alt Hacker NewsAI Has No Wisdom and Neither Will You
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There's a continuum between "vibe coded by someone with no technical knowledge or inclination" and "hand written domain driven design development". You can absolutely use coding agents AND have maintainable code. But yes, the coding agents will not magically make everything maintainable if you don't tell them to.
"Code maintainability and good architecture don’t have good measurements that we can apply"
Who has no wisdom? There are dozens of ways to measure code maintainability. Cyclomatic complexity is just one.
Nothing stops you from wiring up something like SonarQube metrics to your agentic coding workflow.
In the beginning of August 2026, I started a hobby project: building a MongoDB-like database. I have 20 years of industry experience and a master's degree in computer science, so I followed a disciplined, spec-driven development model using Claude, Kiro, Qwen Coder, and Cursor.
The first version was built in about two weeks of part time work. Then I started exploring. I learned relational algebra, researched almost every kind of database, reworked the internals, built a small relational algebra layer, a query planner, and an executor, covering everything from the backend storage to the query language. I learned more in those two months than in the previous 20 years.
Did I care what code the agents wrote? No. I read zero lines of generated code. What I cared about was correctness, verified through tests, and the high-level product features. For the first time in my career, I acted as a senior product manager, steering the project along the right roadmap. Without AI, I wouldn't have been able to do that.
When you have superpowers in your hands, you don't need to worry about the laundry. For the first time in my career, I can produce code in C, C++, Java, .NET, or any other language. Sometimes it takes me longer than a senior developer in that language, but does that really matter? Absolutely not. Writing documentation and code by hand in 2026 is like driving a horse and buggy. It doesn't matter how skilled you are with the reins; you'll never compete with a car. My hobby db project isnt opened source yet.
Maybe I've just given up, or maybe I'm a realist? But I fully believe AI will just...catch up with everything?
There's so much money in it right now. There's such a momentum. There are zero incentives to slow down for those that are in charge.
I've accepted that in 5-10 years, the vast majority of human devs. and engineers will not touch a single line of code. It'll be small increments, with a couple of big ones here and there.
And there will not be any triumph for those that hold steadfast to the principle of human coding. They'll be tiny boutique shops that do custom stuff, in the same way cobblers are to the mega shoe factories.
Man.. just code, let people build, design, adjust. Who cares? Who are these people writing these posts? Why should we give anything they have to say warrant? These posts are getting old, very quick.
> I’m going to make a prediction of my own… In the future we will see more and more companies proudly boasting their “NO-AI” policy as a competitive advantage. And they will be right
I'm going to make my own prediction: this isn't going to happen
Most software is not good, using these rules. And that is written by human developers. The problem is that most software developers have less than 5 years experience. The community doubles every 5 years. Experience is scarce. So AI, learning from all the stuff online, does not learn great code, it learns from available code. You can make it produce better code, if you do the hard work of defining better in terms the AI understands. I've been trying to find a way forward with AI generated code, using my own definition of 'good architecture', and results with chatgpt 6 are promising. Not perfect, but good enough. I used a book I was writing as input, you can read an unfinished version of it here: https://programming-for-wizards.dev.muze.nl/ (Still working out the kinks of the underlying software) The other approach is to explicitly keep the whole decision tree as a causal chain in a repository: https://github.com/muze-labs/spiral-developer Still testing that one out.
I've read a lot of posts similar to this recently and keep asking myself if the past 20 years of my professional experience was that atypical.
I’m sure there are companies who are writing “perfectly maintainable and highly scalable code”. However, for half of my career I’ve been brought into startups to clean up the mess created by engineering teams.
While AI may create an unmaintainable mess (I’m not totally convinced), from my perspective many (not all) engineering teams have been doing that all along. #v2 #refactor
I believe many of us here knows that the idea of a simple prompt to make something more than a sketch or a prototype don’t really work.
Unless you steer and understand what an LLM will produce, you will end up with something that possible ”works” that has no future plans baked in. Suno generated music has a very unpleasant feeling of sounding like competent music with nothing to say.
I’d say that vibecoded software is similar. My speculation is that current breed of LLMs do not have an I, and I really don’t exactly knows what goes on in those vast arrays of numbers. There’s something there perhaps, but no person.
Still even in the short term someone wants to run a company that expects responsibility of its organisation, how are you going to exact that responsibility if no one actually understands how the thing the organisation makes works.
Maybe a simple crud system can be made fast and loose. But a bank settlement? A pacemaker? Deletion of sensitive data?
I know some companies are betting on that the agent can fix what the agent breaks. It may be true, but up until now everytime I try to relax on strict steering of an agent it tends to go badly rather fast.
Again I don’t know, but I think as long as we don’t invent synthetic persons with their own ideas on what they want to do, which btw opens a massive can of worms, the current situation will persist. However clever the current breeds of systems are.
I do want to state that a find the current trajectory fascinating. I use LLMs daily, it expands the number solutions I can explore. But in order to make something I feel is mine. There’s a choice and the buck stops with me.
> Fact is, vibe-coded projects devolve over time into an unmaintainable mess. The reason is simple, yet hard to fix: code maintainability and good architecture don’t have good measurements that we can apply, because it takes months, years even, to notice the effects of bad architecture or of unmaintainable code.
Oh boy do I have some news for you about legacy codebases.
I’m not even sure that his axiom is true. A project started with a 2024/5 model can subsequently be worked on by more capable models (who don’t, unlike some humans, have a deep aversion to paying down technical debt). I’ve seldom seen a human written legacy application spontaneously acquire a better engineering team every six months.
There are already consequences.
We (collectively) were unprepared for a machine that presents itself in human forms. We were the frogs that boiled ourselves. We built a world of images and words on a screen. And then we built a machine that can (increasingly) mirror that world; it does so in a way which most of us are incapable of disambiguating.
It feels like there is indeed a ghost in the machine.
And there is, but that ghost is us. And that ghost is fading surprisingly quickly.
As much as I'd love to believe it, it is now a conservative take. Sure, having a solid architecture in mind still matters right now, but manually writing code is completely unnecessary and, before long, even designing the architecture is going to be completely automated.
>People are actually terrible at making predictions. I believe the future will surprise all of us. But, I’m going to make a prediction of my own…
>In the future we will see more and more companies proudly boasting their “NO-AI” policy as a competitive advantage. And they will be right.
I will very happily take the other side of this bet. Maybe if LLMs stayed as September 2026 LLMs for the next 20 years, I'd grant it's possible. But that's not what's going to happen.
Tbf, at this point, this has been said ad-nauseam.
At least I did not find a new thought in that (granted, relatable) rant.
"This is bad and you are bad" requires people to not defend their reality through rationalization, but the point we're at with AI right now is driven by exactly that. So this is at best highly ineffective at reaching the people it claims to want to reach.
That said, the underlying emotion of "you all suck and I hope you lose your jobs you frauds" is relatable and worth screaming from the rooftops of Linkedin dot com for the catharsis alone.
If you have ever looked at a kite and said to yourself, "that is as good as a hang-glider. maybe better.", you might be subject to the perils of one-shot AISDLC
I think (hope?) our industry will reach some equilibrium between fully agentic development and reviewing/hand tuning the code.
Personally for my job I struggle to justify writing code by hand. Agents are much better at typing the code, reading the code, finding patterns and discovering bugs. They don't have my knowledge and my background so the current models still miss things or overcomplicate the code. I don't know if that's going to be true of the next generation of models (or the one after that).
With current models, I cannot fully give in to the vibes. I _have_ to look at code (maybe not 100% of it but at least a majority of the lines). I _have_ to grill the agent to make sure it writes the best code that I deem possible for the problem at hand. This isn't the fastest way to do agentic development, but AI agents have already made us significantly faster than we were in the pre-AI era. I don't want to squeeze every ounce of 'development speed' if that comes at a cost to code quality, maintainability or debuggability.
For my side projects, things are different: either I only care about the outcome (and I give in to the 'vibes') or I care about the journey just as much as the destination in which case the agents are extremely advanced search engines, code reviewers and mentors but they don't get to write any of the code.
Yeah, I guess I personally fall on the whole spectrum: for side projects I'm at the extremes but for my professional work I fall somewhere in the middle.
To me, the problem is not about what AI is good or bad at, but about institutional knowledge. If AI is doing the coding, writing, designing, or whatever else, you slowly lose the ability to a) learn from others in the org because nobody knows what you need to learn anymore and b) actually improve stuff because there's no more "what good looks like"
> Fact is, vibe-coded projects devolve over time into an unmaintainable mess.
We've had strong coding agents for less than a year. Anyone making such a definitive statement about how vibe-coded projects progress over time is basing it on guesswork, not evidence.
As far as maintainability is concerned, have we all forgotten the 12 Factor App in the age of AI?
https://12factor.net/ https://en.wikipedia.org/wiki/Twelve-Factor_App_methodology
Kevin Hoffman expanded on that with the 15-factor app: https://developer.ibm.com/articles/15-factor-applications/
Mind you I may be dating myself as I was first introduced to this paradigm in 2015 working as a Java SpringBoot engineer on an enterprise project that I then migrated (57 microservices) all to Scala, after onboarding two weeks to Scala fresh from no prior Java experience.
I feel like there is so much "wisdom" encoded in books and writings from some of the most prolific engineers and architects over the last several decades.
Look at Matt Pocock's skills with simple primitives like grilling the human, researching through wayfinder maps (a Godsend to my workflow prior to Cursor Projects and orchestrator patterns), and having a solid Domain Driven Design through defining a shared glossary and breaking up work around proper seams.
Solid points. I will add a few points.
1) A lot of the time i spent deciding on interfaces (methods, classes, etc.) for humans. E.g. should this be two methods or one, should this method be in this class or moved to utility. Those problems went away. 2) What about performant code? This can be prompted away and when the measurements in your performance tests do not go down, then you can step in. 3) Sad to say but the AI has always been better than me at code-reviews. Maybe this is just me and if so I own that, but to the articles point, it might be harder to fix now. 4) "vibe-coded projects devolve over time into an unmaintainable mess". Preventing and managing this mess is the new skill sets we need to develop as software engineers. 5) Another skill-set we will need to master is how to maintain and grow our coding skills. Some ideas are: a) every once in a while implement a feature yourself. b) no AI Tuesdays! c) Have the AI quiz you on the code base. d) Have the AI develop HTML docs about how the code works.
I do think AI might get better and better at coding, as long as there are still humans creating software for AI to learn from—which may actually be a bad thing.
What worries me more is my own tendency to rely on AI more and more. It is becoming increasingly difficult to choose the harder path of coding by myself instead of taking the easier road, even though I know that road may gradually make me lose some of my skills.
Fortunately, I am closer to the end of my career than the beginning, but I worry a great deal about the new generation of developers.
Every large code base goes to shit unless you have a very strict bdfl at the top.
AI is nothing special or new in this regard. It just gives a single guy the velocity to ruin a codebase at the rate of a full enterprise team at double the speed.
A shitty code base still makes money, and that's all that will ever count for the majority of the employed developers, those who don't blog.
Effort is all. It was before AI, it is the same with AI.
Putting in massive effort with AI leads to quality exactly like before hand coding.
Vast majority of work was junk before AI because vast majority of people put the minimal possible effort.
The difference now is that AI can make low effort work look high effort at a surface level.
> because we’ve always done automation at scale, everything we do is automation
I'm weirdly attached to this line. Physical automation a.k.a. factories are not portable. The manufacturing techniques are, but to "spin up" a new factory requires expertise, effort and capital. In contrast to software, replication can be done quite effortlessly, with containers and such tools, it's easily a one-man job.
Writing, coding and art are not meant to be repetitive, scalable tasks. I think that's what's driving the core of the backlash. If these forms of human expressions can be automated, then why would we need to apply our mental capacity at all?
You can still read the code, ask the AI questions about it, and ask it to fix things. Coding agents are a power tool for cleaning up codebases, but you have to ask for the right cleanups, which means figuring out what it is based on your understanding. (You can also ask the AI to suggest ideas.)
This takes time away from implementing new features, but that’s true of all code health maintenance.
I'm all in favor for well crafted software and thoughtful designed stuff. The "No-AI" branding will probably become somewhat popular as you expect it, but how can we trust this? We've already seen so many examples of people (wether normal people or very important people) using AI and disguising it as their own thought. How can we ever trust a company just slapping a "No-AI used" logo on their landing page?
Very interesting post. Feels like my comment from two days ago [1], elaborated. I must thank the author for verbalizing all the concerns.
So now we have a fork: red-pillers who want to regenerate software all the time (esp those with unlimited token budgets) and blue-pillers who want to maintain more code mass per developer-head. Plus, we have software artisans.
That will be an interesting horse race.
the thing is.. the author says all vibe coded projects devolve into an unmaintainable mess, well so does all software, I have never seen any large scale software project that was absolutely beautiful over time, at least not in gaming and enterprise
it just doesn't happend, too much time pressure, and low-hanging fruits must be had, money must be made, and so it goes... that begs the question, if most enterprise software and/or game projects are an unmaintainable mess, yet they still make money and "work" somehow, whats the value in "beautiful" projects/architectures/code over time, if it provably works either way?
Wisdom is very rare. I constantly have to tell people "we did it like that in 2012 because it seemed like a good idea and there were no better options - but this is 2026 and there are now better options and we shouldn't do things the old way instead.
Too often people get an idea and don't stop to ask if there is an even better idea. (I'm guilty of this myself). Often it takes a while to figure out what the good ideas are, but people want an answer now.
> Fact is, vibe-coded projects devolve over time into an unmaintainable mess.
Is it a fact though? Maybe my projects aren't ambitious enough, but about 6 months ago I stopped hitting the point where AI can't maintain what it has written. It's probably unmaintainable by humans, but that might be an increasingly irrelevant quality.
> and let’s be honest, most code in the wild is pretty bad
Most code in general is bad by some metric, and likely many metrics. Let’s not pretend that closed-source, in-house code is better.
> There is no fitness function you can define for maintainable code, at least not one that we can discern, otherwise it would’ve been baked into our linters.
This is emotionally appealing because it tells me that my judgement is irreplaceable. But it’s also essentially an appeal to magic. A metric than cannot be defined is either not real or is a matter of taste. And maintainability cannot be a question of taste because it makes concrete claims about the software, not just human perception of it.
All this is to say that we have yet to establish that “vibe-coded projects devolve over time into an unmaintainable mess” is a fact, or that it’s more true of vibe coded projects than hand coded projects. We are still learning how to build the right guard rails on vibe coded software because previously we relied a lot on humans reasoning over the code and saying “this looks about right” which frankly is not a strong engineering practice to start with.
Forgetting about AI for the moment I do like this paragraph very much:
> The proficient developers, the experts, rely on their intuition built with sweat and tears, working long hours trying to debug and fix production issues, swearing to never again be so foolish as to repeat past mistakes. It’s the kind of intuition that can’t really be made into a list of rigid rules, because everything is context-dependent. Experts are incompatible with the same rules and recipes that make beginners more productive. Experts don’t follow the rules, they make the rules.
.... because I have found the same thing - that there's nobody more zealous about some paradigm than those who are recently converted to it and who haven't come to find that everything has its trade-offs. Design is always about evaluating the trade-offs and seeing which ones most suit the given situation.
> AI isn't good at software maintainability
We're a few years into a new technology that is still improving. This is a point-in-time critique.
This is one of those predictions that sound very wise and do well on Hacker News, but will turn out to be completely wrong. For the simple reason that we’re basing all our assumptions on decades of learned behavior predicated on coding being expensive. Writing code has historically been expensive and, by extension, so is maintaining it.
But when you can literally generate a whole codebase in a few hours, a lot of these assumptions don’t hold up. That doesn’t mean you don’t need sound logic and maintainability. But a lot of the small things you think would go wrong because of a bloated codebase stop being meaningful objections when you can keep generating, testing, and reworking it until those issues are resolved.
A lot of what we consider good engineering judgment is shaped by the constraints we’ve always worked under. I don’t think people appreciate how much of that changes when those constraints go away. The "slop" problem is ultimately a verification and testability problem.
One thing I think it’s still easy for a human to beat AI at is a good PR summary.
They are often too verbose, or miss capturing an important concept or purpose, or add bullets for parts of the changes that no one cares about.
AI lacks wisdom because it processes patterns, not reality. It excels at statistical synthesis but cannot understand the meaning, stakes, or consequences of its outputs.The real danger is cognitive atrophy. If we outsource our critical thinking to automated averages, we stop doing the messy, high-latency work of building our own internal world models—effectively redefining "wisdom" as whatever the model outputs.Would you like to turn this short summary into a social media post, a quick debate rebuttal, or a thesis statement?
We are teaching machines to do the work.
Capital always prefers machines.
Hard disagree. Usually I am wiser than the AIs but there have been times where the AI has pushed back and made me see the light on some poor design I was about to pursue
you don't need to read or write code directly to prevent it from becoming an unmaintainable mess. they way you prompt, the loops and skills you build for your harness and the way you set structure around it can get you most of the benefits of reading & writing code manually
I do not think, the intention in using AI, will ever be gaining wisdom. It is rather moving computations to a larger computing system.
> I'm no luddite, I’ve integrated AI in my everyday work, while actually teaching my colleagues what I’ve learned
It's appalling that we still hold on to such things that are no longer necessary. Code maintainability is not a problem when you don't have to open a file and inspect how something works anymore. You use english to add to it. You sit on chairs everyday where you don't give a shit how they were created. They fulfill their purpose. hopefully the same can be said for your software.
I'm not quite a doomer. I use AI every single day. I also take a fairly negative view of AI generated code, but I think it's unavoidable while remaining employed these days. A fact I find pretty depressing.
I absolutely agree with the author that humans need to be in the loop reviewing and understand the code they're merging, and generally take a "Hey, build X like Y utilizing Z" approach when using AI to build instead of the "Hey, solve this problem" approach. Our PE overlords actually mandate the latter, but I'm not doing it.
However, a point the author misses is that with AI, major refactors become relatively quick. Hours instead of months/years.
Yes, AI can and probably will land you with major foundational and architectural problems, but your architecture isn't set in stone anymore. Your entire codebase bends like a leaf in the wind.
"New technology: cool but the supply chain is vulnerable". A tale as old as time.
Here's the one simple trick that fixes everything:
- Use the code that your agents write in anger.
There you go. Do I know when my agents fuck up? Yes, I absolutely do -- because I'm a user of the code I have my agents write, and I ask things like "why is it taking 50 ms to start this program ..." and then I go in and find stupidity, and excise it. I do this over and over again.
Is it faster than writing it out by hand? Maybe! It's definitely a different perspective.
Start behaving like a baby "why, why, why" and then do a bit of reading, and you'll be fine.
A lot of these blog posts seem like they're aimed at software written by B2B companies who don't even use their own software ...
AI labs should produce AI that can take support calls. But let it stay away from the fun jobs like coding.
A book that has shaped my thinking on AI, and much else besides, has been Horkheimer's Eclipse of Reason.
This book gives a conceptual groundwork for what I believe to be both the promise, and crisis of AI. AI excels at, and either will soon or already has surpassed humans at a kind of reasoning that Horkheimer calls "instrumental reason." This is reason as a tool for achieving ends. AI will, I think, surpass humans at writing maintainable code, as this falls within that domain. The writer is thoughtful, but the obstacles listed in the article are technical, and they will likely fall.
A second kind of reasoning, which he calls "objective reason," is reasoning about which ends are worth pursuing. Our contemporary pragmatic, positivist culture in the West has a hard time reasoning about ends. We tend to just see different perspectives, competing power networks, etc. We are skeptical of capital-J Justice, for example. We tend to see it as historically and culturally contingent, or else as a mask for powerful interests. Horkheimer believed this learned relativism was responsible for new forms of domination and control that emerged in the 20th century in both totalitarian and putatively free societies.
I think the author's thesis would be correct if it had focused on AI's inability to discern ends. But that then raises the question: can we discern ends? Horkheimer never settled on a way to rehabilitate objective reason. He focused on critique, and his critics suggest he ended up with a tangle of negations that couldn't hold up any objective ends. I think rehabilitating objective reason is the task that faces us whether we like it or not, given our moment in history with the advent of AI. It's something practitioners need to think about. Either we recover the philosophical tools of objective reason in light of the critique which caused them to be discarded in the first place, or ends will be imposed arbitrarily by any and everyone with the means to do so.
In traditional software development process two artifacts are created. The obvious one is software. The second, is knowledge of the problem domain in the development team heads.
We could take the Hofstadter idea and say that there is an isomorphism between these two artifacts. Software is a manifestation of the team's knowledge the way an organism is a manifestation of a genome.
With AI, the second artifact is no longer necessarily produced. We don't need to have a team that, as the project progresses, slowly becomes a group of domain experts. Experts that can drive the project direction. Experts that, with time, can see the flaws in their first project and start new breakthrough projects to fix these flaws.
HN is on a comical doomer binge the last couple of years.
The problem imo is the slow deterioration of institutional knowledge that offloading the mental task of wisdom gathering to AI is causing.
One interesting comparison is to the history of manufacturing. West/America decided one day that manufacturing would be cheaper to outsource and better (short term) profit was to be made by outsourcing it all to China. The institutional expertise started to deteriorate, to the point that America simply didn't even have the capacity, or expertise anymore to produce stuff (such as grill brush [1])
I feel like you could take all the handwavy comment that are made today to dismiss this caution, and find equal dismissal back then when companies were actively outsourcing the manufacturing.
"I'm coding 10x faster" "look at the output velocity per employee"
"we are producing much more (in China)" "look at profit / number of (manufacturing) employers"
Seems ok if you're American / Chinese but I'm struggling to understand how the rest can be OK with allowing institutional knowledge to deteriorate while having an active dependency to the former two. We already see this with the tech dependency towards USA and manufacturing competition from China.
[1] https://youtu.be/3ZTGwcHQfLY