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Software engineering is about managing complexity
- a2ff6eeb0 21d agoAI can also generate the architecture for you based on the requirements, and ask the necessary clarifying questions. It's not as good at system design as writing code, yet. But it feels like it's better than most of my coworkers. I think in a few months, system architecture will have its Claude Code moment, and humans will be outclassed.
- VohuMana 21d agoI don’t know, I could be wrong but I think one of the key aspects of system design which I don’t know if there is a lot of training data for us the “why” behind decisions. Separating out good design from bad isn’t always black and white and like the article mentions it’s about managing complexity and trade offs. It’s hard to capture in code/training data “we designed everything in a certain way but compromised in this one area because we were under time constraints and assumed we could fix it later” A fun little exercise you can do is design a system and write some code and then ask LLM to explain why you wrote it that way. Results are varied and interesting but in my experience rarely capture the actual why behind decisions.
- a2ff6eeb0 21d agoInterestingly, while it may not match the reasoning that I have, I find that it usually comes up with things that I should have thought of.
- cobbal 21d agoLost me at the first assumption. People can argue about how useful AI is, but it's obviously not essential because we somehow managed to write code without it a few years ago. I would even say the code was better back then. The two tasks of writing code and engineering software cannot be separated without damaging the integrity of the mental model of the engineer. Having architects who didn't interact with the code always produced map/territory mismatches.
- p0w3n3d 21d agoSadly many managers decided to do so and the damage is done I love to say that Some managers didn't pass the Turing test
- bluegatty 21d agoHave you looked at the machine instructions your compiler produces? No? Why? Because software languages are a pretty good abstraction. To the extent that good abstractions are in place, you can avoid looking at code specifically. Those don't perfectly well exist, so it takes a lot of self discipline and the right tools/methods, but invariably, AI will produce better systems. That said, its very easy to produce slop, so well see much more of it. But mostly, it will be AI from here on in, as a matter of productivity. There are some arguments on the margins but those will fade over the next few years. 'At minimum' - the 'power tools' are here to stay.
- marginalia_nu 21d agoAnyone who is involved in any sort of performance critical work looks at compiler output on a regular, if not daily basis.
- znnajdla 21d agoSure but you only look at it when optimizing some performance critical code, usually the hot path. That is usually a tiny fraction of the codebase. I also use AI to generate large amounts of code but I only inspect the actual code when critical, delicate or architecturally important stuff is involved.
- bluegatty 21d agoYes, but even HFT traders use Java and don't look at the compiler output. Looking at the compiler output is a totally valid concept, but it's definitely a niche case.
- marginalia_nu 21d ago
- skiing_crawling 21d agoIt is revisionist to say that software engineering was never about writing code. It was, in fact, a huge component, and it also wasn't easy. Sure most code is glue but even the glue was tedious and the actual hard and novel parts still aren't really done that well by AI (yet). It's less about writing code now but we're lying if we try to pretend it was a distraction and not a big part of the real work. And every claim about what the job actually is or was all along has an implied (for now) at the end of it.
- tchalla 21d agoSoftware engineering was never about writing "just" code. Simply writing code was not enough in most business environments.
- skiing_crawling 21d agoI didn't use the word "just"
- tchalla 21d agoI did. I am adding on to your argument. In general, a reply to you never needs to be a counter. It can be a build up too.
- ebiederm 21d agoSpitting out code as fast as our fingers could type has never been the job. There has always been a lot of think time. Which is why the productivity of people of people has never been correlated with typing speed. In other jobs productivity is correlated with typing speed and in those jobs a typing speed like 60wpm is part of the job requirements.
- coredev_ 21d agoThanks, might be the best blog I've read in 2026. AI can of course do architecture as well but oh boy will you have a bad time when your application breaks and neither you or AI can fix it.
- raevn 21d agoOne question missing from the list, and it’s the first one I tend to ask… do we really need this? I’m not sure I’ve ever seen an agent pushing back on a request.
- zer00eyz 21d agoBing Bing Bing... How often do engineers get a say in product direction? Every one keeps saying that AI isnt moving the needle on the bottom line. Well duh, code doesn't move the bottom line, features do, products do. If you're building all the wrong things faster, all your doing is performing a speed run to a legacy code base.
- godwinson__4-8 21d agoYou can tweak them to do so. I personally tweak mine to act like a disappointed stack exchange veteran. I personally recommend, but I understand many people do not want to be pushed back by something they see as little more than a servant. This setup does work to also have agents argue with each other. That can be very interesting, though you have to set them up to be very skeptical. Otherwise they will tend to read another agents assertion as authoritative off the bat. I am convinced much of the harness/prompt engineering we are doing now will also be automated away. Within 5 years the best practices for the most popular use cases will have been found, automated and fully baked in.
- intrasight 21d agoAll modern engineering is about managing complexity
- bluegatty 21d agoFair, but Software is very different from mech or chem eng though - enough so that it's worthy characterizing.
- ratelimitsteve 21d agoThis. Software hypothetically can permit unlimited complexity. There are only so many ways you can try to build a bridge such that it won't collapse. There are an infinite number of ways that a set of instructions can reach a desired state given enough time, processing and memory resources. This, combined with our relative naivete in how to design and build software that does not approach infinite complexity compared to other engineering disciplines means that the primary thing about building software is managing complexity.
- intrasight 21d agoLook at the evolution of any complicated human-made thing - for example jet engines or even better computer hardware. Managing complexity in physical things is different and harder I think - because you're putting a stake in the ground deeper and earlier, as opposed to a software which, as the phrase goes, is just a collection of bits being fed into a machine.
- bluegatty 21d agoTotally agree jet engines are hard for that reason, but that's an argument as to why 'software is more complex' ... because it can be. Anyone can knock out unbelievably complicated nonsense. Much of it is. I think the complexity arising from 'laminar flows' etc. is just a different thing.
- ratelimitsteve 21d ago
- thi2 21d agoI started to look around the site and opened this: https://hack8s.com/409/ziglings-all-exercises-solve-v0-16-0 https://hack8s.com/409/ziglings-all-exercises-solve-v0-16-0 The site just goes into a reload loop on iOS?
- justorius 21d agoI'm the author: what's wrong with this article? No issues detected...
- zug_zug 21d agoThe complexity stuff is all absolutely true. However I think it's aggrandizing what human engineers actually do with remarks like "Engineers own tradeoffs." My experience is that certainly less than half of the employed software engineers don't actually give a real analysis to questions like: "Given these constraints, this team, this business, this infrastructure, this budget, these risks, and the expected evolution of the product, what is the most appropriate way to implement X, today?" Thus I think AI is more able to replace the average engineer more than this article admits, however the inadequacy of "average engineering" will be much more apparent now: codebases can become large/complex enough to be unwieldy in months now when it used to take 5 years [a timescale where accountability is effectively impossible].
- KronisLV 21d ago> this team, this business These get overlooked so often. The way you build software if you’re at the helm vs the way you need to build it when dealing with a more/less capable team and business, especially if someone else will be doing the deployment and will need lots of consultations, is way different.
- hn_go_brrrrr 21d agoThis is my favorite part of software engineering. It's not just a set of rules you can apply to get the right answer. You need to use your judgement to make a context-appropriate decision.
- echelon 21d agoIt's been ten months since good models started landing and threatening our current job descriptions. Do you think this is where it stops? This is where it begins. Machines will be good at managing complexity too. You can't draw a line and say improvement stops here, because everything we've seen so far flies in the face of that. I shudder to think what these models will be capable of in 24 months.
- preommr 21d ago
- bananaflag 21d agoI have no idea why people believe AI will not be good at all the other things. It's a general reasoning machine, it surely can reason on many things beside the actual code. I've been hearing this "writing code is not what being an enginner is" mantra for years like some sort of gotcha. (It was prevalent even before AI, and I think people underestimated a lot how many people were simply incapable of writing code even given all the specs and design choices.)
- znnajdla 21d agoBecause it’s not about being “good at things”. You missed the whole point of the article. It’s not about AI vs. human capabilities, even a human could not build software if they are not in the right context. Here’s an example: back in the 2000s, everyone was afraid programming was going to get outsourced to India or other countries. It didn’t happen, Sillicon Valley continues to spend billions to import engineers to work in person even though it’s 10x cheaper to hire remote outsourcers in India who are just as skilled programmers. Why do they spend 10x to move physical bodies to the office? Because it’s impossible to write good software without being in the physical context of the problem domain and team. Similarly you cannot outsource to AI, because it cannot have complete context. No matter how good AI is, the problem is not mechanically solvable.
- ds_opseeker 21d agoTechnically it is not a reasoning machine. If it was a reasoning machine then we would not see results like this: > To systematically investigate the role of end-user semantics of derivational traces, we set up a controlled study where we train transformer models from scratch on formally verifiable reasoning traces and the solutions they lead to. We notice that, despite gains over the solution-only baseline, models trained on entirely correct traces can still produce invalid reasoning traces even when arriving at correct solutions. More interestingly, our experiments also show that models trained on corrupted traces, whose intermediate reasoning steps bear no relation to the problem they accompany, perform similarly to those trained on correct ones, and even generalize better on out-of-distribution tasks. https://arxiv.org/abs/2505.13775 https://arxiv.org/abs/2505.13775 Beyond Semantics: The Unreasonable Effectiveness of Reasonless Intermediate Tokens
- imhoguy 21d agoIs it only me who gets eye pop by just how the font is rendered on that website?
- threethirtytwo 21d agoThese software engineering analogies are getting tiresome. People are shouting “yeah the hard part was never writing code, it was managing complexity” as a sort of last hurrah before AI engulfs them. This is reality: not only can AI write code. It can manage complexity. Prompt: read the article in this thread then execute its principles on my codebase. Write a harness and programmatic procedures that will trigger you to respond with the articles philosophy to code changes. Be vigilant and monitor every aspect constantly. I would say for the above prompt, AI is about 60 to 70 percent as a good as a human now. A year ago it was 20 percent. The gap is closing.
- znnajdla 21d agoNo the AI cannot. Did you even read the article? The principles in the article are not rules that can be applied or handed to a prompt. They are questions, not answers. Questions that are impossible to answer and that have no right answer except by human judgement in a concrete context. Even a human could not “manage complexity” if it’s not in the right context. This is not about AI vs. human capabilities.
- softwaredoug 21d agoSometimes these tradeoffs involve half a dozen over a few lines of code. And that’s where I’m hesitant to let an agent work. It’ll do fine with creating correct code. And you can somewhat constrain it to think about one other thing. But it loses track, ignores constraints, cheats, and do you layer complexity on top to prevent this? Or just look at a dozen lines of code to fix it?
- mermadicsolutio 21d ago[flagged]
- zerolayers 21d agoAI writing code is a force multiplier and amplifies an orgs existing practices. In other words, if you lack structure and are a fan of chaos engineering, then that gets way worse. On the other hand, if you already have god workflows and an overall structure, it'll help you get things done more quickly.
- karim79 21d agoAlways reminds me of why OOP came about in the first place; it was a way to manage complexity and led to much better and grander software. Now nobody talks about OOP because abstractions are built into just about everything.
- huijzer 21d agoIt was sold as a way to manage complexity but then made everything complex in a different way. For some problems, OOP makes sense, but for many I think it doesn’t. Unless you have a Rust like trait system that looks a lot like OOP but isn’t. That works well. Essentially don’t put state inside your classes, or you will be spending lots of refactoring time on moving variables up and down in the class hierarchy or throwing computer out of the window because a variable on second thought shouldn’t have been added near class Y.
- vb-8448 21d agoI agree, but to manager the complexity you need far less swe.
- ryandvm 21d agoThe real wild shit I'm seeing and having trouble reconciling with continuing my career in this field is that there seems to be a majority contingent of C-suite out there that is absolutely obsessed with force-feeding their organizations AI. As an software engineer, I will readily admit that LLMs have greatly increased my output - especially on the menial work. But now we have leadership telling everyone to "use moar AI" on everything, everywhere. I literally have observed folks dropping into incident Slack chats saying things like, "hey all - i asked Claude about this issue and then i had it write a solution. here's the PR." This feels like the kind of thing that should be a fire-able offense, but instead they're getting shout-outs from the CEO. Hell, the next time I go on vacation, I think I could put Claude Code on YOLO mode for 2 weeks and I'd probably come back to find I'd been promoted. I do not know how this is going to end, but I have a feeling it's going to get way darker before it gets better.
- junior44660 21d agoThe article is AI generated and there's nothing new in it. Just a bait for boomers. Hacker News version of what Gen Z calls "coal posting".
- justorius 21d agoIt’s not AI-generated. I’m the author. I wrote it in about an hour this morning, then spent another twenty minutes or so fixing a few mistakes. There are probably still a few more in there :)
- junior44660 21d agoThere are AI tells all over the article unlike two previous articles I saw on the blog. It's a daily hacker news coal post regardless: "see how I can't be replaced because I do some more things". There's nothing new in it and some rehashing of this argument gets posted to HN ten times a week.
- deleted 21d ago[deleted]
- justorius 21d agoThe article is entirely handwritten. As a non-native speaker, I'm pretty proud of how it turned out, even if, in a long article like this, I have to use AI tools like Grammarly and WordReference here and there to sound more fluent and natural. There are probably still plenty of mistakes in it which, if anything, is further proof that a human wrote it. I'm sorry you didn't find it interesting, and I hope that future submissions will better meet your standards. Cheers, Alberto
- owebmaster 21d agoSure but the HN discussion of just the titles is already great.
- wouldbecouldbe 21d agoIts about creating a programmatic tool or game that solves something or creates joy to someone or a group. Thats it. The rest is just a way to make to above goal cheaper or easier to accomplish
- wyum 21d agoSoftware complexity grows superlinearly, if not exponentially as you add components. There are things an engineer can do to flatten the curve - that is OP's complexity management idea - but complexity growth can never be linear as long as you are adding to the software. I made a model/theorem for this that I posted on X: https://x.com/i/status/2027771813346820349 https://x.com/i/status/2027771813346820349 Code generation has exposed that verification is the central problem of software engineering. And I think it always has been. Defining what is "correct" can be hard enough, let alone building a system that lends itself to verification, let alone spending the time to verify. Releasing software and letting users find bugs is therefore a very efficient strategy, because it spreads the burden. But you have to ride the line between losing users and getting enough feedback to find and fix the bugs that matter. As we confront whether AI might take our jobs, I take some comfort in the idea that the world might be too complex for even the largest, best trained AI we can imagine. At a certain point, you need to simulate the whole world (or some substantial portion of it) and the cost/benefit of trying to do all that with compute may not be worth it versus using the real world (that is, humans) as your verifier.
- brvier 21d agoRemember who bought it ? And you expect a better product ? Nahh, really ?
- dzonga 21d agopretty good article. I wish everyone in our industry read 'No Silver Bullet, & Grug-brained Developer'. a lot of complexity - is about what can we do now, with what we have.
- nirui 21d ago> By algorithmic thinking, I mean defining a set of basic rules to follow and applying everyday... This is actually kinda a good suggesting for everyday life as well. Many problem people face are too complex to figure out without systematic analysis, and with this method of thinking, complexity can be simplified. Of course you have to do some swaps: > Understand data flows -> Understand whats, hows and whys > Choose appropriate data structures -> Choose appropriate tools > Reason about time and space complexity -> Reason about cost and effectiveness BTW: Is this article was about LLMs induced identity crisis? I noticed quite a few blogs written in a similar color trying to realign themselves in the new world of AI. Of course that's a reasonable thing to consider, but in addition to that, I think it's also kinda useful to remember why you started as a software engineer to begin with, what were you planning that drives you to select this path? Will AIs be a blocker of that plan, or a enhancer?