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.
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...
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 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?)
Thankfully we don't have to know how to write well to enjoy a well written book.
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).
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.
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.
I doubt it; share something you wrote prior to, say... 2024.
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.
"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.
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?
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.
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.
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.
I would read the hell out of Joyce’s Perl 5 documentation, but only after six or seven beers.
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.
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.
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. :)
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?
I hate it. I couldn't read much more after that.
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.
"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.
Looks like something AI would say. Regardless of how it really was written
Admittedly it was so long and basic, I stopped halfway.
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)
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?
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.
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.
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.
In what way? False positives or false negatives?
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.
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.
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.
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.
I don't think this is true. I'm pretty sure most of them do it because it pays good salary.
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.
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.
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?
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.
AI makes using them a breeze.
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'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.
Maybe this is not entirely true yet, but it most likely will be in the near future.
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.
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.
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.
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...
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.
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.
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!
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.
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.
> 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.
> 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.
Can you point to examples of these patterns with the same frequency in any written content dated any time prior to 2024?
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.
That can't be right?
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 never was
That's different than saying a lot of people *believed* writing code was the hardest/most important part.
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.
... 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.
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.
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.
> but there does remain a degree of instability and non-determinism which will require human intervention to remediate.
I agree.
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.
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.
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.
"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.
THE MARKET WILL FILL THAT VOID
IT DOES NOT MAKE IT TRUE
Also, check out the dude's linkedin: https://www.linkedin.com/in/ivanturkovic/
For me, this is a bit different. Writing code has always been the bottleneck. I get most of my joy out of solving edge cases and finding optimizations. My favorite projects are when I’m given an existing codebase with the task, “When mars and venus are opposite eachother, the code gets this weird bug that we can’t reproduce.”
When a project requires me to start from scratch, it takes me a lot longer than most other people. Once I’ve thought of the architecture, I get bored with writing the implementation.
AI has made this _a lot_ easier for me.
I think the engineers who thrive wi be the ones know when to use what tool. This has been the case before AI, AI is just another tool allowing more people to thrive.
I’ve been pulling projects out of the closet that have been sitting there for years. It’s because I can sit down and get started so seamlessly. Before, I might waste the first couple hours configuring my environment and tool setup, but with Claude Code I can just jump in and start building, start solving the real problem.
I just built something this week where I had the parts sitting in my closet for a couple years, but just got curious to see how Claude does with embedded C, so it got me started. (Turns out Claude scanned my drive and found an abandoned C project that was outside my usual DEV folder, and just built on that). The code was 5% of the project, but it got done because Claude Code gave me the momentum push.
For my personal projects, the last 3 weeks have been more productive than the last 3 years.
Well if you're ever in need for a complementary mind in side projects- huh, how does one connect over HackerNews?
I stopped trying to recruit cofounders because I don’t need them anymore
I can do everything I need to do by myself plus tools at a pace no set of humans can achieve
I'm not afraid of breaking stuff because it is only a small set of users. However for my own code for my professional job no way I would go that fast because I would impact millions of users.
It is insane that companies think they can replace teams wholesale while maintaining quality.
Tech-savvy people might understand this feeling, but those who are responsible for hiring will easily proceed with another candidate that goes fast.
When push comes to shove, then, programmers will opt to have food to eat over handling technical debt generation.
It’s amazing for him and it works on his iPad.
However when I tried it on my iPhone it was a broken mess. Completely unusable (not because of screen size differences).
I tried getting Claude to fix it but it couldn’t do it without changing too much of the look and feel, so I dug into the code and it was thousands of lines of absolute madness. I know from using this at work that there are things I could have done. Write tests to lock in things I like etc…
But so much of the speed up was about not caring about the specifics that once I started caring about making an actual product, I was not much faster maybe not any faster at all. The bottleneck in writing a game was never in banging out code.
Ask the AI to assess the code itself and to propose ways to gradually refactor it for better cleanliness. It can be good at that stuff, but you need to make it an explicit goal.
It’s not an impossible problem to solve. I could probably setup a test harness that uses the existing game as an oracle that checks to see if the same sequence of inputs produces the same outputs. But by the time one done all, got it to clean up the code, and then diagnosed and fixed the issue, I doubt I would have saved very much time at all if any.
I find it… Amusing? That’s not quite the word. That programmers—a group notoriously for making wrong estimates of how long something will take to build—continuously and confidently spew a version of this.
And it’s not even estimating how long we ourselves would take to build something, now we’re onto estimating what an undetermined team of completely made up strangers could do. It’s bonkers. It has no basis in reality.
Yes, it is. “It would take a team 6 months” is an estimate, and I don’t see how you can argue it’s not. Even if it just said it would take them longer, that would still be an estimate.
> Claude makes me 10x faster than on my own without AI
Also an estimate.
> where 10x absolutely is a completely made up number
And by your own admission, an estimate taken from the ass that you thus cannot be certain is true. Made up perception does not equal reality.
https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...
Yes you do, you already made the argument when you pointed out the “team” size and makeup was completely unspecified, therefore the number is not an estimate, it’s just a number.
When you call it an “estimate” you are adding additional unsupported specificity to something that was explicitly stated as being hand-wavy to make an obviously rhetorical point. Are you saying you can’t understand the point being made?
My 10x is based on my experience doing projects with Claude. I also said “some” tasks, not all tasks, and I didn’t specify which tasks, and I clarified that my number is made up, which is why my number is also not an estimate of anything. There are some tasks that Claude can do 10x faster than me, and there are some tasks that it can do 100x faster than me, and there are some tasks I can do faster than Claude... for now... More importantly for me personally, Claude makes starting projects and using tech I don’t already know easier; it’s lower effort, regardless of speed.
The paper is interesting and a valid data point, but I don’t think it proves your point. I’ll respond with a few thoughts.
First, the dev’s self estimate of AI productivity speedup was +20%, even though their measured productivity was -20%. This may relate to the effort and not the speed, and it’s important to note that this is a gray zone the paper didn’t explore, and something that can be true on both sides. I can be “faster” at developing and still take the same or longer wall clock time. Measuring the time doesn’t capture how the time was spent, nor the qualities of that time.
Second, this study was done a year ago. That’s an eternity in AI land, and everyone noticed Claude and other models getting substantially better at code writing last fall, plus workflows and tooling are improving even faster than that. There’s every reason to believe the outcome of the exact same study might be different this year than it was last year.
Third, this study is explicitly biased toward large projects, and large projects are, even today, more difficult to find the productivity boosts with. I find Claude absolutely amazing at starting new projects, and absolutely terrible at working in large code bases that don’t fit in context. When I say Claude makes me 10x faster at some projects, I’m referring to something like setting up a new CRUD app when I don’t know much about setting up a database and web server backend, or writing a graphics app in Vulkan when I’ve only used OpenGL. Doing stuff like that, having Claude help me with tech stacks I don’t know, absolutely is many multiples faster than doing it on my own, and the paper link you’ve shared doesn’t address that use of AI at all.
Note specifically the paper says they are not demonstrating or claiming that “AI systems do not currently speed up many or most software developers”, and they have not demonstrated or claimed that “AI systems in the near future will not speed up developers in our exact setting”. It might be a mistake on your part to try to use this as some kind of evidence that AI isn’t speeding devs up.
The assumption is that AI will continue to improve. If we get another one or two quality jumps over the next 1-3 years, which is not totally unreasonable, AI quality might be good enough.
This would augment actual engineer code reviews and help deal with volume.
I think this isn't being discussed enough in the SWE world. It wasn't too long ago that engineers on HN would describe a line of code as "not an asset but a liability". Now that code is "free" though, I'm seeing more excessively verbose PRs at work. I'm trying to call it out and rein it in a bit but until engineers on average believe there is inherent risk here, the behavior will continue.
Dunno man. Ideas alone aren't worth anything [0] and execution is everything [1], but good ideas and great execution will never go out of style regardless of how much competition is out there. I'm of the opinion that even if 10% of the population is now capable of creating a side project, there's still the same relatively-fixed amount of people capable of making a good side project, and even fewer who will see it through to a real product. Nothing has really changed in the aggregate. It's like architecture, there are always improvements in materials, tools and processes, and Claude and Codex can provide more laborers for almost free, but most people are still gonna be building uninspired McMansions instead of the Guggenheim.
What do you mean "nothing has changed"? Using your numbers, the SNR went off a cliff.
Use HN as an example - I used read the new stories all the time before they hit the frontpage, and upvote as needed.
But with 100s of slop submitted for every 1 actual good article, I can't do that anymore.
IOW, I have finite time. If 10% of the population is now able to vomit out side-projects, I am never going to find the one good one because it will be lost in a sea of rubbish.
And you're right that people still have limited, fixed bandwidth with regards to attention available to give to things.. but the same amount of things that break through doesn't change from what could break through and stick before (in the monoculture). The amount of niches/verticals where you have the opportunity to break through inside of is significantly higher than ever. That gives you a better chance for success, because your audience is more targeted, more receptive, hungrier for authenticity, hungrier for quality, and desperate for connection to something they like.
TL;DR if you have a good, valuable idea that people want (or don't yet know that they want), execute it well, deliver something that is undeniable, promote it effectively, and stick it out for the long haul, you'll find success. There's no magic formula beyond that, and it doesn't matter if there are 10 or 10 million amateurs clogging the toilet bowl next to you.
So really, they are comparatively cheap. I, for one, have hundreds of ideas, but always lacked the time to execute on 5% of them.
Why do you look at it that way? Why does anyone beside you have to care about what you do?
Just build something for yourself. You will always have things you'd like to build for yourself. You will be in competition with yourself only and your target audience will be yourself.
Market forces do not apply to side-projects, because that's what people do for fun.
Just because there are chess computers, doesn't mean that no one plays chess anymore at home.
This is just a correction of something that managed to remain in an invalid state for an impressively long time.
Yes.
> The entire open source ecosystem works on this idea otherwise there would be no point in sharing and we can move to closed software.
No.
The _actual_ open source system consisted of hackers scratching their own itch and sharing the artifacts, because (it was assumed that) sharing is free. So if the work is already done and solved their problem, why not also share it as gift.
This remains unchanged.
The driving force of FOSS is not "how can I fix someone else's problem". It never has been.
Well.. maybe on HN it was different, but that's not "the open source ecosystem". And, yes, maybe some corps have gaslit naive people into believing that they must donate their lives to said corps.
If you have the time tona scratch your own itch and gift the results, it implies you have a source of income that gives you the time/lifestyle to do such a thing. You might be a tenured academic, or live in a society with a strong safety net. Or you might be able to do your day job in 1/2 the allotted time.
The problem is that a those scenarios are eroding precipitously, leaving more to seek compensation for their work output, whether it is closed or open source.
So what is really changing?
Higher education is less affordable and accessible to more families, and the value proposition is eroding. CS academics survive by joint ventures with corporations, not by their University salaries.
Escalating cost of living and reduction in institutional support systems push more people toward allocating their scarce spare time toward fundamental needs rather than contributing to the software commons.
If you are good at something that you enjoy doing and that is valued by others, that’s the ideal scenario. And that’s what writing software looked like for many people for a long time.
That doesn’t mean you should do things just to please others. And it also doesn’t mean you can’t do something just because you enjoy doing it. But it means that these people now have a diminished ability to employ their unique skills to help others while doing something they love doing. That can sting, understandably.
a) Almost no one but you cares and
b) Now that this has become trivial, there's no much joy in it. The struggle we had before A.I was the real joy; prompting agents for a few days and getting what you want isn't that joyful.
If you have no reaction at all, you probably weren't paying attention.
Eventually though, people _should_ recover and return after having processed the changes. So maybe the professor was still recovering at the time?
The whole side project or even private project thing doesn't just hinge on being able to produce software. There's a lot more.
In software it's the same thing. People don't really want software they want data and data transformation. But traditionally the proxy for that has been selling the software (either as a desktop app or then later as sole kind of service).
You could argue that in either case the proxy is not what people want but yet because of the difficulty of selling the "actual" thing the proxy market has flourished.
We're now inventing a new tool that will completely disrupt that market and any software business that is predicated on the complexity required to create the software to transform the data is going to get severely disrupted. Software itself will be worthless.
The value of computers since its inception was that it's capable of transforming data very, very fast and autonomously. But someone has to input that data from the real world or capture it using some device, and someone has to write the rules.
What happened is that we created a whole world of information and the rules has become very complex. Now we have multiple layers stacked vertically and multiple domains spread horizontally. At one time, ASCII was enough, now we have to deal with Unicode.
Software becoming worthless will mean that everyone has learned the rules of the systems we created and capable of creating systems with good enough quality. I'm not seeing that happens anytime soon.
When you drive down that cost you drive down the potential value of the software products. Remember that what is a cost to one party is revenue to the other party. Without revenue there cannot be profit and without revenue software has no dollar value.
If anyone can create "photoshop" with minimal cost and there are thousands of said "photoshop" apps what will be the retail sell value of those apps. Close to zero.
This same lifecycle already happened with games. Driving down the cost of producing games resulted in a proliferation of games that are mostly worthless that you can't even give away.
I do agree with you on that point.
> If anyone can create "photoshop" with minimal cost and there are thousands of said "photoshop" apps what will be the retail sell value of those apps. Close to zero.
This is the point that I cannot agree with. Not anyone can create photoshop because of the amount of knowledge you need about the data and transformations that needs to be applied to get a specific result. And then make a coherent system around it. You can create isolated function just fine, just like a lot of people knows how to build a shed with planks and nails. But even when given all the materials and tools, only a few can build a skyscraper or a mansion.
That knowledge of how to create a coherent systems that does something well is the real cost of software. Producing code isn't it.
That being said what already exists was already enough to shutter the stock prices of many software companies precisely because the fear is that their clients will just re-create the software themselves instead of buying it from someone else.
I guess we'll see how this will pan out in the next few years.
A lot of the moats are gone, but quality (and security) is in a nose dive. AI built project might be the Ikea furniture. Good for the masses, but there's still a market (much smaller) for well crafted applications and services. It's hard to say what it'll look like in a couples years though. Maybe even the crafting is eventually gone. /shrug
No one else will want this specific piece of software. But I love it.
Sure, there will be 100x the competition, but there will be also 100x the software needs. Now, if you want to get crazy rich building software, that does get tougher, but that's a good thing, I think.
Even if they were I disagree that 10x more ideas being produced means 10x more products in competition. You could leverage AI to execute but still have terrible ideas, leadership, product stewardship etc.
I think some clever people with a real and valuable insight will finally be able to turn that insight into a product. I also think the other 9 products will be get rich quick attempts by people with nothing to offer.
I think there's more opportunity to do something novel.
AI can't do it, and the humans with the skills to do it are rapidly disappearing.
> Why? Because the bottleneck was never typing code.
Were you also shipping side projects every 2 months before AI?
If not, this comment just reads like cognitive dissonance. Your core claim is that AI has enabled you to ship 7 projects in 12 months, which presumably was not something you did pre-AI, right? So the AI is helping ship projects faster?
I agree that AI is not a panacea and a skilled developer is required. I also agree that it can become a trap to produce a lot of bad code if you’re not paying attention (something a lot of companies are going to discover in 2026 IMO)
But I don’t know how you can claim AI isn’t helping you ship faster right after telling us AI is helping you ship faster.
The part about the identity shift from builder to reviewer - that's the real thing nobody's talking about. I spent years getting good at turning thoughts into code. That's a craft. There's a rhythm to it, a kind of flow state you hit when the problem and the solution start locking together.
Now I spend most of my time evaluating code I didn't write, catching issues I didn't create, in systems I didn't design. The volume is higher. The satisfaction is lower.
The HBR study numbers track with what I'm seeing around me. 83% saying AI increased their workload. That's not a bug, that's the whole point. We made code production faster, so now we produce more code. Nobody stopped to ask if that was actually the bottleneck worth solving.
The thing that gets me is the pretense. Everyone talks about AI making engineers more productive. But if you look at what's actually happening, we're not producing better software. We're just producing more of it, faster, with the same number of people. That's not productivity - that's volume.
What's being lost is the time to think. To sit with a problem long enough that you actually understand it before you start implementing. The old friction of writing code manually gave you that thinking time by default. Now you have to fight for it.
I can guarantee you this... the story is not absolute. Depending on who you are and what you need to work on dev time could be slower, same or faster for you. BUT what we don't know is the proportion. Is it faster for 60% of people? 70%, 80%?
This is something we don't know for sure yet. But i suspect your instinct is completely wrong and that 90% of people are overall faster... much faster. I do agree that it produces more bugs and more maintenance hurdles but it is that much faster.
The thing is LLMs can bug squash too. AND they are often much faster at it then humans. My agentic set up just reads the incoming slack messages on the issue, makes a ticket, fixes the code and creates a PR in one shot.
I'm sure it also helps translate an app written for iOS into an app written for Android.
So it definitely improves performance.
The shift I've experienced is something akin to being able to finally focus on the aspects I've always enjoyed most: architecture and user experience. I review all the code, but through iteration my prompts have gotten better, and for the most part my automated codemonkey 'employee' produces good code. It's not reasonable to expect complex things to be one-shot; UX improvements always require follow-ups, and features need to be divided and conquered one at a time. Engineers who lack those higher level skills will struggle. You are leading a small team now, not just plugging away at implementing user stories.
Anyone could ship thousands of projects, depending on the definition of "ship" and if you don't care what value the project has beyond notionally increasing your tally.
Since managing dependencies is one of the major maintenance burdens in some of my projects (updating them, keeping their APIs in mind, complexity due to overgeneralization), this can help quite a lot.
See also https://www.karl.berlin/simplicity-by-llm.html for some of my thoughts regarding this.
Anything else? I'll struggle and grow as a developer, thanks. And before anyone says "but there are architecture decisions etc. so you still grow"... those existed anyways. If I have to practice, I'll practice micro AND macro skills.
I think from time to time, it's better to ask the AI whether the codebase could be cleaned and simplified. Much better if you use different AI than what you use to make the project.
The AI can help you in these tasks too, but you need to ask for the help in terms that it can help you with, and not expect it to be genuinely intelligent or to have a crystal ball. As a bonus, once you've gotten these things into the agentic context, the code itself becomes better too.
One-shotted vibe coding is an anti-pattern.
You can use it to discuss about what you should build, identify edge cases, ask you questions to force you to take decisions, etc.
As you mentioned, scope definition and constraints play a major role but ensuring that you don't just go for the first slop result but refine it pays off. It helps to have a very clear mental model of feature constraints that doesn't fall prey to scope creep.
Were you able to fairly split test?
Why will the 8th project still have those things as the bottleneck given your experience?
Also if you're not seeing any real gains in productivity, why are you using AI for your side projects and wasting tokens/money?
At work, I was told to use AI but it doesn't actually work for anything that I couldn't have handed off to a brand new undergraduate intern. So I use it for things that I don't care about then go spend twice as long rewriting what it output because it made the task longer by being wrong.
One area --and many may not like that fact-- where it can help greatly is that the cost of adding tests also drops to near zero and that doesn't work against us (because tests are typically way more localized and aren't the maintenance burden production code is). And a some us were lazy and didn't like to write too many tests. Or take generative testing / fuzzy testing: writing the proper generators or fuzzers wasn't always that trivial. Now it could become much easier.
So we may be able to use the AI slop to help us have more correct code. Same for debugging edge cases: models can totally help (I've had case as simple as a cryptic error message which I didn't recognize: passed it + the code to a LLM and it could tell me what the error was).
But yup it's a given that, as you put it, when the marginal cost of adding complexity drops to near zero, we're opening a whole new can of worms.
TFA is AI slop but fundamentally it may not be incorrect: the gigantic amount of generated sloppy code needs to be kept in check and that's where engineering is going to kick in.
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 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.).
And yes, there are also traditionalists who think the old ways are the best ways.
"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!)
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 see to have some whacky assumptions about it.