... crickets!
You're asserting that this is a common way for humans to write, correct? Should be pretty easy, then, to find a large volume of examples.
Like I said: I think I write like this on some occasions.
I wouldn't know how I would search for examples. I guess you'd have to search old reddit comment threads or something. But yeah, I have no motivation to do that, tbh.
It could be that it's hard to find examples because they are scattered about in countless comment threads and single posts on countless platforms. Things I rarely keep links to, things nobody indexed on a large scale before LLMs.
It may be that it wasn't a very popular style of writing, because most people don't like writing a lot and keep their texts on the internet short. LLMs exaggerate this style because they generate exaggerative amounts of text in general. The style wasn't particularly annoying in the past because it wasn't that popular. It's annoying now because LLMs flood the internet with it.
The quoted example in particular didn't appear uncanny to me. And it still doesn't. I can see myself writing like that.
I'm sorry I have no example for you. But I'm genuinely unsure whether I'm oblivious to the patterns others see, or whether others see patterns because they want to see them.
One of the good book about writing I read was William Zinsser's "On Writing Well". Striving for simplicity and avoiding clutter was the two first principles described in the book. AI writing feels more like ramblings than communication.
There's an art to it. Most human attempts, and every LLM attempt I've ever seen, are awful, sometimes bordering on unreadable, but, as you say, there are a relatively small number of authors who do it well. That doesn't mean that most people should do it.
I'm a French speaker and florid and elaborate writing is something I've grown up with. It can be difficult if you don't know the word or are not used to the style, but it's not boring. AI writing is just repetitive.
When I've used AI for proofreading the suggestions it makes to me is to cut a lot and shorten it. It also gives me examples, never with my voice or style though.
5 sentence paragraph. First sentence is parataxis claim. Followed by 3 examples in sentence fragments, missing verbs, that familiar cadence. Then the final sentence, in this case also missing a verb.
I feel like it's such a lack of self respect and respect for others when people write using AI on personal blogs.
Reading AI code is very pleasant. It's well annotated and consistent - how I like to read code (although not how I write code LOL). Reading language/opinions is not meant to be this way. It becomes repetitive, boring, and feels super derivative. Why would you turn the main way we communicate with each other into a soulless, tedious, chore?
I think with coding it's because I care* about what the robot is doing. But, with communication, I care about what the person is thinking in their mind, not through the interpretation of the robot. Even if the person's mind isn't as strong. At least then I can size the person up - which is the other reason understanding each other is important and ruined when you put a robot in between.
It's also because we (generally) consider a blog to be human communication and we consider math and programs to be something else.
If you're talking to someone on the phone and halfway through they identify themselves as a bot, surprising you, there's a profound sense of something like betrayal. A moment ago you were having a human connection, and suddenly that vaporized. You were misled and were just talking to an unfeeling robot.
And heartfelt writing is similar. We imagine the human at the other side of the screen and we relate. And when we discover it was a bot, no matter how accurate the sentiment, that relationship vanishes.
But with math and software, it's already sterile from a human connection perspective. It's there for a different purpose. Yes, it can be beautiful, but when we read it we don't tend to build a human connection with the coder.
An interesting exception is comments. When we read the fast inverse square root code and see the "what the fuck..." comment, we instantly relate to the person writing the software. If we later learned that comment was generated by an LLM, we'd lose that connection, again.
Totally agree. I'll extend this to email and slacks, too. I cannot stand getting AI written slop from fellow co-workers because they couldn't write the message themselves. Do not even bother to engage with me if you need to put your thoughts through an AI first. It won't go well. People gotta work on themselves a lot more and I think they're using AI to do the opposite.
> I feel like it's such a lack of self respect and respect for others when people write using AI on personal blogs.
Not so sure about the respect aspect: I have lots of self-respect, but I don't generally broadcast respect for random other people when I write my blogs - the most recent one even called readers stupid, IIRC!
I feel it's more a matter of expression of contempt: if you can't be bothered to write it, WTF are you expecting people to read it?
If you're writing an online blog with the intention that people are to read it, there should be some semblance of respect for their time and respect for your craft. IMO.
It's funny how seemingly easy it is to tell articles like this have that AI generated whiff to them. The first bit that raised my suspicion was the "The Identity Crisis Nobody Talks About" headline. This "The x nobody talks about" feels like such a GenAI thing.
I saw someone point out something like: ai makes every sentence count. There’s no building or allowing a point to breathe. Every sentence is an axiom to get the meaning across, and its so grating
Maybe that's why the writing feels so terrible. The AI is attempting to maximize every sentence while simultaneously expanding on just a few actually meaningful points. And the net result of that dissonance is this rage-inducing vapidity. It's the written equivalent of the Uncanny Valley.
A better question is "Why can't the devs producing code with AI spot the same poor patterns in the code they are generating?"
Maybe my point is that, to a poor speaker of English, the AI blogpost looks good and reads well. In much the same way, to a poor programmer, the AI produced code looks good and reads well.
In a nutshell, if it generates poor English, WTF would anyone think it generates anything but poor code?
One problem I have seen IRL is AI deployment mistakes and IMO Vibe Coders need an IT/Dev Father Figure type to avoid these simple mistakes. Here is one example:
A surgeon (no coding experience) used Claude to write a web app to track certain things about procedures he had done. He deployed the app on a web hosting provided (PHP LAMP stack). He wanted to share it with other doctors, but wasn't sure if it was 'secure' or not. He asked me to read the code and visit the site and provide my opinion.
The code was pretty reasonable. The DB schema was good. And it worked as expected. However, he routinely zipped up the entire project and placed the zip files in the web root and he had no index file. So anyone who navigated to the website saw the backups named Jan-2026.backup, etc. and could download them.
The backups contained the entire DB, all the project secrets, DB connection strings, API credentials, AWS keys, etc.
He had no idea what an 'index' file was and why that was important. Last I heard he was going to ask Claude how to secure it.
I'm not opposed to AI generated text in principle. But not knowing how it was written is problematic, because it can change the meaning of the text. Take this paragraph for instance:
"From my experience building and scaling teams in fintech and high-traffic platforms, I can tell you that role expansion without clear boundaries always leads to the same outcome: people try to do everything, nothing gets done with the depth it requires, and burnout follows."
This reads like a first person account of someone's experience. Is it though? If it's nobody's experience then it robs this text of its meaning. If it is somebody's experience and that person used AI to improve their style then that's absolutely fine with me.
I would prefer to have the prompt he used to generate the article. Similarly, for compiled binaries, I would rather have the source code that produced them, instead of just an .exe file.
> Here is something that gets lost in all the excitement about AI productivity: most software engineers became engineers because they love writing code.
1) I guess I am not included in the set named "most software engineers."
2) If the title is "Software Engineer," I think I should be engineering, not coding.
This has probably been beaten to death, but I think this is the biggest disciminating question between "pro ai" and "against ai" in the software world is: "Dp you do (this) becuase you like writing code, or because you like building things for the world?"
Of course I don't think it's a binary decision.
Although I more more motivated by building things, I do somewhat miss the programmer flow state I used to get more often.
It's a different skillset and way of thinking. Engineers tend to think vertically deep on technical problems. With AI, you have to think horizontally broad and vertically up on the architectural problem. The trick is to be comfortable relegating the details to AI.
One concrete example of this realization was when I was researching how to optimize my claude code environment with agents, skills, etc. I read a lot of technical documents on how these supplemental plugins work and how to create them. After an hour of reading through all this, I realized I could just ask Claude to optimize the environment for me given the project context. So I did, and it was able to point out plugins, skills, agents that I can install or create. I gave it permission to create them and it all worked out.
This was a case of where I should not think more technically deeper, but at a more "meta" level to define the project enough for Claude to figure out how to optimize the environment. Whether that gave real gains is another question of course. But I have anecdotally observed faster results and less token usage due context caching and slightly more tools-directed prompts.
The post is right superficially. It made being an engineer harder because it took away the easy parts that anyone can do and it forces engineers to think of the hard ones.
No jobs get easier with automation - they always move a step up in abstraction level.
An accountant who was super proficient in adding numbers no longer can rely on those skills once calculator was invented.
This is the key. I haven't found that things have become harder. The hard parts are still hard, and those have been the most important and prominent parts of my job once I reached a certain level.
Exactly. I'm a principal software engineer. My job is a lot less about writing code and a lot more about planning, designing, reviewing, and training.
However, I do wonder how we will train juniors to become seniors. Perhaps the answer is that the curriculum changes from coding and data structures to architecture and design which was typically a last minute addition in college.
I disagree on making it easier. I'm very capable of writing code in multiple languages but it's boring and monotonous. It's getting in the way of me building the system I have in mind. I prefer the engineering (design) to writing. If I can describe my system design to something (a junior developer or an AI) and see it come to life quickly, that's great; it lets me spend more time on designing the system, or perhaps designing more systems.
That said, there are plenty of amateurs who find coding to be approachable and system design to me daunting. For them, eliminating coding and moving the focus to system design would be a nightmare.
> The post is right superficially. It made being an engineer harder because it took away the easy parts that anyone can do and it forces engineers to think of the hard ones.
I dunno about that. Look at blogging as an example - AI took away the "easy"[1] part of blogging, and now we are left with 90% crap AI-generated "articles" like the one you just read.
I feel it's the other way around - AI took away the hard parts, of both blogging and programming, and now what have to look forward to every single damn day is a deluge of AI slop of absolutely poor quality.
Continuing with the literature analogy (because this article was written by an AI), adding AI as a tool for authors isn't producing the next Terry Pratchett quicker, it's delaying the production of the next Terry Pratchett because the next Terry Pratchett will be drowned out by an unstoppable volume of AI slop.
After all, if you can't recognise obvious AI blog posts, what makes you think you can recognise poor code?
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[1] I am using the term as you are using it. I don't really believe that it took away the easy part.
It might be worth mentioning studies that show the lack of productivity gains from LLM usage. These posts take it as an unequivocal given. Management might still have the expectations that certain tasks are faster. But they aren’t always connected to reality because they’re not thinking as engineers.
There's nothing new about this pattern. When the tractor was invented, the farmer didn't get to knock off early. He just started producing 10x more. Then the tractors got bigger and more powerful, and the things you used them with got more sophisticated too and suddenly you're producing 100x more.
And the only people who could afford to tractor at scale are Cargill/Monsanto who bought out most of the small/medium-sized farms while leaving farms that didn't take the offer to slowly die...
And yet there isn't widespread unemployment. Fewer farmers were needed so fewer people became farmers. Food became cheap and plentiful. Everyone else went on to do other things that they couldn't afford to do before. Software will do the same; we will make more software with fewer people and it will become ubiquitous to the point that people will just quickly generate whatever software they need rather than do many monotonous tasks manually.
"the skills that the new engineering landscape actually requires: system design, architectural thinking, product reasoning, security awareness, and the ability to critically evaluate code they did not write."
These, surely, are the skills they always needed? Anyone who didn't have these skills was little more than a human chatgpt already, receiving prompts and simply presenting the results to someone for evaluation.
That’s a great point, but yes: a lot of devs were nothing more than a glorified LLM and during reviews were just an expensive linter. Reality is catching up to those.
This is what hapenned 10 years ago, when machine translation entered the professional translation business. Post-editing the translation was often slower than human translating sentences from scratch. Now nearly the whole industry is post-editing machine translations, and there is more and more content that is not even post-edited.
What I never enjoyed was looking up the cumbersome details of a framework, a programming language or an API. It's really BORING to figure out that tool X calls paging params page and pageSize while Y offset and limit. Many other examples can be added.
For me, I feel at home in so many new programming languages and frameworks that I can really ship ideas. AI really helps with all the boring stuff.
Same here. I like bringing ideas to life; code is just a means to an end. I can now give detailed designs to an AI and let it write the hundreds of lines of code in just minutes, and with far fewer typos than I would make. It's still not perfect - I have to review it all - but if I give it a proper spec in generally creates exactly what I had in mind.
Agree, it’s made programming so much fun. The other day I wrote a C# app just because it was the best language for the job, I’ve never touched .Net in my life. Worked great, clients loved it.
I can actually build nice UIs as a traditional ML engineer (no more streamlit crap). People are using them and genuinely impressed by them
I can fly through Rust and C++ code, which used to take ages of debugging.
The main thing that is clear to me is that most of the ecosystem will likely converge toward Rust or C++ soon. Languages like Python or Ruby or even Go are just too slow and messy, why would you use them at all if you can write in Rust just as fast? I expect those languages to die off in the next several years
Prompt: "The text of the blog article is copy-pasted below. Please summarize it in one parargaph."
Result: "The blog article explores the paradox that while AI has made coding easier by automating many tasks, it has also made the role of software engineers significantly harder. AI tools have raised expectations for productivity, leading to increased workloads and burnout without corresponding support or recognition. Engineers, especially juniors, are now expected to take on broader responsibilities—ranging from product thinking to deployment—while the hands-on act of writing code has diminished. This shift in roles and expectations is often unacknowledged by leadership, creating a disconnect between those who use AI tools and those who manage the work. The article emphasizes the growing complexity of the engineer's job, the burnout that results from unrealistic demands, and the identity crisis many engineers face as their core role changes. It calls for better leadership, structured training, and realistic expectations to avoid the erosion of trust and talent in the industry."
Whatever. Blog posts about AI that dance around the topic are not that valuable. For a project, you'll hire somebody, not some AI (as bcachefs' author does). What matters is that you trust a person to do the job, and expecting them to churn code is useless. The real benefits of someone appear on their CV (experience, known technologies, human abilities -- communication, empathy, understanding).
AI generated content is an existential threat to human knowledge.
I get instantly turned off by a mere whiff of AI when reading something, and consequently I refuse to foist such garbage on my fellow human beings. But by god if I read another two-line (!!!) comment with an emdash on LinkedIn, I'm going to drop a bollock.
I've unfortunately stopped reading articles before reading comments here as it's all mostly garbage now. I'm not sure what people are trying to accomplish with generating blogs aside from either clout farming or marketing for their companies.
I originally thought AI-assisted writing would help synthesize what felt like original ideas I had, that I just wanted to get out there without the laborious task of editing. I didn't expect the writing to end up feeling so incredibly tired and watered-down, but upon more reflection on how the models actually work, it's not all that surprising. Uniqueness in writing is both in the style/structure and the message, and all AI seems to do is find the local maximum of both. Lately I've found myself going back to writing things myself (not all the time, depends on the task), and wishing there was a way I could just completely eliminate the slop from certain things I look at. I worry about all our minds, and the garbage-in, garbage-out net effect of this.
Te be frank, a lot of companies don't need engineers. They need someone to do the jobs "quickly", "ASAP" and that's it. They are hiring coders masqueraded as programmers who masqueraded as engineers.
I'd say this -- if you really want to be a real engineer, you should avoid many career paths out there. Potentially ANY positions DIRECTLY facing business stakeholders is at best not a good choice, and at worst deprive your already remote chance to be a good engineer. The lower level you move into, the better, because the environment FORCES you to be a true engineer -- either you don't and fail, or you do and keep the job.
The scenario I'm somewhat worried about is that instead of 1 PM, 1 designer and 5 developers, there will be 1 PM, 1 designer and 1 developer. Even if tech employment stays stable or even slightly increases due to Jevons paradox, the share of software developers in tech employment will shrink.
I have a similar problem - AI is making building products easier, but it's made "shipping" a product 100x harder.
I was always a mediocre engineer, and stopping out on a personal usually happened bc "feature XYZ is way too hard to build and I won't spend another three weeks on it". Nowadays anything can be built in a couple of days, scope creep plus "would be cool if it could also do XYZ" makes it harder to walk away from a project and call it done.
But ofc these are personal projects, and I use them daily (like a personal workout system and tracker which I run w/ Claude Code, which love to call Claude Co-Workout). It doesn't "work" as a standalone app. It's mostly a "display system" for whatever CC outputs to me, so I can take the daily workout to the gym.
I got into software bc I liked to put out fun products and projects; I never really liked the process of writing software itself. But either way I'm still running into the "it's harder to put projects out than ever" dilemma, even though the projects are way easier to make, and higher quality than ever.
I'm wondering if it'd be fun to have a "Ask HN: Show us what you've build with (mostly) AI" thread?
AI allows you to accelerate the initial build process, but I think engineering is all about craftsmanship. Today most LLMs have poor taste and chipping away the cruft matters more than ever.
It's an extension of pretending that developer productivity can be measured in lines of code per day, as well as the managerial blindness to the fact that code can have negative value.
> Yeah but a manager can do those things. You don't need an engineer for that.
Can they really? Engineering is about keeping the whole picture in mind so that you know which lever to push and which to not push for a certain goal. Trying until you're lucky can get you to that goal, but it's costly and not sustainable. So you need someone that can work out a model for experimentation in a less costly manner.
Judgment in this case is about deciding which path to direct the project, tradeoffs is being aware that there are other paths that are better in some aspects. And responsibility is acknowledging that a bad decision will bear a personal cost.
Everyone does the above in their own domain. But I don't think I've ever see a manager wanting to do it in the engineering domain. It's more about pushing the engineer to accept the responsibility, but denying them the power of judgment.
The author introduces the term "Supervision Paradox", but IMHO this is simply one instance of the "Automation Paradox" [1], which has been haunting me since I started working in IT.
Interestingly, most jobs don't incentivize working harder or smarter, because it just leads to more work, and then burn-out.
You seem to be right. The author is pumping out one such article per day. I think I've spent more time in forming my comment than they did in generating the article. Oh well :)
> Here is something that gets lost in all the excitement about AI productivity: most software engineers became engineers because they love writing code.
This resonates somewhat, but for a different reason. My mental model is that there are two kinds of developers, the craftsmen and the artists.
The artist considers the act of writing code their actual fulfillment. They thrive on beautifully written code. They are often attached to their code to a point where they will be hurt if someone criticizes (or even deletes) it.
The craftsman understands that code exists to serve a purpose and that is to make someone's life easier. This can be a totally non-technical customer/user that now can get their work done better. It could be another developer that benefits from using a library we wrote.
The artist hates LLMs as it takes away their work and replaces their works of beauty with generic, templatized code.
The craftsman acknowledges that LLMs are another tool in the toolbelt and using them will make them create more benefits for their customers.
For me, one thing that completely changed almost overnight was dealing with junior developers.
In the past, I would give them an assignment and they would take a few days to return with the implementation. I was able to see them struggling, they would learn, they would communicate and get frustrated by their own solution, then iterate.
Today, there are two kinds: 1) the ones who take a marginally smaller amount of time because they’re busy learning, testing and self reviewing, and 2) the ones who watch Twitch or Youtube videos while Claude does the job and come to me after two hours with “done, what’s next” while someone has to comb through the mess.
Leadership might see #2 and think they’re better, faster. But they are just a fucking boat anchor that drags down the whole team while providing nothing more than a shitty interface to an LLM in return.
Here is something that gets lost in all the excitement about AI productivity: most software engineers became engineers because they love writing code.
I think there's a big split between those who derive meaning and enjoyment from the act of writing code or the code itself vs. those who derive it from solving problems (for which the code is often a necessary byproduct). I've worked with many across both of these groups throughout my career.
I am much more in the latter group, and the past 12mo are the most fun I've had writing software in over a decade. For those in the first group, it's easy to see how this can be an existential crisis.
One supposition I see in this and so many other articles is that using AI to generate code results in not knowing how it works. I believe that's only true for "vibe coding", not for engineers using AI to generate code. The difference is in how much you plan, design, and specify upfront.
If you give an AI a very general prompt to make an app that does X, it could build that in any imaginable way. Someone who doesn't know how these things are done wouldn't understand what way was chosen and the trade-offs involved. If they don't even look at the code, they have no idea how it works at all. This is dangerous because they are entirely dependant on the AI to make good decisions and to make any changes in the future.
Someone who practices engineering by researching, considering their options, planning and designing, and creating a specification, leaves nothing up to chance. When the prompt is detailed, the outcome is constrained to the engineer's intent. If they then review the work by seeing that it wrote what they had in mind, they know that it worked and they know that the system design matches their own design. They know how it works because they designed it and they can modify that design. They can and have read the code so they can modify it without the help of the AI.
If you know what code you want generated, reviewing it is easy - just look and see if it's what you expected. If you didn't think ahead about what the code would look like, reviewing is hard because you have to start by figuring out what the codebase even does.
This goes the same for working in small iterations rather than prompting am entire application into existence. We all know how difficult it is to review large changes and why we prefer small changes. Those same rules apply for iterations regardless of whether it was written by a person or an AI.
AI code generation can be helpful if the engineer continues acting as an engineer. It's only when someone who isn't an engineer or when an engineer abdicates their responsibilities to the AI that we end up with an unmaintainable mess. It's no different than amateurs writing scripts and spreadsheets without a full understanding of the implications of their implementation. Good software comes from good engineering, not just generating code; the code is merely the language by which we express our ideas.
> One engineer captured this shift perfectly in a widely shared essay, describing how AI transformed the engineering role from builder to reviewer.
I stopped here. Was this written by an an LLM? This sentence in particular reads exactly like the author supplied said essay as context and this sentence is the LLM's summarization of it. Nowhere is the original article linked, either, further decreasing trust. Moreover, there's an ad at the bottom for some BS "talent" platform to hire the author. This article is probably an LLM generated ad.
My trust is vacated.
This makes me feel that the SWE work/identity crisis is less important than the digital trust crisis.
I've always been motivated by making simple solid foundations in my code the fastest way possible.
So for me being able to have AI wrote certain things extremely fast with me just doing voice to text with my specific approach, is amazing.
I am all in on everything AI and have a discord server just for openclaw and specialized per repo assistants. It really feels like when I'm busy I can throw it an issue tracker number for things.
Then I will ssh via vs code or regular ssh which forwards my ssh key from 1password. My agents have read only repo access and I can push only when I ssh in. Super secure. Sorry for the tangent to the article but I have always loved coding now I love it even more.
Ah, just in time summarizing what we went through recently. Our "leaders" officially added these to our already nonsensical list of goals.
A. Measurably demonstrate that atleast 50% of code/tests are AI generated.
B. X% Faster delivery timelines due to improved productivity tools.
You can't expect to make a pizza in 50% less time just because you bought a faster doughmaker. Specially when you don't even know whether the dough comes out under kneaded, over kneaded or as plain lumps!
AI learned this figure of speech from humans. Even the frequency in which it is used is copied from humans. So you can't really use it to determine if something is written by an AI or not.
LLMs might follow the frequencies of the training data in their raw form, but nobody uses raw LLMs, they use models which have been RLHFed to hell and back to bias them towards specific patterns. Then newer models were trained on the output of those RLHFed models, and further RLHFed, and so on, and so on.
In practice RLHF isn't a survey of every living humans personal style or preferences though, its purpose is to make the model more useful in the eyes of the vendor, mainly by getting cheap third-world labor to nudge the model according to the vendors instructions. You don't get a subservient, sycophantic and "safe" chat interface out of unstructured data without putting your thumb on the scale, hard.
If you think that the article is written by human or that is is unclear, please go ahead. Others here on HN also have pointed out that the author shoots out such lengthy blog posts every day. And you can also see the typical emoji AI slop here: https://www.ivanturkovic.com/services/
But I have no issue with your argumentation whatsoever, it is just that I think there is more than sufficient evidence, and you think there is not.
AI made it so individual developers can outsource their work, not just companies. Maybe there are some lessons to be learned from companies that manage outsourced work successfully.
In writing code, as in writing poetry, the mechanical labor is 5% writing, 45% editing, and 50% reading. But the only thing that makes it yours is you.
I'm not sure if it's made engineering harder, but it's certainly changing what it means to be a good engineer. It's no longer just about writing code. Now it's increasingly about having good taste, making the right decisions, and sometimes just being blessed with the Midas touch.
In any case, I think we should start treating the majority of code as a commodity that will be thrown away sooner or later.
But that was a large part of it. When it was difficult to write correct, well-structured code, that was a major determinant in who would get a job as a developer - ability to design and test came second. Now that generating code is automatic, it's the rest that becomes important. That works well for those of us who could do all of those things, but hurts those whose only ability was to generate code.
The role of an engineer is to produce software that probably works. If you are producing more bugs it's because you're skipping provability. AI is also really good at writing tests and doing test-driven development You can get 100% branching coverage. You use a secondary LLM to review the work and make sure everything follows best practices.
LLMs Can accelerate you if you use best practices and focus on provability and quality, but if you produce slop LLMs will help you produce slop faster.
This section very much resonated with me, even though I still haven't tried any of the AI tools:
... most software engineers became engineers because they love writing code. Not managing code. Not reviewing code. Not supervising systems that produce code. Writing it. The act of thinking through a problem, designing a solution, and expressing it precisely in a language that makes a machine do exactly what you intended. That is what drew most of us to this profession. It is a creative act, a form of craftsmanship, and for many engineers, the most satisfying part of their day.
Actually surprised none of the other comments have picked up on this, as I don't think it's especially about AI. But the periods of my career when I've been actually writing code and solving complicated technical problems have been the most rewarding times in my life, and I'd frequently work on stuff outside work time just because I enjoyed it so much. But the other times when I was just maintaining other people's code, or working on really simple problems with cookie-cutter solutions, I get so demotivated that it's hard to even get started each day. 100%, I do this job for the challenges, not to just spend my days babysitting a fancy code generation tool.
I feel like there's a market out there for a weekly newsletter that summarises all the AI takes like this and collects the one meaningful snippet of insight (if any)
> ...most software engineers became engineers because they love writing code. Not managing code. Not reviewing code. Not supervising systems that produce code. Writing it...
A SWE who bases their entire identity and career around only writing code is not an engineer - they are a code monkey.
The entire point of hiring a Software ENGINEER is to help translate business requirements into technical requirements, and then implement the technical requirements into a tangible feature or product.
The only reason companies buy software is because the alternative means building in-house, and for most industries software is a cost-center not a revenue generator.
I don't pay (US specific) 200K-400K TCs for code monkeys, I pay that TC for Engineers.
And this does a disservice to the large portion of SWEs and former SWEs (like me) who have been in the industry because we are customer-outcome driven (how do we use code to solve a tangible customer need) and not here to write pretty code.
You might be missing that a lot of companies are giddy that the mgmt can just vibe code stuff and there's no opportunity for engineers to be involved, (except for when it crashes?). I use AI tools and they are nice, but the mgmt are mostly not logical and need someone to sort through their bullshit.
Yeah no. Almost all companies I've chatted with - from MSPs to C-Suite of F10s - expect and demand humans-in-the-loop. I'm also on a couple boards and we've aligned on the same expectation as well.
Look, AI/ML and especially LLMs are powerful, but there does remain a degree of instability and non-determinism which will require human intervention to remediate.
That said, there is a lot of dev work in companies that is a cost-center, and those are the portions that will start getting vibe coded and deployed in product with little-to-no oversight (eg. a support portal for SMBs at an enterprise), but the equivalent feature would have already been an afterthought even without LLMs and probably given to a couple SWEs we'd be fine re-orging in a quarter anyhow.
> Yes just not driven or owned by engineers. That's what I'm seeing from company and a few peer's companies
I mean, it depends on the feature/product and how critical it is to the health of the business.
Like I mentioned in my edited comment, there is a lot of dev work in companies that is a cost-center, and those are the portions that will start getting vibe coded and deployed in product with little-to-no oversight (eg. a support portal for SMBs at an enterprise), but the equivalent feature would have already been an afterthought even without LLMs and probably given to a couple SWEs we'd be fine re-orging in a quarter anyhow because we cannot justify spending $500K-750K a year (the backend cost of 3 FT SWEs or Contractors for a company) on a customer form which nets $0 in revenue and is not directly tied with pipeline generation.
We are probably talking past each other but I am saying I see:
Leaders thinking they will basically prompt out new revenue generating features with no human engineers to "figure it out". Not cost centers, low hanging fruit, etc. No these are not giant corps like Google or whatever and likely run by morons, but it was easier when they did not think they were "empowered". There is no opportunity for engineers to "think in higher abstractions" or whatever in these cases.
> Leaders thinking they will basically prompt out new revenue generating features with no human engineers to "figure it out"
Yeah and I'm telling you as one of those leaders that most of the leaders I am meeting with know this is unrealistic and non-tech enterprises.
I think the issue is, a lot of SWEs think their work actually matters to the bottom line (and PMs and execs will massage their ego - I'm guilty of doing this as well) but in reality they don't matter because they are working in a cost-center product or feature.
Every SWE on HN should sit down and ask themselves whether or not
1. The feature they are working on directly generates revenue for their employer.
2. If it does, does it generate revenue equivalent to at least 1% of overall revenue per year.
3. Whether the cost of your team of SWEs+PMs are putting the feature/product in the red (ie. If you are 3 Eng and 1 PM working on a product who's revenue is only $500K/yr).
If all of those questions are negative, your product/feature is at risk from LLMs but was already at risk of being offshored.
While I agree with the thrust of the article: It would help if the article itself wasn't clearly at least partially LLM written. It has many of the shibboleths:
"This is not a minor adjustment. It is a fundamental shift in professional identity. "
"That is not empowerment. That is scope creep without a corresponding increase in compensation"
Honestly, it's lazy. At least edit the bloody thing.
I still feel like I'm writing code. I tell Claude what to write and I am very specific about it. There's still tons of problems for which Claude has no particular solution and it's on me and other humans to figure out what to do. For those cases where I tell it to just go off and write a whole script that I'm not even looking at, those are throwaway / low-value cases I dont care about where previously I'd not have even taken on that particular job.
Developers will become admins. Responsible for supervising and owning the outcomes of increasingly agentic engineering outputs. Trust is the most important thing in business and it’s worth more than ever.
There's always a grain of truth in everything, but the recent article by the Redis guy (sorry for the lack of name) resonated more with me. It's correct that the load in other areas is increasing also because these tools are not there yet when it comes to for lack of a better word "good taste". I work with someone who hasn't written a line of code in a year and it shows and I'm about tired dealing with the slop. But also there's a bunch of things at work that you either did a million times already, aren't really challenging problems just annoying problems hard to solve because of all the cruft, a lot of boring manual work etc. and for this it's just an amazing help to the point I am more relaxed at work than I was previously. And when it does something that is not quite there, I can either fix it manually or tell it to fix it and it usually "gets it". Of course it it ultimately replaces me I will not be relaxed but that's a different topic.
Another little thing that resonated was a tweet that said "some will use it to learn everything and some so that they don't have to learn anything ". Of course it's not really a hard truth. It's questionable how much you can learn without really getting your hands dirty. But I do think people looking at it as a tool that helps then and/or makes them better will profit more than people looking to cut corners.
Not really, I disagree. The article did slightly touched on the real issue on why people enjoy writing code, a “craftsmanship”, yes, coding is NOT engineering, it is writing, and the people who enjoy doing it are actually writers not engineers, and I always keep mentioning that. With AI however, those writers have to be doing the engineering work: the goals, architecture design, managing blueprints, process design and refining, among many other things, and that job is not easy hence why engineers are “supposedly” paid well, AI now took the writing role, and you have to do the engineering one.
> Here is something that gets lost in all the excitement about AI productivity: most software engineers became engineers because they love writing code.
> Not managing code. Not reviewing code. Not supervising systems that produce code. Writing it. The act of thinking through a problem, designing a solution, and expressing it precisely in a language that makes a machine do exactly what you intended. That is what drew most of us to this profession. It is a creative act, a form of craftsmanship, and for many engineers, the most satisfying part of their day.
> Now they are being told to stop.
Yeah, so what I've been realizing from witnessing the Rise of the Agents™ is that there are tons of developers that actually don't like writing code and were in it for the money all along. Nothing wrong with money --- I love the green stuff myself --- but it definitely sucks to have their ambivalence (at best) or disdain (at worst) for the craft imposed on the rest of us.
Feel free to replace `writing code` for most work functions that are enjoyable for some that are being steamrolled by Big AI atm (writing, graphic design, marketing copy, etc.).
It's not about "best" or "traditional," though your bias is clearly set.
"Write me a feature that does _x_" isn't satisfying for me, and, like the author said in the post, it sucks that people that think otherwise are telling me that my way is the "old way", as you put it.
(It's doubly-ironic for me, as I actually like writing documentation!)
> "Write me a feature that does _x_" isn't satisfying for me, and, like the author said in the post, it sucks that people that think otherwise are telling me that my way is the "old way", as you put it.
Ya, no. It’s not that easy. Programming with prompts is much more intellectually challenging, maybe it will be like that in 5-10 years? Right now we are in a productivity increase that is more similar to interactive terminals over using punch cards.
I wonder if the traditionalists simply don’t understand the tech, they seem to have made some whacky unfounded assumptions about it.
Its worth mentioning that this essay has some signs of being either partially AI generated or heavily edited through an LLM. Some of the signs are there (It's not X, it's Y), With the blog having gone from nearly zero activity between 2015 and 2025 to have it explode in posts and text output since then also raises an eyebrow.
It's now almost certain that every submission about LLMs will be written (or assisted) by LLMs.
That this kind of writing puts a great number of us off is not important to many who seek their fortune in this industry.
I hear the cry: "it's my own words the LLM just assisted me". Yes we have to write prompts.
My current policy on this is that if text expresses opinions or has "I" pronouns attached to it then it's written by me. I don't let LLMs speak for me in this way.
I'll let an LLM update code documentation or even write a README for my project but I'll edit that to ensure it doesn't express opinions or say things like "This is designed to help make code easier to maintain" - because that's an expression of a rationale that the LLM just made up.
I use LLMs to proofread text I publish on my blog. I just shared my current prompt for that here: https://simonwillison.net/guides/agentic-engineering-pattern...
my policy as well
I think it is very fair to say that in the same way that LLM's have given english majors access to programming, LLMs have also given engineers access to clear communication.
I'm not shy to admit that LLMs even from 2 years ago could communicate ideas much better than me, especially for a general audience.
It’s not “clear communication” though. The prose that comes out of LLMs is awful - long, vapid paragraphs with distracting tropes. You can ask them to be concise but then they file down all the wrong bits of the sentence and lose meaning. There’s a reason people bother clocking it and complaining about it, it’s *bad*
It’s like everything else that AI can do - looks fine at a glance, or to the inexperienced, but collapses under scrutiny. (By your own admission you’re not a great communicator… how can you tell then?)
>By your own admission you’re not a great communicator… how can you tell then?
Thankfully we don't have to know how to write well to enjoy a well written book.
> LLMs have also given engineers access to clear communication.
A lot of the time, the inability to express an idea clearly hints at some problem with the underlying idea, or in one's conceptualisation of that idea. Writing is a fantastic way to grapple with those issues, and iron out better and clearer iterations of ideas (or one's understanding thereof).
An LLM, on the other hand, will happily spit out a coherent piece of writing defending any nonsense idea you throw at it. Nothing is learnt, nothing is gained from such "writing" (for either the author or the audience).
Recently read a tweet suggesting to ask an llm to defend a position you know to be false. It's quite eye opening. I mean, it shouldn't be, if you did debate club etc. Or know how lawyers and politicians work. But it's quite revealing how it can piece together a good defense, selectively quoting real facts, embuing them with undue weight etc to make the thesis stand quite well.
It's often warping the message or "snapping it to grid", taking off the edge, the unique insight. A lack of clear communication is much more a symptom of unclarity about the intended message, audience, prioritization etc. I don't doubt that you internally have a clear idea but sharing it requires thinking about the intended audience and the diff of their current state of knowledge and doubt and where you want to move their thinking. This is a much bigger part than knowing eloquent vocab and grammar tricks.
It doesn't come naturally to the more introverted type of person who cares about the object level problem and not whatever anyone else may know or doubt, I'll admit this. But slapping LLMs on it is not a great solution.
As someone who has written a few deeply personal articles with LLM assistance, I see the signs and I'm almost certain this was generated off a few bullet points. The repetition and cadence strongly resembles the LLM output. Its the kind of fluff that I remove from a piece, because it lacks humanity and offers little substance.
The comments as well. I won't give away the tells but HN is less and less pleasant to read. Now is the time to cherish your pockets of small scale high quality forums that's not flooded by this stuff yet.
How do you find those pockets?
I guess talking to people and making friends helps. Online, maybe seek out discords and befriend people and they may tell you. Not unlike how you find cool underground clubs.
I do this but it mainly leads to a lot of 1-to-1 conversations, which is fine, but a wider but still “curated” audience would be interesting.
Even the title has that unmistakable smell of punchy LinkedIn profundity.
Even the linkedin profile has a studio-ghibli-style avatar. People are going to assume that he is just an "analog interface" to an LLM. Which is sad, because he might be a good programmer. In fact, I tend to see a lot of english-as-second-language people embrace LLMs as a kind of "equalizer", not realizing that in 2026 it is the opposite (not saying that it's right either way, just pointing out that it is becoming a kind of anti-marketing, like showing up to a conference without any clothing, and getting banned from the conference permanently).
We should probably normalize publishing things in our native languages, and expecting the audience to run it through a translator. (I have been toying with the idea of writing everything in Esperanto (not my native language, but a favorite) and just posting links to auto-translated English versions where the translation is good enough).
EDIT: as someone with friends and family from Eastern Europe, I can tell you that the prevailing attitude is: "everything is bullshit anyway" (which, to be fair, has a lot of truth to it), and so it is no surprise that people would enthusiastically embrace a pocket-sized bullshit factory, hook it up to a fire-hose, and start spraying. We saw it with spam, and we see it now with slop. It won't stop unless the system stops rewarding it.
This was my thought after getting through a few paragraphs as well. At first, I was thinking, this is interesting, maybe worth sharing with colleagues. But then it became too obvious it was AI written or "assisted". Can't take that seriously.
AI made writing words easier. It made communicating well harder.
AI made writing blog posts easier. It made critical thinking harder.
LLMs write this way because people write this way. Maybe not everyone, but enough for it to train the models to do it. Much of my writing reads like an LLM wrote it, but that doesn't make me an LLM.
No, this statement is not true for anything except a base model. Benchmaxxing during RL phase is how you get the advertisement style "punchy" writing, because even though people don't usually write that way it is eye catching and people will vote for the bullet-point emdash slop. I wonder if some lab will be bold enough to do "anti rlhf", lmarena score be damned.
Yes and no. LLMs take all the writing on the Internet (good and bad) and average it out. It's similar to the way generative AI images always have an identifiable, artificial "look". They've averaged out the personality and thereby erased the individuality that went into the efforts the original artists used to create them.
> Much of my writing reads like an LLM wrote it,
I doubt it; share something you wrote prior to, say... 2024.
Why is this sentiment expressed so often ("It was written/edited by AI"?
It seems to bother people, perhaps since it may have been low-effort. Doesn't it not matter as long as the content is good? Otherwise, it seems to be no different than a standard low-quality post.
The formulaic style/cadence/structure/tone is annoying, for one due to its LLM-induced prevalence, but also because it is padded and stretched without adding substance while being dyed in superficialities, and has a weird tendency of meandering through its thematic territory, like the author was slightly distracted or is writing the same thing for the 20th time, or is missing a good editor. Pre-LLM, it might have been an okay-ish, but not great, article. Now it’s just grating and makes you feel like you’re wasting your time reading it.
> Doesn't it not matter as long as the content is good?
"Why is everyone railing against my spam? Doesn't it not matter as long as the deal I am offering is good?"
When people don't want the spam, it is irrelevant whether the spammer is offering a good deal or not.
When I want to read Ai writing (which is not never), I chat with it myself and I prompt it better and get more interesting stuff than these generic insight blogspam.
LLM prose is typically _painful_ to read, overly long, and bullshit-heavy.
I couldn’t even finish it. I picked up on it after reading the other one that made it to the front page the other day.
I don’t think there will be a point in coming to this site if it’s just going to be slop on the front page all the time.
Maybe mods should consider a tag or flag for AI generated content submissions?
AI writings should be notified
It is almost 90% generated using AI text. So many paragraphs to say basically nothing at all.
Like look at this paragraph:
> Junior engineers have traditionally learned by doing the simpler, more task-oriented work. Fixing small bugs. Writing straightforward features. Implementing well-defined tickets. This hands-on work built the foundational understanding that eventually allowed them to take on more complex challenges.
The first sentence was enough to convey everything you needed to know, but it kept on adding words in that AI cadence. The entire post is filled with this style of writing, which, even if it is not AI, is extremely annoying to read.
What would he have written instead?
My point is that there's nothing to be written there "instead", it just is not needed text that is added to make the text longer, typical of AI writing that parrots the same points over and over to make up for word count.
Here's another example from the blog:
> Here is something that gets lost in all the excitement about AI productivity: most software engineers became engineers because they love writing code.
> Not managing code. Not reviewing code. Not supervising systems that produce code. Writing it. The act of thinking through a problem, designing a solution, and expressing it precisely in a language that makes a machine do exactly what you intended. That is what drew most of us to this profession. It is a creative act, a form of craftsmanship, and for many engineers, the most satisfying part of their day.
can just be:
> Most software engineers became engineers because they love writing code. It is a creative act, a form of craftsmanship, and for many engineers, the most satisfying part of their day.
Clarity is something that is taught in every writing class but AI generated text always seems to have this weird cadance as follows: The sound is loud. Not a whimper, not a roar, a simple sound that is very loud. And that's why... blah blah blah.
You have to care about your readers if you're writing something seriously. Throwing just a bunch of text that all mean the same thing in your writing is one of the bigger sins you can do, and that's why most people hate reading AI writing.
I don't know...
The part you'd like to remove ("Not managing code...") may be not required to convey the objective meaning of the sentence, but humans have emotions, too. I could have written stuff like that. To build up a bigger emotional picture.
> The act of thinking through a problem, designing a solution, and expressing it precisely in a language that makes a machine do exactly what you intended.
This sentence may not be relevant for whatever you experience to be the relevant message of the text. But it still says something the remaining paragraph does not. And also something I can relate to.
Also, as LLMs are statistical models, one has to assume that they write like this because their training data tells them to. Because humans write like this. Not when they do professional writing maybe, but when they just ramble. Not all blogs are written by professionals. I'd say most aren't. LLM training data consists mostly of humans rambling.
I also sometimes write long comments on the internet. And while I have no example to check, I feel like I do write such sentences, expanding on details to express more emotional context. Because I'm not a robot and I like writing a lot. I think it's a perfectly human thing to do. I find it sad that "writing more than absolutely needed" is now regarded as a sign of AI writing.
> Because humans write like this. Not when they do professional writing maybe, but when they just ramble.
I keep seeing this assertion and I keep responding "Please, point to the volume of writing with this specific cadence that has a date prior to 2024" and I keep getting... crickets!
You're asserting that this is a common way for humans to write, correct? Should be pretty easy, then, to find a large volume of examples.
Like I said: I think I write like this on some occasions.
I wouldn't know how I would search for examples. I guess you'd have to search old reddit comment threads or something. But yeah, I have no motivation to do that, tbh. It could be that it's hard to find examples because they are scattered about in countless comment threads and single posts on countless platforms. Things I rarely keep links to, things nobody indexed on a large scale before LLMs.
It may be that it wasn't a very popular style of writing, because most people don't like writing a lot and keep their texts on the internet short. LLMs exaggerate this style because they generate exaggerative amounts of text in general. The style wasn't particularly annoying in the past because it wasn't that popular. It's annoying now because LLMs flood the internet with it.
The quoted example in particular didn't appear uncanny to me. And it still doesn't. I can see myself writing like that. I'm sorry I have no example for you. But I'm genuinely unsure whether I'm oblivious to the patterns others see, or whether others see patterns because they want to see them.
It doesn't have to be your writing you show - the assertion is that LLMs write this way because humans write this way.
My counter is that I don't find examples of this type of writing prior to 2024; not with the same frequency of "tells" per paragraph, at any rate.
One of the good book about writing I read was William Zinsser's "On Writing Well". Striving for simplicity and avoiding clutter was the two first principles described in the book. AI writing feels more like ramblings than communication.
Out of curiosity, how do you feel about florid and elaborate writing (e.g. Faulkner, Lispector, Mieville, Mossman, Joyce, Austen, etc)?
I do not think Faulkner would write very good C++ library documentation.
I would read the hell out of Joyce’s Perl 5 documentation, but only after six or seven beers.
There's an art to it. Most human attempts, and every LLM attempt I've ever seen, are awful, sometimes bordering on unreadable, but, as you say, there are a relatively small number of authors who do it well. That doesn't mean that most people should do it.
I'm a French speaker and florid and elaborate writing is something I've grown up with. It can be difficult if you don't know the word or are not used to the style, but it's not boring. AI writing is just repetitive.
When I've used AI for proofreading the suggestions it makes to me is to cut a lot and shorten it. It also gives me examples, never with my voice or style though.
Classic LLM construction.
5 sentence paragraph. First sentence is parataxis claim. Followed by 3 examples in sentence fragments, missing verbs, that familiar cadence. Then the final sentence, in this case also missing a verb.
Pure AI slop.
I feel like it's such a lack of self respect and respect for others when people write using AI on personal blogs.
Reading AI code is very pleasant. It's well annotated and consistent - how I like to read code (although not how I write code LOL). Reading language/opinions is not meant to be this way. It becomes repetitive, boring, and feels super derivative. Why would you turn the main way we communicate with each other into a soulless, tedious, chore?
I think with coding it's because I care* about what the robot is doing. But, with communication, I care about what the person is thinking in their mind, not through the interpretation of the robot. Even if the person's mind isn't as strong. At least then I can size the person up - which is the other reason understanding each other is important and ruined when you put a robot in between.
It's also because we (generally) consider a blog to be human communication and we consider math and programs to be something else.
If you're talking to someone on the phone and halfway through they identify themselves as a bot, surprising you, there's a profound sense of something like betrayal. A moment ago you were having a human connection, and suddenly that vaporized. You were misled and were just talking to an unfeeling robot.
And heartfelt writing is similar. We imagine the human at the other side of the screen and we relate. And when we discover it was a bot, no matter how accurate the sentiment, that relationship vanishes.
But with math and software, it's already sterile from a human connection perspective. It's there for a different purpose. Yes, it can be beautiful, but when we read it we don't tend to build a human connection with the coder.
An interesting exception is comments. When we read the fast inverse square root code and see the "what the fuck..." comment, we instantly relate to the person writing the software. If we later learned that comment was generated by an LLM, we'd lose that connection, again.
IMHO. :)
Totally agree. I'll extend this to email and slacks, too. I cannot stand getting AI written slop from fellow co-workers because they couldn't write the message themselves. Do not even bother to engage with me if you need to put your thoughts through an AI first. It won't go well. People gotta work on themselves a lot more and I think they're using AI to do the opposite.
> I feel like it's such a lack of self respect and respect for others when people write using AI on personal blogs.
Not so sure about the respect aspect: I have lots of self-respect, but I don't generally broadcast respect for random other people when I write my blogs - the most recent one even called readers stupid, IIRC!
I feel it's more a matter of expression of contempt: if you can't be bothered to write it, WTF are you expecting people to read it?
If you're writing an online blog with the intention that people are to read it, there should be some semblance of respect for their time and respect for your craft. IMO.
Yeah the article is 100% AI generated according to Pangram
Why were flags manually removed? It's obvious that the community does not want to read AI slop.
It's funny how seemingly easy it is to tell articles like this have that AI generated whiff to them. The first bit that raised my suspicion was the "The Identity Crisis Nobody Talks About" headline. This "The x nobody talks about" feels like such a GenAI thing.
I hate it. I couldn't read much more after that.
> you are not imagining things. The job changed. The expectations changed. And nobody sent a memo.
Looks like something AI would say. Regardless of how it really was written
Its really long winded. The entire thing could have been a couple bullet points.
Admittedly it was so long and basic, I stopped halfway.
> The entire thing could have been a couple bullet points
It probably was
Yep the article is 100% AI generated according to Pangram
Why is AI such a bad writer? Phrasing like this feels like reading Fox News.
I saw someone point out something like: ai makes every sentence count. There’s no building or allowing a point to breathe. Every sentence is an axiom to get the meaning across, and its so grating
It's an interesting way to view it, because what happens in fact is likely the opposite - AI is asked to expand a few bullet points into a blog post
Maybe that's why the writing feels so terrible. The AI is attempting to maximize every sentence while simultaneously expanding on just a few actually meaningful points. And the net result of that dissonance is this rage-inducing vapidity. It's the written equivalent of the Uncanny Valley.
I think it has got past the uncanny valley really - it does read like a human, just a very attention-seeking one, like your typical LinkedIn salesman.
That's probably just default settings though - I asked it to rewrite, and most of the tell-tale signs are gone as I can see (apart from the em-dash)
https://chatgpt.com/s/t_69a46b290fb08191ad3bd93066b8cad4
Making fluff sound grandiose is probably what makes so grating.
> Why is AI such a bad writer?
A better question is "Why can't the devs producing code with AI spot the same poor patterns in the code they are generating?"
Maybe my point is that, to a poor speaker of English, the AI blogpost looks good and reads well. In much the same way, to a poor programmer, the AI produced code looks good and reads well.
In a nutshell, if it generates poor English, WTF would anyone think it generates anything but poor code?
To be honest it still feels crazy that AI is a writer at all. But yeah, not a good one
Article definitely has an AI writing style
One problem I have seen IRL is AI deployment mistakes and IMO Vibe Coders need an IT/Dev Father Figure type to avoid these simple mistakes. Here is one example:
A surgeon (no coding experience) used Claude to write a web app to track certain things about procedures he had done. He deployed the app on a web hosting provided (PHP LAMP stack). He wanted to share it with other doctors, but wasn't sure if it was 'secure' or not. He asked me to read the code and visit the site and provide my opinion.
The code was pretty reasonable. The DB schema was good. And it worked as expected. However, he routinely zipped up the entire project and placed the zip files in the web root and he had no index file. So anyone who navigated to the website saw the backups named Jan-2026.backup, etc. and could download them.
The backups contained the entire DB, all the project secrets, DB connection strings, API credentials, AWS keys, etc.
He had no idea what an 'index' file was and why that was important. Last I heard he was going to ask Claude how to secure it.
Claude is crazy good at coding but it won't hold your hand when it comes to the unknown unknowns that the regular joe like this doesn't know.
Lots of comments here critiquing the article for allegedly being written by AI.
I see the post is even flagged now.
Irrespective of who wrote it or how it was written, the essay is packed with wisdom.
I’ve been programming for 30+ years and leading teams for the last 20 - and I found the essay deeply insightful.
I realise I’m a sample size of 1, but just figured I’d comment here to advocate against this post being flagged. Surprised that it is.
I'm not opposed to AI generated text in principle. But not knowing how it was written is problematic, because it can change the meaning of the text. Take this paragraph for instance:
"From my experience building and scaling teams in fintech and high-traffic platforms, I can tell you that role expansion without clear boundaries always leads to the same outcome: people try to do everything, nothing gets done with the depth it requires, and burnout follows."
This reads like a first person account of someone's experience. Is it though? If it's nobody's experience then it robs this text of its meaning. If it is somebody's experience and that person used AI to improve their style then that's absolutely fine with me.
I would prefer to have the prompt he used to generate the article. Similarly, for compiled binaries, I would rather have the source code that produced them, instead of just an .exe file.
I agree with you. I found it interesting too.
why was it flagged?
> Here is something that gets lost in all the excitement about AI productivity: most software engineers became engineers because they love writing code.
1) I guess I am not included in the set named "most software engineers."
2) If the title is "Software Engineer," I think I should be engineering, not coding.
This has probably been beaten to death, but I think this is the biggest disciminating question between "pro ai" and "against ai" in the software world is: "Dp you do (this) becuase you like writing code, or because you like building things for the world?"
Of course I don't think it's a binary decision.
Although I more more motivated by building things, I do somewhat miss the programmer flow state I used to get more often.
It's a different skillset and way of thinking. Engineers tend to think vertically deep on technical problems. With AI, you have to think horizontally broad and vertically up on the architectural problem. The trick is to be comfortable relegating the details to AI.
One concrete example of this realization was when I was researching how to optimize my claude code environment with agents, skills, etc. I read a lot of technical documents on how these supplemental plugins work and how to create them. After an hour of reading through all this, I realized I could just ask Claude to optimize the environment for me given the project context. So I did, and it was able to point out plugins, skills, agents that I can install or create. I gave it permission to create them and it all worked out.
This was a case of where I should not think more technically deeper, but at a more "meta" level to define the project enough for Claude to figure out how to optimize the environment. Whether that gave real gains is another question of course. But I have anecdotally observed faster results and less token usage due context caching and slightly more tools-directed prompts.
The post is right superficially. It made being an engineer harder because it took away the easy parts that anyone can do and it forces engineers to think of the hard ones.
No jobs get easier with automation - they always move a step up in abstraction level.
An accountant who was super proficient in adding numbers no longer can rely on those skills once calculator was invented.
> it took away the easy parts
This is the key. I haven't found that things have become harder. The hard parts are still hard, and those have been the most important and prominent parts of my job once I reached a certain level.
Exactly. I'm a principal software engineer. My job is a lot less about writing code and a lot more about planning, designing, reviewing, and training.
However, I do wonder how we will train juniors to become seniors. Perhaps the answer is that the curriculum changes from coding and data structures to architecture and design which was typically a last minute addition in college.
I disagree on making it easier. I'm very capable of writing code in multiple languages but it's boring and monotonous. It's getting in the way of me building the system I have in mind. I prefer the engineering (design) to writing. If I can describe my system design to something (a junior developer or an AI) and see it come to life quickly, that's great; it lets me spend more time on designing the system, or perhaps designing more systems.
That said, there are plenty of amateurs who find coding to be approachable and system design to me daunting. For them, eliminating coding and moving the focus to system design would be a nightmare.
> The post is right superficially. It made being an engineer harder because it took away the easy parts that anyone can do and it forces engineers to think of the hard ones.
I dunno about that. Look at blogging as an example - AI took away the "easy"[1] part of blogging, and now we are left with 90% crap AI-generated "articles" like the one you just read.
I feel it's the other way around - AI took away the hard parts, of both blogging and programming, and now what have to look forward to every single damn day is a deluge of AI slop of absolutely poor quality.
Continuing with the literature analogy (because this article was written by an AI), adding AI as a tool for authors isn't producing the next Terry Pratchett quicker, it's delaying the production of the next Terry Pratchett because the next Terry Pratchett will be drowned out by an unstoppable volume of AI slop.
After all, if you can't recognise obvious AI blog posts, what makes you think you can recognise poor code?
---------------------
[1] I am using the term as you are using it. I don't really believe that it took away the easy part.
The post is 100% AI generated according to Pangram
Yes
The post itself is 100% AI written https://www.pangram.com/history/6572a5c4-f548-46e2-977d-9813...
Reminder that AI-writing detection tools are largely junk.
Pangram is reliable. Quoting from their website
> Pangram achieves essentially zero false positive rates and false negative rates on medium-length to long passages.
Note Pangram is not like the others and has heavy academic research on statistical method soundness
> Reminder that AI-writing detection tools are largely junk.
In what way? False positives or false negatives?
Ironic
It might be worth mentioning studies that show the lack of productivity gains from LLM usage. These posts take it as an unequivocal given. Management might still have the expectations that certain tasks are faster. But they aren’t always connected to reality because they’re not thinking as engineers.
There's nothing new about this pattern. When the tractor was invented, the farmer didn't get to knock off early. He just started producing 10x more. Then the tractors got bigger and more powerful, and the things you used them with got more sophisticated too and suddenly you're producing 100x more.
And now there are 1% of the number of farmers that there used to be
And the only people who could afford to tractor at scale are Cargill/Monsanto who bought out most of the small/medium-sized farms while leaving farms that didn't take the offer to slowly die...
And yet there isn't widespread unemployment. Fewer farmers were needed so fewer people became farmers. Food became cheap and plentiful. Everyone else went on to do other things that they couldn't afford to do before. Software will do the same; we will make more software with fewer people and it will become ubiquitous to the point that people will just quickly generate whatever software they need rather than do many monotonous tasks manually.
That argument does people who have invested decades of their lives into software engineering a lot of good.
> most software engineers became engineers because they love writing code.
I don't think this is true. I'm pretty sure most of them do it because it pays good salary.
"the skills that the new engineering landscape actually requires: system design, architectural thinking, product reasoning, security awareness, and the ability to critically evaluate code they did not write."
These, surely, are the skills they always needed? Anyone who didn't have these skills was little more than a human chatgpt already, receiving prompts and simply presenting the results to someone for evaluation.
That’s a great point, but yes: a lot of devs were nothing more than a glorified LLM and during reviews were just an expensive linter. Reality is catching up to those.
This is what hapenned 10 years ago, when machine translation entered the professional translation business. Post-editing the translation was often slower than human translating sentences from scratch. Now nearly the whole industry is post-editing machine translations, and there is more and more content that is not even post-edited.
AI made programming A LOT MORE FUN for me.
What I never enjoyed was looking up the cumbersome details of a framework, a programming language or an API. It's really BORING to figure out that tool X calls paging params page and pageSize while Y offset and limit. Many other examples can be added. For me, I feel at home in so many new programming languages and frameworks that I can really ship ideas. AI really helps with all the boring stuff.
Same here. I like bringing ideas to life; code is just a means to an end. I can now give detailed designs to an AI and let it write the hundreds of lines of code in just minutes, and with far fewer typos than I would make. It's still not perfect - I have to review it all - but if I give it a proper spec in generally creates exactly what I had in mind.
Yeah, GUI code, for example is notoriously chatty. Forms, charts, etc.
AI makes using them a breeze.
Agree, it’s made programming so much fun. The other day I wrote a C# app just because it was the best language for the job, I’ve never touched .Net in my life. Worked great, clients loved it.
I can actually build nice UIs as a traditional ML engineer (no more streamlit crap). People are using them and genuinely impressed by them
I can fly through Rust and C++ code, which used to take ages of debugging.
The main thing that is clear to me is that most of the ecosystem will likely converge toward Rust or C++ soon. Languages like Python or Ruby or even Go are just too slow and messy, why would you use them at all if you can write in Rust just as fast? I expect those languages to die off in the next several years
I played the blog post game (using chatgpt).
Prompt: "The text of the blog article is copy-pasted below. Please summarize it in one parargaph."
Result: "The blog article explores the paradox that while AI has made coding easier by automating many tasks, it has also made the role of software engineers significantly harder. AI tools have raised expectations for productivity, leading to increased workloads and burnout without corresponding support or recognition. Engineers, especially juniors, are now expected to take on broader responsibilities—ranging from product thinking to deployment—while the hands-on act of writing code has diminished. This shift in roles and expectations is often unacknowledged by leadership, creating a disconnect between those who use AI tools and those who manage the work. The article emphasizes the growing complexity of the engineer's job, the burnout that results from unrealistic demands, and the identity crisis many engineers face as their core role changes. It calls for better leadership, structured training, and realistic expectations to avoid the erosion of trust and talent in the industry."
Whatever. Blog posts about AI that dance around the topic are not that valuable. For a project, you'll hire somebody, not some AI (as bcachefs' author does). What matters is that you trust a person to do the job, and expecting them to churn code is useless. The real benefits of someone appear on their CV (experience, known technologies, human abilities -- communication, empathy, understanding).
AI generated content is an existential threat to human knowledge.
This article is obviously written by ai and it’s just painful for me to read ChatGPT’s writing style day in and day out
I get instantly turned off by a mere whiff of AI when reading something, and consequently I refuse to foist such garbage on my fellow human beings. But by god if I read another two-line (!!!) comment with an emdash on LinkedIn, I'm going to drop a bollock.
I've unfortunately stopped reading articles before reading comments here as it's all mostly garbage now. I'm not sure what people are trying to accomplish with generating blogs aside from either clout farming or marketing for their companies.
I originally thought AI-assisted writing would help synthesize what felt like original ideas I had, that I just wanted to get out there without the laborious task of editing. I didn't expect the writing to end up feeling so incredibly tired and watered-down, but upon more reflection on how the models actually work, it's not all that surprising. Uniqueness in writing is both in the style/structure and the message, and all AI seems to do is find the local maximum of both. Lately I've found myself going back to writing things myself (not all the time, depends on the task), and wishing there was a way I could just completely eliminate the slop from certain things I look at. I worry about all our minds, and the garbage-in, garbage-out net effect of this.
Te be frank, a lot of companies don't need engineers. They need someone to do the jobs "quickly", "ASAP" and that's it. They are hiring coders masqueraded as programmers who masqueraded as engineers.
I'd say this -- if you really want to be a real engineer, you should avoid many career paths out there. Potentially ANY positions DIRECTLY facing business stakeholders is at best not a good choice, and at worst deprive your already remote chance to be a good engineer. The lower level you move into, the better, because the environment FORCES you to be a true engineer -- either you don't and fail, or you do and keep the job.
Regarding expanding role:
The scenario I'm somewhat worried about is that instead of 1 PM, 1 designer and 5 developers, there will be 1 PM, 1 designer and 1 developer. Even if tech employment stays stable or even slightly increases due to Jevons paradox, the share of software developers in tech employment will shrink.
I think more likely - no PM, no Designer, one stressed out Mega PM-D-SWE
1 PM, 1 designer, 1 developer, and 10 SREs to clean up the mess.
I have a similar problem - AI is making building products easier, but it's made "shipping" a product 100x harder.
I was always a mediocre engineer, and stopping out on a personal usually happened bc "feature XYZ is way too hard to build and I won't spend another three weeks on it". Nowadays anything can be built in a couple of days, scope creep plus "would be cool if it could also do XYZ" makes it harder to walk away from a project and call it done.
But ofc these are personal projects, and I use them daily (like a personal workout system and tracker which I run w/ Claude Code, which love to call Claude Co-Workout). It doesn't "work" as a standalone app. It's mostly a "display system" for whatever CC outputs to me, so I can take the daily workout to the gym.
I got into software bc I liked to put out fun products and projects; I never really liked the process of writing software itself. But either way I'm still running into the "it's harder to put projects out than ever" dilemma, even though the projects are way easier to make, and higher quality than ever.
I'm wondering if it'd be fun to have a "Ask HN: Show us what you've build with (mostly) AI" thread?
Ironic the article is 100% AI generated according to Pangram
AI allows you to accelerate the initial build process, but I think engineering is all about craftsmanship. Today most LLMs have poor taste and chipping away the cruft matters more than ever.
AI may have sped up coding, but it also exposed that real engineering is about judgment, trade-offs, and responsibility—not just producing code.
We already knew this, but in the light of this AI hysteria we pretend like coding speed has always been the bottleneck
It's an extension of pretending that developer productivity can be measured in lines of code per day, as well as the managerial blindness to the fact that code can have negative value.
Yeah but a manager can do those things. You don't need an engineer for that.
Maybe this is not entirely true yet, but it most likely will be in the near future.
Managers' jobs are more at risk here than the engineers'.
> Yeah but a manager can do those things. You don't need an engineer for that.
Can they really? Engineering is about keeping the whole picture in mind so that you know which lever to push and which to not push for a certain goal. Trying until you're lucky can get you to that goal, but it's costly and not sustainable. So you need someone that can work out a model for experimentation in a less costly manner.
Judgment in this case is about deciding which path to direct the project, tradeoffs is being aware that there are other paths that are better in some aspects. And responsibility is acknowledging that a bad decision will bear a personal cost.
Everyone does the above in their own domain. But I don't think I've ever see a manager wanting to do it in the engineering domain. It's more about pushing the engineer to accept the responsibility, but denying them the power of judgment.
I love learning ChatGPT's opinion on things.
The author introduces the term "Supervision Paradox", but IMHO this is simply one instance of the "Automation Paradox" [1], which has been haunting me since I started working in IT.
Interestingly, most jobs don't incentivize working harder or smarter, because it just leads to more work, and then burn-out.
[1] https://en.wikipedia.org/wiki/Automation#Paradox_of_automati...
Phrases like: "identity crisis", "burnout machine", "supervision paradox", "acceleration trap", "workload creep" are just AI slop.
You seem to be right. The author is pumping out one such article per day. I think I've spent more time in forming my comment than they did in generating the article. Oh well :)
> Here is something that gets lost in all the excitement about AI productivity: most software engineers became engineers because they love writing code.
This resonates somewhat, but for a different reason. My mental model is that there are two kinds of developers, the craftsmen and the artists.
The artist considers the act of writing code their actual fulfillment. They thrive on beautifully written code. They are often attached to their code to a point where they will be hurt if someone criticizes (or even deletes) it.
The craftsman understands that code exists to serve a purpose and that is to make someone's life easier. This can be a totally non-technical customer/user that now can get their work done better. It could be another developer that benefits from using a library we wrote.
The artist hates LLMs as it takes away their work and replaces their works of beauty with generic, templatized code.
The craftsman acknowledges that LLMs are another tool in the toolbelt and using them will make them create more benefits for their customers.
For me, one thing that completely changed almost overnight was dealing with junior developers.
In the past, I would give them an assignment and they would take a few days to return with the implementation. I was able to see them struggling, they would learn, they would communicate and get frustrated by their own solution, then iterate.
Today, there are two kinds: 1) the ones who take a marginally smaller amount of time because they’re busy learning, testing and self reviewing, and 2) the ones who watch Twitch or Youtube videos while Claude does the job and come to me after two hours with “done, what’s next” while someone has to comb through the mess.
Leadership might see #2 and think they’re better, faster. But they are just a fucking boat anchor that drags down the whole team while providing nothing more than a shitty interface to an LLM in return.
Here is something that gets lost in all the excitement about AI productivity: most software engineers became engineers because they love writing code.
I think there's a big split between those who derive meaning and enjoyment from the act of writing code or the code itself vs. those who derive it from solving problems (for which the code is often a necessary byproduct). I've worked with many across both of these groups throughout my career.
I am much more in the latter group, and the past 12mo are the most fun I've had writing software in over a decade. For those in the first group, it's easy to see how this can be an existential crisis.
One supposition I see in this and so many other articles is that using AI to generate code results in not knowing how it works. I believe that's only true for "vibe coding", not for engineers using AI to generate code. The difference is in how much you plan, design, and specify upfront.
If you give an AI a very general prompt to make an app that does X, it could build that in any imaginable way. Someone who doesn't know how these things are done wouldn't understand what way was chosen and the trade-offs involved. If they don't even look at the code, they have no idea how it works at all. This is dangerous because they are entirely dependant on the AI to make good decisions and to make any changes in the future.
Someone who practices engineering by researching, considering their options, planning and designing, and creating a specification, leaves nothing up to chance. When the prompt is detailed, the outcome is constrained to the engineer's intent. If they then review the work by seeing that it wrote what they had in mind, they know that it worked and they know that the system design matches their own design. They know how it works because they designed it and they can modify that design. They can and have read the code so they can modify it without the help of the AI.
If you know what code you want generated, reviewing it is easy - just look and see if it's what you expected. If you didn't think ahead about what the code would look like, reviewing is hard because you have to start by figuring out what the codebase even does.
This goes the same for working in small iterations rather than prompting am entire application into existence. We all know how difficult it is to review large changes and why we prefer small changes. Those same rules apply for iterations regardless of whether it was written by a person or an AI.
AI code generation can be helpful if the engineer continues acting as an engineer. It's only when someone who isn't an engineer or when an engineer abdicates their responsibilities to the AI that we end up with an unmaintainable mess. It's no different than amateurs writing scripts and spreadsheets without a full understanding of the implications of their implementation. Good software comes from good engineering, not just generating code; the code is merely the language by which we express our ideas.
> One engineer captured this shift perfectly in a widely shared essay, describing how AI transformed the engineering role from builder to reviewer.
I stopped here. Was this written by an an LLM? This sentence in particular reads exactly like the author supplied said essay as context and this sentence is the LLM's summarization of it. Nowhere is the original article linked, either, further decreasing trust. Moreover, there's an ad at the bottom for some BS "talent" platform to hire the author. This article is probably an LLM generated ad.
My trust is vacated.
This makes me feel that the SWE work/identity crisis is less important than the digital trust crisis.
I've always been motivated by making simple solid foundations in my code the fastest way possible.
So for me being able to have AI wrote certain things extremely fast with me just doing voice to text with my specific approach, is amazing.
I am all in on everything AI and have a discord server just for openclaw and specialized per repo assistants. It really feels like when I'm busy I can throw it an issue tracker number for things.
Then I will ssh via vs code or regular ssh which forwards my ssh key from 1password. My agents have read only repo access and I can push only when I ssh in. Super secure. Sorry for the tangent to the article but I have always loved coding now I love it even more.
Ah, just in time summarizing what we went through recently. Our "leaders" officially added these to our already nonsensical list of goals.
A. Measurably demonstrate that atleast 50% of code/tests are AI generated.
B. X% Faster delivery timelines due to improved productivity tools.
You can't expect to make a pizza in 50% less time just because you bought a faster doughmaker. Specially when you don't even know whether the dough comes out under kneaded, over kneaded or as plain lumps!
> This is not a contradiction. It is the reality ...
> That is not an upgrade. That is a career identity crisis.
This is not X. It is Y.
> The trap is ...
> This gap matters ...
> This is not empowerment ...
> This is not a minor adjustment...
Your typical AI slop rhetorical phrasing.
Phrases like: "identity crisis", "burnout machine", "supervision paradox", "acceleration trap", "workload creep"
These sound analytical but are lightly defined. They function as named concepts without rigorous definition or empirical grounding.
There might be some good arguments in the article, but AI slop remains AI slop.
N=1. I'm not convinced yet.
N=2 form the same author: https://www.ivanturkovic.com/2026/02/24/first-1000-lines-det...
> AI is an in-context learner, not a standards enforcer.
> The AI is not judging your code. It is learning from it.
> Speed without structure is not speed. It is borrowed time.
> This is not about premature optimization or over-engineering. It is about giving the AI the patterns it needs to work effectively on your behalf.
> This is not a theoretical distinction. It is the single most important practical reality of working with AI coding tools in 2026.
Its not this, its that.
> But here is the part nobody wants to hear: the reverse is equally true.
> The result was transformative.
> Here is why.
If you want I can provide N=3 with the same AI pattern and phrases again.
AI learned this figure of speech from humans. Even the frequency in which it is used is copied from humans. So you can't really use it to determine if something is written by an AI or not.
LLMs might follow the frequencies of the training data in their raw form, but nobody uses raw LLMs, they use models which have been RLHFed to hell and back to bias them towards specific patterns. Then newer models were trained on the output of those RLHFed models, and further RLHFed, and so on, and so on.
The H in RLHF stands for human. If humans didn't use the expression, then the LLM wouldn't.
In practice RLHF isn't a survey of every living humans personal style or preferences though, its purpose is to make the model more useful in the eyes of the vendor, mainly by getting cheap third-world labor to nudge the model according to the vendors instructions. You don't get a subservient, sycophantic and "safe" chat interface out of unstructured data without putting your thumb on the scale, hard.
> AI learned this figure of speech from humans. Even the frequency in which it is used is copied from humans.
Can you point to examples of these patterns with the same frequency in any written content dated any time prior to 2024?
If you think that the article is written by human or that is is unclear, please go ahead. Others here on HN also have pointed out that the author shoots out such lengthy blog posts every day. And you can also see the typical emoji AI slop here: https://www.ivanturkovic.com/services/
But I have no issue with your argumentation whatsoever, it is just that I think there is more than sufficient evidence, and you think there is not.
Bro, it reeks of AI.
I find this a better argument.
AI made it so individual developers can outsource their work, not just companies. Maybe there are some lessons to be learned from companies that manage outsourced work successfully.
In writing code, as in writing poetry, the mechanical labor is 5% writing, 45% editing, and 50% reading. But the only thing that makes it yours is you.
This exact same article has been rewritten and posted here at least 10 times now
I'm not sure if it's made engineering harder, but it's certainly changing what it means to be a good engineer. It's no longer just about writing code. Now it's increasingly about having good taste, making the right decisions, and sometimes just being blessed with the Midas touch.
In any case, I think we should start treating the majority of code as a commodity that will be thrown away sooner or later.
I wrote something about this here: https://chatbotkit.com/reflections/most-code-deserves-to-die - it was inspired by another conversation on HN.
> It's no longer just about writing code
It never was
But that was a large part of it. When it was difficult to write correct, well-structured code, that was a major determinant in who would get a job as a developer - ability to design and test came second. Now that generating code is automatic, it's the rest that becomes important. That works well for those of us who could do all of those things, but hurts those whose only ability was to generate code.
Maybe 30-40 years ago. For as long as I've been around the basic ability to write good code was secondary to a long list of other skills.
That's different than saying a lot of people *believed* writing code was the hardest/most important part.
And now even more so.
> Burnout was reported by 62 percent of associates and 61 percent of entry-level workers. Among C-suite leaders? Just 38 percent.
That can't be right?
Previous: https://www.ivanturkovic.com/2026/02/25/ai-made-writing-code...
Yep, my day to day is now miserable, LLMs have ruined everything that made this field fun, and greatly enhanced everything that made it suck.
Remember when even a month ago you'd come on HN and people were like It'S jUsT a StAtIsTiCaL pArRoT?
The role of an engineer is to produce software that probably works. If you are producing more bugs it's because you're skipping provability. AI is also really good at writing tests and doing test-driven development You can get 100% branching coverage. You use a secondary LLM to review the work and make sure everything follows best practices.
LLMs Can accelerate you if you use best practices and focus on provability and quality, but if you produce slop LLMs will help you produce slop faster.
Ironic the article is 100% AI generated according to Pangram
Is it adding to the conversation to add that observation so many times?
This section very much resonated with me, even though I still haven't tried any of the AI tools:
... most software engineers became engineers because they love writing code. Not managing code. Not reviewing code. Not supervising systems that produce code. Writing it. The act of thinking through a problem, designing a solution, and expressing it precisely in a language that makes a machine do exactly what you intended. That is what drew most of us to this profession. It is a creative act, a form of craftsmanship, and for many engineers, the most satisfying part of their day.
Actually surprised none of the other comments have picked up on this, as I don't think it's especially about AI. But the periods of my career when I've been actually writing code and solving complicated technical problems have been the most rewarding times in my life, and I'd frequently work on stuff outside work time just because I enjoyed it so much. But the other times when I was just maintaining other people's code, or working on really simple problems with cookie-cutter solutions, I get so demotivated that it's hard to even get started each day. 100%, I do this job for the challenges, not to just spend my days babysitting a fancy code generation tool.
> code is for humans to read
Is this still true?
I feel like there's a market out there for a weekly newsletter that summarises all the AI takes like this and collects the one meaningful snippet of insight (if any)
> ...most software engineers became engineers because they love writing code. Not managing code. Not reviewing code. Not supervising systems that produce code. Writing it...
A SWE who bases their entire identity and career around only writing code is not an engineer - they are a code monkey.
The entire point of hiring a Software ENGINEER is to help translate business requirements into technical requirements, and then implement the technical requirements into a tangible feature or product.
The only reason companies buy software is because the alternative means building in-house, and for most industries software is a cost-center not a revenue generator.
I don't pay (US specific) 200K-400K TCs for code monkeys, I pay that TC for Engineers.
And this does a disservice to the large portion of SWEs and former SWEs (like me) who have been in the industry because we are customer-outcome driven (how do we use code to solve a tangible customer need) and not here to write pretty code.
You might be missing that a lot of companies are giddy that the mgmt can just vibe code stuff and there's no opportunity for engineers to be involved, (except for when it crashes?). I use AI tools and they are nice, but the mgmt are mostly not logical and need someone to sort through their bullshit.
Yeah no. Almost all companies I've chatted with - from MSPs to C-Suite of F10s - expect and demand humans-in-the-loop. I'm also on a couple boards and we've aligned on the same expectation as well.
Look, AI/ML and especially LLMs are powerful, but there does remain a degree of instability and non-determinism which will require human intervention to remediate.
That said, there is a lot of dev work in companies that is a cost-center, and those are the portions that will start getting vibe coded and deployed in product with little-to-no oversight (eg. a support portal for SMBs at an enterprise), but the equivalent feature would have already been an afterthought even without LLMs and probably given to a couple SWEs we'd be fine re-orging in a quarter anyhow.
Yes just not driven or owned by engineers. That's what I'm seeing from company and a few peer's companies.
> but there does remain a degree of instability and non-determinism which will require human intervention to remediate.
I agree.
> Yes just not driven or owned by engineers. That's what I'm seeing from company and a few peer's companies
I mean, it depends on the feature/product and how critical it is to the health of the business.
Like I mentioned in my edited comment, there is a lot of dev work in companies that is a cost-center, and those are the portions that will start getting vibe coded and deployed in product with little-to-no oversight (eg. a support portal for SMBs at an enterprise), but the equivalent feature would have already been an afterthought even without LLMs and probably given to a couple SWEs we'd be fine re-orging in a quarter anyhow because we cannot justify spending $500K-750K a year (the backend cost of 3 FT SWEs or Contractors for a company) on a customer form which nets $0 in revenue and is not directly tied with pipeline generation.
We are probably talking past each other but I am saying I see:
Leaders thinking they will basically prompt out new revenue generating features with no human engineers to "figure it out". Not cost centers, low hanging fruit, etc. No these are not giant corps like Google or whatever and likely run by morons, but it was easier when they did not think they were "empowered". There is no opportunity for engineers to "think in higher abstractions" or whatever in these cases.
> Leaders thinking they will basically prompt out new revenue generating features with no human engineers to "figure it out"
Yeah and I'm telling you as one of those leaders that most of the leaders I am meeting with know this is unrealistic and non-tech enterprises.
I think the issue is, a lot of SWEs think their work actually matters to the bottom line (and PMs and execs will massage their ego - I'm guilty of doing this as well) but in reality they don't matter because they are working in a cost-center product or feature.
Every SWE on HN should sit down and ask themselves whether or not
1. The feature they are working on directly generates revenue for their employer.
2. If it does, does it generate revenue equivalent to at least 1% of overall revenue per year.
3. Whether the cost of your team of SWEs+PMs are putting the feature/product in the red (ie. If you are 3 Eng and 1 PM working on a product who's revenue is only $500K/yr).
If all of those questions are negative, your product/feature is at risk from LLMs but was already at risk of being offshored.
See also The Mythical Agent-Month https://wesmckinney.com/blog/mythical-agent-month/
fuck me another ai written post
it's all so fucking tiresome
Why is this flagged?
Why was this flagged? I don’t get it.
While I agree with the thrust of the article: It would help if the article itself wasn't clearly at least partially LLM written. It has many of the shibboleths:
"This is not a minor adjustment. It is a fundamental shift in professional identity. "
"That is not empowerment. That is scope creep without a corresponding increase in compensation"
Honestly, it's lazy. At least edit the bloody thing.
Writing code was never particularly difficult in the first place.
Yes its accurate. Ai is a zero sum game for the devs and as usual we cheered for it while they were feeding it all opensource projects
THERE IS A NEARLY INFINITE DEMAND FOR SKEPTICAL AND COMFORTING TAKES ABOUT AI CODING
THE MARKET WILL FILL THAT VOID
IT DOES NOT MAKE IT TRUE
Yeah, and for every take like that, there’s an overhyped one that’s just trying to sell you the sensation.
I still feel like I'm writing code. I tell Claude what to write and I am very specific about it. There's still tons of problems for which Claude has no particular solution and it's on me and other humans to figure out what to do. For those cases where I tell it to just go off and write a whole script that I'm not even looking at, those are throwaway / low-value cases I dont care about where previously I'd not have even taken on that particular job.
Pangram detects this as a 100% AI generated article. Downvote this hustling slop to oblivion.
Also, check out the dude's linkedin: https://www.linkedin.com/in/ivanturkovic/
Wish we could downvote articles. Is it legitimate to flag AI slop?
Developers will become admins. Responsible for supervising and owning the outcomes of increasingly agentic engineering outputs. Trust is the most important thing in business and it’s worth more than ever.
There's always a grain of truth in everything, but the recent article by the Redis guy (sorry for the lack of name) resonated more with me. It's correct that the load in other areas is increasing also because these tools are not there yet when it comes to for lack of a better word "good taste". I work with someone who hasn't written a line of code in a year and it shows and I'm about tired dealing with the slop. But also there's a bunch of things at work that you either did a million times already, aren't really challenging problems just annoying problems hard to solve because of all the cruft, a lot of boring manual work etc. and for this it's just an amazing help to the point I am more relaxed at work than I was previously. And when it does something that is not quite there, I can either fix it manually or tell it to fix it and it usually "gets it". Of course it it ultimately replaces me I will not be relaxed but that's a different topic.
Another little thing that resonated was a tweet that said "some will use it to learn everything and some so that they don't have to learn anything ". Of course it's not really a hard truth. It's questionable how much you can learn without really getting your hands dirty. But I do think people looking at it as a tool that helps then and/or makes them better will profit more than people looking to cut corners.
it didn't do shit
Not really, I disagree. The article did slightly touched on the real issue on why people enjoy writing code, a “craftsmanship”, yes, coding is NOT engineering, it is writing, and the people who enjoy doing it are actually writers not engineers, and I always keep mentioning that. With AI however, those writers have to be doing the engineering work: the goals, architecture design, managing blueprints, process design and refining, among many other things, and that job is not easy hence why engineers are “supposedly” paid well, AI now took the writing role, and you have to do the engineering one.
> Here is something that gets lost in all the excitement about AI productivity: most software engineers became engineers because they love writing code.
> Not managing code. Not reviewing code. Not supervising systems that produce code. Writing it. The act of thinking through a problem, designing a solution, and expressing it precisely in a language that makes a machine do exactly what you intended. That is what drew most of us to this profession. It is a creative act, a form of craftsmanship, and for many engineers, the most satisfying part of their day.
> Now they are being told to stop.
Yeah, so what I've been realizing from witnessing the Rise of the Agents™ is that there are tons of developers that actually don't like writing code and were in it for the money all along. Nothing wrong with money --- I love the green stuff myself --- but it definitely sucks to have their ambivalence (at best) or disdain (at worst) for the craft imposed on the rest of us.
Feel free to replace `writing code` for most work functions that are enjoyable for some that are being steamrolled by Big AI atm (writing, graphic design, marketing copy, etc.).
Or maybe people who liked writing code also like writing prompts to write code?
And yes, there are also traditionalists who think the old ways are the best ways.
It's not about "best" or "traditional," though your bias is clearly set.
"Write me a feature that does _x_" isn't satisfying for me, and, like the author said in the post, it sucks that people that think otherwise are telling me that my way is the "old way", as you put it.
(It's doubly-ironic for me, as I actually like writing documentation!)
> "Write me a feature that does _x_" isn't satisfying for me, and, like the author said in the post, it sucks that people that think otherwise are telling me that my way is the "old way", as you put it.
Ya, no. It’s not that easy. Programming with prompts is much more intellectually challenging, maybe it will be like that in 5-10 years? Right now we are in a productivity increase that is more similar to interactive terminals over using punch cards.
I wonder if the traditionalists simply don’t understand the tech, they seem to have made some whacky unfounded assumptions about it.