AI Has No Wisdom and Neither Will You(alexn.org) |
AI Has No Wisdom and Neither Will You(alexn.org) |
No. Most rules are there before and will stay after the expert enter the field within its career path, unless the field is bright new territory no one fooled before, which is rare.
Not only experts have to know the rules, otherwise they wouldn’t be expert, but good experts also ideally know why the rules were set, and at least have a fairly well aligned representation of what it would likely lead to to follow or not each rule in this or that situation, which rules are in conflicts and what the tradeoffs are when favoring one on the other.
Everything is context dependant, yes. And LLMs can help to leverage on far wider contexts that a single individual would be able to do on its own. It’s require interest to reach some goal in some social context, and not everyone will use LLMs with the same creativity.
This week-end I was discussing with a friend about our respective use of LLMs. At some point they told me they no longer read the MR, as LLMs can also do great job on that matter now, which is in sharp contrast with what I do. Not that I don’t auto-review with LLMs, but I use that as a first step, be it mine or some other colleague. And then I ask an LLM to prepare me a reading plan to check the MR, taking into account the activity scope and the implementation architecture. To me it was an obvious way to go, but for them it was something they never considered. That’s random sample, of course they would certainly be cases we would switch the "I wouldn’t have thought about it" role.
So yes, LLMs can be used to produce faster giant piles of unmaintainable codebases. Or they can be used to strengthen processes that lead to code quality. The nail won’t prevent us to knock our thumb, to use a nail in reverse side, or to smash our coworker.
I get the paperclip plant issue, but that’s some extreme scenario (which is of course the point of the allegory), and most bad uses will be far more mundane in how they look and what the consequences are. And most good uses will look more and more transparent to users to the point they won’t even wonder about it at each use. Like, most people open the tap, see water fall and they don’t get a sense of wonder, not giving a thought to this masterpiece of engineering and gratitude for all people that works daily in the shadow for this miracle to happen. They don’t leave the toilets thinking "how freaking amazing such a complex system of wastewater is something I can benefit from everyday, unlike so many other of the 100G humans that walked this earth."
Next time you go to WC or tap some water, think about it.
At this point AI is a better developer than most mid-level SMEs I've worked with. I hate this fact, but I don't have a shred of evidence to dispute it anymore. I work on a 20 year old codebase that thousands of developers use and it finds bugs in pre-AI code daily.
IMO reviews are more useful for communicating some change to the rest of your team so they can maintain it later than as bug finders. AI is better as a bug finder.
Counter prediction: using AI attractive even for employees. Do you really think you’d wanna join such a company? No way.
has the author not started to develop instincts with regard to ai usage and pitfalls?
you won’t lose your expertise if you continue to develop it, and blaming ai is like blaming macros or installers or…
the game hasn’t changed, really, but many are fretting that it has fallen apart.
dumbest take ever. AI is here and not going away, any company that does so will not survive or will be a niche thing for hippies.
i wouldn't know a single engineer who'd want to work at a place like that
Just because it's bad for a human doesn't necessarily mean everything will fall apart - unless a human has to maintain it unaided.
The latter often doesn't even require looking at the code, you can usually feel it just by using the software. From my experience, all software primarily written by AI is full of little bugs and inconsistencies that reflect bad code architecture (such as two very similar pieces of functionality in two different places behaving in wildly different ways, due to the AI being unaware of the first when asked to implement the second and writing the code twice)
I've vibecoded loads of AI apps for myself and almost none of them are still in use. Not because I didn't really want them or they didn't work, but because the more I used them the dirtier I felt, as though I could feel the bad decisions and the bugs underneath just by interacting with it.
Build the systems around the code and let the agents do their work.
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.
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.
>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.
I guess there's no reason to believe these models can't be as smart as a great software architect / engineer or team of such people that build an elegant and maintainable software solution over many years together based on customer feedback, then again the models are appallingly bad at some forms of reasoning, I mean they will "understand" something once you make them aware of it like e.g. a flaw in the software architecture, but when asking them to audit the code and check for issues they will often have a blind spot to finding such problems. It's interesting, like they have very high ability but very little awareness or self-directed thinking outside of the prompts they receive.
I experience all the same issues you mention. I am just predicting where the ball is moving. In the scheme of things, LLMs have been useful for coding for, what, like... 1.5 years??? What other technology has ever existed where people expect it to go from "just came out" to "changes everything for everyone" in 2 years?
In the arc of history I see us at the very, very early stages of AI-driven software development.
> 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.
We're a few years into a new technology that is still improving. This is a point-in-time critique.
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.
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?
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.
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.
You, as an experienced engineer, are doing a lot of hand holding and review of LLM-generated output, maybe even(?) using it as purely a check on your own work. There are others, though, that are essentially outsourcing the entire process to a basic, underspecified chat prompt.
https://github.com/ityonemo/bpa
For my projects that I use day to day in prod there is considerably more hand-holding and code review.
I suppose it'll be a few years before we see the true volume of technical debt catch up with the worst offenders, but even then, without the original LLM conversations associated with it, it'll be difficult to assess that in a structured way. Mind you, that doesn't even address the ever-improving models.
Capital always prefers machines.
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.
This will probably maintain the problem in a different form.
Caring about the architecture only matters if you intend to build on top of it, where it become hard to mutate for needs. Big refactors are quick and (relatively) cheap if you don't care about the code.
Don't get me wrong, I am not in any way a fan of vibe coding but the "you're going to vibe code yourself into a corner you can't get out of" argument doesn't hold water.
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 am dumb on chair making, as the average chair user. I wouldn't trust myself building a commercial-grade chair.
If you are as dumb with software as I am with chair making, you should refrain from building software for others. This is regardless of LLM.
Hilarious and unhinged junior dev and/or outsourced dev and/or expert beginner take here.
Not everything is baby's first React app for an internal business or B2B SaaS startup with 3 users. Sometimes your software is actually used by people, and bugs happen, and you need to figure out why bugs happen, and quickly. You do this by reading the code. Yes, AI is very helpful with this--sometimes. But even SOTA agents cannot solve every bug, particularly when the person directing them has no clue what they're doing, as in your case, so they aren't given good constraints or starting points.
Even if you never read/write code anymore you still need to care. LLMs also suffer from a bad code. LLMs very quickly lose track of their own shit and start producing more bugs.
How do you guide the LLM away from making a horrible choice if you don't understand the internals?
- 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 ...
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.
And, so what? Software as an engineering discipline has long lacked standardization and regulation to be on par with other engineering disciplines, and the fact that code and all its surrounding ecosystems are not "visibile" or "malleable" makes this extremely hard.
You can use terraform and yaml to define infrastructure that literally spins up machines _somewhere_ in the internet. With all its issues, bugs and associated consequences mostly being ignored.
I just don't understand the difficulty in KNOWING what needs to be done: NO LLM usage in university/grad/high schools, NO LLM usage in the first 3 years of your professional career.
Once the basics are solidly grasped, then they can use it at will.
The problem has never been about wisdom, knowledge or LLMs writing good, bad, maintainable or terrible code. It has always been about the skill level of people using it AND on the fact that people start off-loading basic things to these models that they wouldn't before.
If you have the knowledge and "suffered" through experience to learn the fundamentals, than not using LLMs becomes more deterimental than beneficial.
You just CAN NOT skip the trial by fire of learning and absorbing knowledge on your own. That's all.
The only plausible thing to enforce in practice is "no LLM usage in tests", ie using pen and paper or a fully managed digital device.
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.
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.
Imagine breeding a new tomato plant. One is robust and tasty, the other is neither, but the genetic code is easier to understand. Picking the legible plant, because legibility is important to you, and may make future tinkering easier, is a dead end. Being able to create a billion different tomato plants and applying selection pressure is more effective, if you have the resources.
The idea that we are applying all this effort to make creating nauseating ads and heinous travel booking sites easier disgusts me, but hey, whatcha gonna do?!? :-)
Yea, probably with a more expensive model.
Writing code is like many things. One means of producing the output.
My belief is that paying off tech debt requires a better model than creating it. At the same time there are people who will create tech debt no matter the tool.
Should the models stop improving, the debt will pile up.
Alternatively stated: codebases will expand to the limit of an organization's ability to manage them, so the equilibrium will remain at the point of near, but not total, incomprehensibility.
It was mostly 256k, then it went to 1M and now it has stalled there.
For starters, LLM’s need to stop being our friends. But that won’t happen because the dopamine loop is baked in on purpose.
Source?
> 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.
>
> For one, AI is not trained on what it means for code to be maintainable. For instance, any reinforcement learning done needs a reward signal that can be measured immediately, not in months or years.
I think at the end of the day, it is not impossible to have AI write "good" or "high quality" code. If anything, once the patterns are established, AI will be more likely to adhere to the patterns and rules than any human team. It requires the most experienced engineers on the team to split their time writing the core patterns and documenting them in references/skills.
But it takes a lot of "taste" and a willingness to slow down a bit with AI (to create necessary artifacts), something teams find hard to do when you can ship so fast now.
My experience has been that there is a camp of very senior engineers that are unwilling to adapt to reality and focus on documentation and writing (effectively producing skills and agent guidance which multiplies their effectiveness); they will cling to their knowledge thinking coding a sacred art.
I don't think so. It's true that human also write shitty code but the key difference is we actually remember what is the intention behind those crappy implementations so someone can fix it later. aka it is the matter of long term memory that currently LLM architecture is not capable of.
You can argue that claude can read the whole linux codebase and report bugs, but they can only report local bugs, not systematic one. 1M context windows seems like huge, but the effective range is actually pretty limited, and it still does not equal to human insight.
> aka it is the matter of long term memory that currently LLM architecture is not capable of
Code comments are line-of-sight for agents and one of the cheapest, highest leverage ways to get better coding performance from AI because unlike skills that may or may not activate, comments end up in context as long as they are well placed and carry the right instructions.
Best places to have it leave comments: 1) start of the file because it frequently uses `sed -n 1,200p` to read files and 2) inside the body of the method because it may find by keyword and read a few lines past. If your harness is set up with an LSP, language standard comments are also useful because then it can read comments on the member.
Tips for comments: point it to other, related members or artifacts; point it to external canonical docs; point is to a specific issue number or PR; have examples directly in the comment using your language's example markers; point it to example, reference usages in code. Use AGENTS.md to tell your agents how you want it to leave comments and to specifically read, follow, and maintain comments.
You don't need infrastructure or special architecture; Every coding agent is text-in, text-out. You need comments that get carried with text-in and a bit of guidance to the agent on how to use comments effectively.
Well, and "intention" is a mine field of its own.
1M context window is plenty. Once it's skimmed the code and come up with a theory for the problem, it can spin up a subagent that has a whole fresh context window and it can dedicate the whole thing to that one hunch.
LLMs cannot truly learn and so are destined to produce whatever the "average" software looked like at their training cutoff, or worse to produce code based on _other_ LLM generated code.
Ouroboros eat your heart out
Why do you think that "write maintainable code" is somehow impossible to learn for an AI? We already have AI storming the frontiers of research math - way beyond the "average" of the field. If you can RL for "better at math", I see no reason why "better at maintaining code" would be somehow impossible.
You can construct an RL env where a codebase is presented as a "tree", and the AI is given one change to make at a time - and the per-change reward is not just whether the change itself has been evaluated as "made successfully", but also whether it made future changes down the line more or less likely to be successful, and harder or easier to make.
This is a formulation already used by some "maintainable code" benchmarks, so I expect something like it to make is way into frontier lab RL pipelines some time between "next week" and "a couple months ago".
Historically, this was caused by hiring the cheapest developers one can find, having high turnover, outsourcing, pushing to ship at any cost and more. AI just lets you get there faster, and without having to hire bargain bin Indians.
The thing is, today's AI is already far better at "code rot per feature shipped" than the worst of developers - and I struggle to believe that we're at the limit there.
I've already seen benchmarks that test for AI's ability to make incremental changes and tweaks to code continuously - thus, tracking whether earlier changes make the latter changes harder. This makes for a clear target to RL for.
AI produced code is a function of the team driving and instructing the agents along with the scaffolding produced by the team (skills, examples, docs, comments); same with human teams.
A team that cannot guide a human team to produce better code will not be able to guide an AI team to produce better code because it's the same skillset: being able to write good docs, create constraints structurally in code, produce core architecture that enforces good behavior.
Yes but the ceiling is still higher, and that's the author's point. If you vibe code, without code review, code becomes a mess quickly. If humans write code by hand, then this is often the case too, but crucially, this is not unavoidable. Sure, most codebases are a terrible mess, but some are not. AIs unfortunately got trained on all of them (+ reinforcement-learned stuff) and therefore their quality standard is about as low as that of the average codebase, ie pretty damn bad.
But there are plenty examples of acceptably decent yet long-lived codebases, both in OSS and inside companies. You simply couldn't get that quality by vibe coding. (unless you review every line of code and every design decision, at which point you're about as fast as you would be writing it all by hand, assuming some seniority)
I really don't think so, poor written human code IME is rarely overly complex, where as the AI code is almost always vastly over complex. Naturally complexity can be an issue because it leads to more surface area for failures and challenges to diagnose, but where I am REALLY seeing an issue is the complexity hiding an issue. Something that should normally fail or produce an error is covered up by something multiple layers deep in the code that returns an incorrect value instead of an error when something goes off the rails.
That is why I believe the most senior engineers on the team with the most scars and most experience need to shift into writing those constraints instead of writing code.
In writing those constraints, they can multiply their effect across a tireless fleet of agents that generally want to copy existing patterns and can be guided to use skills.
When the pace is slower you can notice mistakes earlier because you have time to reflect. It also allows you to detect when it’s becoming hard to maintain and you can correct course, rather than after it has become an unworkable mess.
> because you have time to reflect
There is no team without tech debt because there is no "golden" project where every decision has been made right because of reflection on decisions made wrong.
False. AI is evolving by leaps and bounds and the outputs are better and better by the day
Personally, repeating what the article says, I haven't coded since may 2026, not a single line, and AI has been delivering exceptional results, improving by the day
Any AI related article necessarily needs to consider future improvements as they will come not by surprise but by steady refinements, and posts like this will look just like the same early AI slop they complain about
Coding IS solved
The world will always have people whose coding skills are beaten by a chatbot.
> Coding IS solved
You coding is solved.
They can't think that a software can be written, designed and maintained in English or higher constructs.
It is binary of either vibe coded app, or worshipping the code that they worship (and I spent 20+ years on)..the industry is moving on faster than most can wrap their heads on, but for those who understand software engineering was always beyond and above code, they will adapt, meanwhile, those who built their entire identity and skillset around managing code will struggle. There will be a place for those people, but it will be niche and specific, and we won't need as many.
I think this is a bad take... if you are going that long without noticing, you are (hopefully) delivering end user value all that time. That is the main driver of code. You can normally dig/hack/rearchitect your way out of an ugly code situation. If you've been building value for the last year based on the hacky code, that's a win.
The truth is, "good code" is relative. It is determined by the specific domain and the composition of the team. Is incomprehensible FP (Functional Programming) code good? No, it isn't. A programmer must assess the team's capabilities and adapt accordingly. Good code is ultimately something that morphs based on the shape of the organization. Once defined this way, good code might share certain commonalities (like readability or a shared mental model), but its actual form varies wildly.
So, what is good code? That definition is missing. To be blunt, the Hacker News posts insisting that we must write "good code" are essentially a form of self-hypnosis.
Just look at paradigms. The mechanics of OOP have changed significantly, FP approaches have evolved, and DOD or DDD are fundamentally different from their early days. Whenever paradigms are discussed, someone claims, "That problem was solved in the past, and nowadays we do X," only for someone else to reply, "I don't think that's actually solved," leading to a fragmented breakdown in consensus. Ultimately, which knowledge remains as tacit knowledge is entirely dependent on the organization's capability.
You could argue that AI is terrible at simplifying code. However, I am skeptical that AI coding needs to be identical to human coding. When you actually code with AI, it often produces structures humans would call anti-patterns, including God Objects. Yet some of those structures can be faster or simpler for machines to navigate. There is no reason to assume that the optimal modularity for AI maintainers must be identical to the optimal modularity for human maintainers.
Of course, I am not denying that the rewards of good architecture are delayed, or that there comes a point where maintenance becomes impossible. But as the AI era ushers in an age of overproduction, software could become disposable, strictly personal, highly tailored to small niches, or ultimately, heavily polarized.
Realistically, programming domains fall into two major categories: "ship it and forget it" (one-offs) and continuous services. I agree with the OP's point that AI struggles to understand boundary delineations. But honestly, you can enforce those boundaries by injecting them into the spec. How those boundaries are drawn in the first place, however, is purely a matter of personal experience.
Personally, I define "good code" as code that allows the entity responsible for the software to achieve its purpose with a sufficiently low cost and error rate, factoring in the software's expected lifespan and future changes.
If you ask an AI to generate work based on this standard of what level of code is "adequate," you might get entirely different results. The biggest problem with discussions around AI is not just that ideological identities prevent proper evaluation (as seen in that article), but that the AI itself scales proportionally to its input. It is an incredibly difficult issue to judge because you don't know an individual's workflow or exactly how they are utilizing the tool.
I do think the value of reading code is important. However, much of what we are discussing in the AI era is actually rooted in the path dependency of how to become a good human senior developer.
Instead, the core focus of AI-driven development might shift toward defining broader abstractions: data semantics, invariant external contracts, and migration strategies.
Ultimately, I believe the paradigm shift of our era should lead us to ask: "How do we write code most economically in a system where AI is the primary maintainer?" The OP might think differently, but at least, that is where I stand.
Let's stop overvaluing human work. Sure, I don't want AI to write the entire codebase without I know what the fuck it did.
But AI is on an equal footing with an average dev.
This is the labor theory of value; consumers don't care if the code is hand-made, they want the cheap goods (software) that are the output.
The agents are trained by contained data. All agents. Similar data sets. Human knowledge packaged into little boxes that all look just the same.
When the boxes are doing all the work for us, the differences between them will come down to color.
There’s yellow ones, and blue ones…. And, they’re all made out of ticky tacky and they all cost just the same…
Well said. The cost of bad architecture imposes a heavy penalty on the project down the line.
> The proficient developers, the experts, rely on their intuition built with sweat and tears, working long hours trying to debug and fix production issues
Long hours of grinding with the problem, the solution, the code and the machine all makes for an intuitive understanding of what a good architecture is in the first place.
LLMs are a tool. A useful tool unlike any we have seen so far. But still a tool. Treating it as such and leveraging it should be prioritized.
Everything is reactionary. The focus is always on short term profits. No one cares about long term effects --- until they start impacting short term profits.
This is an inherent vulnerability that is easily exploited by someone willing to engage in some simple, long term planning --- like China has already done with manufacturing.
For decades, the USA gladly shifted manufacturing to China without a second thought. Now, we lack the expertise to make our own and have no choice but to use China.
Replace "manufacturing" with "software" and "China" with "AI" and it's deja vu all over again.
The author seems to be confusing "I didn't write any code" with "I don't care about software design and maintainability". There exist maintainable and thoughtfully designed software systems for which the designer did not write any code and didn't read most of it. It's not the median, but the median software project has always been unmaintainable before agents.
Just to give a hot take, it's funny to look at his builtwith.com. As a developer you have a static site that depends on Cloudflare, Mailchimp, Postmark, Isso ...
Twenty years ago any self-respecting dev would have run the equivalent of all that themselves on their own metal. In 2026 elite neckbeard practice is write the "never reach mastery, because they no longer make choices, they no longer take responsibility" post, hit "publish" and it's magically deployed around the global internet for you. Like a child.
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.
Yes, a "NO-CLOUD" policy was already so popular, surely this will happen too.
The argument I'd make in support of the title would be that wisdom is embodied knowledge - knowledge earned from direct experience. Known first-hand. An AI can already have linguistic/technical ability that exceeds a human (at various levels of skill, even) - in coding, copy-editing, what have you. But wisdom? That is the scar tissue we carry from wrong choices. Until an agent has persistent sense of self and a "body" (or pseudo-body) whose existence it is predicated on (thus creating incentivization/outcome alignment), it will not be able to approach "wisdom" in the sense that humans do.
That may seem to have no bearing on the specific subset of tasks related to coding and so on - but it actually does. Humans have a hard limit of attention span, hours in a day, years in a life. An AI does not. So if it fucks things up royally by half-assing an architectural decision, it won't care. And if it doesn't have a persistent memory, it won't even know that it made that mistake before. So it has neither the prerequisite abilities nor the incentives to gain what would be hard-earned wisdom from its mistakes.
In fact, as long as the business model of its creators depends on more usage = more profit, they are actively discouraged from implementing any kind of wisdom-like ability.
For them to be able to offer that without undercutting their profit, they'd need to be able to execute "outcome based billing".
>>"The AI learns rules from rulebooks meant for beginners. The AI notices patterns from code in the wild and let’s be honest, most code in the wild is pretty bad."
Similarly, for self-driving, the AI for driving learns from rulebooks meant for beginners and observes patterns of driving from ordinary drivers in the wild and let’s be honest, most ordinary drivers in the wild are pretty bad. The best the AI self drivers trained in this way can hope to get is the average slop driver minus the catastrophic errors.
Similarly, in legal specialties, there are a few highly expert and wise practitioners, and hordes of average practitioners, and quite a few near-malpractice practitioners, and it is the latter group who write the most stuff on the web because that is how they do marketing, trying to make their ignorance look better — and mostly louder — than what the average lay-person knows. The AIs can NOT tell the difference and 'learns', and dispenses both the good and the actively harmful advice. Acdg to relative who is a top expert in their specialty, AIs can both find key relationships between laws in complicated situations but also readily dispense the actively harmful advice, and you MUST already be a top expert to recognize which is which.
They are great averaging machines, but when average is not good enough, you must be on top of your game.
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.
Absolutely people were extremely dismissive to anyone saying that we're losing the ability to make things in this country!
There were all these theories like Comparative Advantage that people would trot out to point out that, if you don't like outsourcing, not only are you ignorant and backwards you're also probably racist.
I guess we were all lacking wisdom.
The idea that the US lost its manufacturing is not based in reality. One reason people think that is because people think cheap plastic crap and consumer electronics when they think “manufacturing.” Another reason is that US manufacturing has become highly efficient and automated, so a fairly small portion of the population works in it.
Yeah, your iPhone wasn’t built in the US. But the plane that got it here probably was.
If there was no China, Globalization would spread very strongly with Bangladesh manufacturing clothes and Milan doing the fashion show. All those calculations are coming undone. So please keep this main reason front and center when we discuss this matters.
This is already showing not to be true with AI. Institutional knowledge is not the same as knowing how to implement low-level software details. Even today, every company does not need engineers to remember git cli syntax by memory, how to write parsers for JSON, or write the large amount of boilerplate from scratch that is at every software company. Most company's already hire engineers who have zero experience in the existing code base, yet they are productive despite this lack of institutional knowledge. For a company to maintain institutional knowledge they may only need N/K engineers.
Do not share videos with si parameters. It links together the accounts of the sender and receiver.
@mitxela: Thank to for taking a moment of your time to educate those who didn't know.
To build a $100M software company, you need 6 engineers and 6 laptops.
To build a $100M hardware company, you need 60 engineers and $100M.
Everyone decided on the most logical choice. It gets even worse if you jump into profit margins, as software is the unambiguous winner there too.
The resilience is more in ensuring archival process to ensure it's never lost forever, and a continuity plan to ensure enough (but few) people still know.
I believe this is what Socrates said (verbatim) about the invention of writing.
Besides, the dialogue is not about writing itself, but about writing down political speeches and the consequences and impact of political speeches being read rather than heard.
Also, the dialogue dates from about three thousand years after writing was invented.
Some direct quotes from the dialogue:
SOCRATES: So, it is obvious to everyone that speech writing is not shameful, not in itself anyway (258d)
SOCRATES: We are left with the question of the appropriateness and inappropriateness of writing, and how it may be executed in a worthy or an inappropriate manner. Do you agree? (274b)
And this, a very relevant insight for an age when some are convinced that text analysis should suffice for intelligent work:
SOCRATES: Then a person who thinks he has left a skill in writing behind him, and anyone who, for his part, inherits this on the assumption that something clear and certain will emerge from writing, would be full of enormous silliness, and indeed ignorant of the prophetic words of Ammon in believing that written words are anything more than a reminder to a person who already knows whatever it is that the written words may refer to.
Ironically, the sharpest criticism Socrates made of writing in the often misread Phaedrus was of misreading texts.
We have significantly degraded in our ability to memorize e.g. epic poems or preserve oral traditions since the advent of writing.
I know that sounds trite in retrospect, given the benefits, and that's half the point. But there are real tradeoffs too! For example, culture has a LOT more generation-to-generation turnover as a result of literacy being common, making it unstable.
In the world where we didn’t outsource Western manufacturing to China, our homes would not be overflowing with disposable junk; in the world where the Luddites won, our wardrobes would not be stuffed full of disposable clothes; perhaps in the world where Socrates won our minds would not be jammed full of nonsense.
Having something else do all of that for you is completely different and essentially tossing any gain outside of the creation of some gray slurry of an output in the trash.
Right. And the spin I would put on your point: while the U.S. could never compete with outsourced labor, institutional manufacturing expertise could (you would think) still be valuable to startups finding some kind of niche in the manufacturing space.
With all this new 3D printing infrastructure, and constantly improving robotics, maybe some manufacturing efficiencies in some spaces could emerge that compete on cost and outcompete the cost of shipping stuff across an ocean. The availability of institutional expertise could be one of the necessary ingredients for mixing and matching the way to a new efficiency.
Yeah it's bleak in terms of automating away labor, but I'd like to think robotics is the next automated frontier after LLMs and we want to get there first.
At the very latest, when "AGI robots" will be commonplace and taking on the most crucial labour on our behalf.
That's the whole point of Global managerial class even without AI. The idea is just by creating metrics one can measure how teams, groups, organization and whole industries are performing.
Before the current AI complex the previous one Agile Industry Complex One can see we starting producing 10x more code, 50x more JIRAs resolved, 100x more network bandwidth used. All these metrics tremendous gain in productivity.
Also it may not have been seen in US but in many places a change in political regime does want to burn down previously collected institutional knowledge because new dispensation have their own idea about what is knowledge, who will preserve it and how it will be preserved.
I wish more people would at least consider this idea.
It just seems to me that fundamentally this is a knowledge organization problem.
“Any idiot can run a business, but it takes an MBA to run a business that barely functions.”
So the client who writes the ticket understand the domain, the LLM that implement it understand it too.
The dev is the only one that is clueless.
Maybe it will be no big deal, and nobody will read code anymore, but it is understandable why somebody might be concerned about it.
The political move to put all of that death and destruction onto China, where environmental regulation is willfully ignored in the interest of economics, was a smart move on their end.
We lost so much intellectual knowledge and other "tribal" technology in these processes to this outsourcing, which is unfortunate. We're almost having to rebuild our manufacturing from first principles, which may not be a bad thing.
Avoiding environmental regulation was one reason to move factories to China, avoiding unions and high wages was another.
But the local communities weren't demanding that their factories be moved overseas so they could have a clean if impoverished towns. Your entire causality is completely backwards.
Americans produce the highest technology equipment in the world. Our machining base is structurally sound, but its all making weapons, so you don't hear about it.
It's true that China benefitted immensely from outsourcing, but they took the jobs Americans didn't want. It's the same with immigration today - folks cross the Rio Grande to do chores that Americans won't, or others fly in and work for nothing in academia while they wait for their PhD.
> The problem imo is the slow deterioration of institutional knowledge that offloading the mental task of wisdom gathering to AI is causing.
Have you considered the institutional knowledge could actually be actively preserved and distributed with AI? The kind of tacit knowledge that is situated and not readily preserved in a book might be absorbed by thinking machines and proliferated to the next person who needs it. The caveats would be trade secrets, skill differentiators, that people might not be willing to discuss, and manufacturing secrets of national importance. Maybe you can think of others.
Well that's what this whole debate comes down to. And, to my mind at least, it's a rare case of an actually interesting question about AI, because like the article says, it can plausibly deteriorate exactly that kind of knowledge. But as you note, it can also maintain it.
I think there's a sense in which it might do both at the same time. AI's version of on call tacit knowledge might be something like lazy-loading just-in-time tacit knowledge, but at the cost of who knows what cognitive paths we might have by keeping that knowledge resting in-house. We would gain real efficiency but we wouldn't know we wouldn't know.
>The kind of tacit knowledge that is situated and not readily preserved in a book might be absorbed by thinking machines and proliferated to the next person who needs it. The caveats would be trade secrets, skill differentiators, that people might not be willing to discuss, and manufacturing secrets of national importance. Maybe you can think of others.
It's funny that you credit AI with this (and I don't disagree), because tacit knowledge was exactly the thing Hubert Dreyfus spent a career insisting computers would never have, and his wisdom was taught to generations of undergraduates across the country and world who treated it as received wisdom and still is regarded as such in certain academic corners.
It's also very automated so the jobs went away, but the US continues to manufactures a lot of things.
You don't develop "institutional knowledge" from a few prompts. It takes years to develop them.
They took jobs that Americans would have wanted higher pay to do, higher benefits, higher safety standards...
Bogus nonsense.
1. Manufacturing output in West/America is higher than it ever was in history.
2. What has reduced over time is the number of people employed in manufacturing, not manufacturing output. And that's an effect of automation, same as it happened over centuries in agriculture.
3. West/America did not decide anything, the realities of capitalism did.
You're either a capitalist and embrace efficiency of producing where it makes sense (for many different reasons) or you're fine with inefficient third-tier industries.
The tax payer may help a handful of sectors that would otherwise die survive if they are truly critical for national security, but just a handful, not all of them.
"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.
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.
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.
I'm going to make my own prediction: this isn't going to happen
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.
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.
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
To me, AIs seem to write code with a normal (low) level of quality, but I now have so many more tools to make sure the software works nonetheless, to refactor quickly, and to encode better practices that (mostly) stick in the future.
So to me, this is all a huge net win. But I guess if I'd ever worked on a pristine perfectly engineered system, I might see this all differently.
To me, it's always been a mess, and I'm just ecstatic that I have more ways to manage the mess now.
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.
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.
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.
"A Project must have proper tests and specs, and only incidentally for a working program that executes"
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.
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.
The short version of the result is that we're completely copying the leaders in our market that have 100x our marketing budget because they're the only ones with documented strategies for AI to cheatsheet from, with no sense or irony or concern that perhaps their scale is a core part of their strategy.
It's not just institutional knowledge. We're suffering the death of the specialist. Now every generalist is pulling triggers with no sense of limitations.
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.
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.
It is perfectly possible to vibe-code a badly designed app that still passes those 12, 15 or whatever points you define.
"Knowing what you don't know is the beginning of wisdom", and a new take on AI reasoning, but didn't actually use the word wisdom in the text.
Anyway, I think we're increasingly finding the old adage "garbage-in, garbage-out" still applies.
The question I would ask is: Does anyone first ask the AI for a non-coding solution rather than jumping straight to code? Is Systems Analysis dead? Does no one read Hawryszkiewycz anymore?
This takes time away from implementing new features, but that’s true of all code health maintenance.
This only works in small projects. For large projects, it is close to impossible. Everybody talks about how new models appear all the time and nobody comments on the fact that context size has almost stalled.
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.
True, but we'll also get higher standards I think. It's become easier to do things, so skilled people can do more complicated things. We compare to what humans can achieve.
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?
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.
Generally though, you are also investing your time into leveling up junior engineers to take over responsibilities from you. I just never really see that happening with AI. Even as it gets "better" technically, there's no real growth pattern to its work and it doesn't understand ownership or responsibility.
But if scaling isn't a problem, then sure just write it yourself.
It doesn’t take much effort to setup cross-agent reviews and automatic reviews for slop and accretion, while directing design decision questions back to the human to consider. I have had a considerable increase in throughput of code that I designed and made the important decisions about, and that I’m pleased with the quality of, although as always in these discussions, someone will be a long shortly to tell me that that implies I must be a terrible engineer.
And the observation that it's happening slower than it was means, not that it's stalled, but at least that it's not exponential anymore. The exponential phase is already over. That has implications for where we think the plateau is likely to be.
Cyclomatic complexity has been pretty solidly discredited within the maintainability research community for decades.
Sonar's cognitive complexity metric is a bit better, but here's a study that found that it still only has about a 0.5 correlation with how much difficulty programmers actually had reading code as measured by multiple methods.
They found that the most accurate way to measure code complexity that didn't involve something like an eye tracker or EEG is still basically just vibes - asking programmers if they thought it was hard to understand.
https://www.frontiersin.org/journals/neuroscience/articles/1...
Halstead Effort came in second, and scored pretty well, but here's another one where it doesn't do so well, either. And it scores the SonarQube metrics even worse, with only a 0.35 correlation: https://www.sciencedirect.com/science/article/abs/pii/S01641...
> There are dozens of ways to measure code maintainability.
There are no good ways. I'm averse to making absolute statements, but here I'll take that chance. I worked in dev producitivy for years with people who spent decades in that domain across multiple companies with very high volumes of code production. Everybody agreed: All metrics are flawed and even a combination of metrics is insufficient.
Just to give one fundamental reason (in addition to a lot of the sibling comments): for any given metric there are an infinite set of counter-examples that don't trigger any thresholds but are clearly bad code. So these metrics typically only help in trivial cases, don't catch a majority of the cases, and so often become more of an annoyance due to low SNR. A lot of dev productivity work ends up being wiring these metrics in and then providing escape hatches when they inevitably get too noisy!
And most relevant to this discussion: these tools do not say anything about higher-level concerns like architecture, over-engineering and design, which IME is where agents tend to mess up most. I've almost never had a complaint about the code itself; the logic, naming, functions, data structures, even a lot of the testing, are all on point. It's always been the higher-level structure and design: over-engineering, duplicate classes, suboptimal abstractions, redundant operations across layers that could be solved by adding a single variable in a class, etc. etc.
I think the problem, like with code written by humans, is lack of sufficient context while doing a task leading to tunnel-vision. This is why we need to oversee and ensure things are good holistically. I suspect models are now good enough to play the role of an architect as well, though, and I've read some indications of that online... I just haven't tried giving them that much control yet.
Yes. But I think the idea is without "hand written domain driven design development" the result trends to "vibe coded by someone with no technical knowledge or inclination," as developers de-skill.
Somewhat relevant parallel..
Calculators exist, but not all is lost:
- lot of (most?) people can do basic multiplication (I’m too lazy to fetch any stats but I hope you’ll have some observations in your bubble dear reader)
- some people actually compete in mental calculations https://worldmentalcalculation.com/mental-calculations-world...
Same will be with software devs, enthusiasts will continue to exist.
Somewhere in the middle of that continuum sits - "domain driven specifications, described using high level english concepts(that are well defined) from the domain , combined with a selection of a few standard architectures"
Induction fallacy at its best
There's the rub. It requires knowing about and caring about maintainability. And a lot of the people who "haven't written a line of code since 2025" don't care
I’m beginning to believe that if this was a “solvable” problem then the billions of dollars poured into coding agents would have solved it by now.
You say that like adding "Make it maintainable." to your prompts solves the problem. But the reality is we only have weak metrics for measuring maintainability. For example, you can trivially optimize for Cyclomatic complexity by blowing away abstractions and duplicating code everywhere. That doesn't make the code better. Cyclomatic complexity is a tool that has to be applied judiciously.
That doesn't mean you can't or shouldn't use AI to generate code. But it does mean if you want your project to scale, you're still going to need a lot of developer involvement at the code level to ensure the code remains maintainable so that future developers can build on top of it. AI is not like compilers, which allow developers to build complex solutions without being proficient at the next level down (assembly).
> 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.
One way of understanding "good" here is "actionable", imho.
But that's not actually a change. Humans wouldn't magically make everything maintainable if you don't tell them to (and maybe not even if you do). You have to monitor them and train them, carefully, basically forever.
The trillion dollar question is how you do this, if your employees do not care (they are optimising for salary & time spent not code quality) and you have no way of telling apart AI slop vs. good maintainable code. (If you could you would just train the AI.)
Before AI there was at least some way to tell apart good programmers from bad, because there was some human effort involved in coding. Now with AI and slop generation there is almost now way to do this.
From what I see is it's mainly managers and higher brass who doesn't care about code quality and sustainability, and aims to drive time to market metrics down aggressively with AI.
Any employee who cares about code quality will become a poor performer with a red luddite label because they dare to change what the AI has emitted for them.
I'd love to be wrong, very wrong about this, actually.
Sounds like there is a compensation problem then.
There were only bad ways, and the best way was to just find people who were both good programmers and cared about quality to keep an eye on the rest. Nothing much has changed in that respect.
I don't think it's that hard of a question to answer. I've noticed on my team, our thinking has shifted from how do you directly solve a problem, to how you get an agent to effectively solve the problem and not produce slop in the process.
One thing that we have done that's probably made the biggest impact is alot more upfront architecture with the knowledge that pretty soon agents will be running wild all over the code. Having worked with these agents for a while now, you get a very good sense of how they will go about solving a problem and the various footguns they will encounter along the way. Editing an AGENTS.md file or building a skill is not nearly as fun as coding by hand but it will pay dividends over and over if you do it right.
Another big thing is doing refactoring passes. Early on in our projects our agents generated ALOT of slop and we had to go back and fix alot of it. But every time we did one of these passes, a major aspect was improving agent instructions / skills / etc so it doesn't happen again. It can be a painful process at first but I found that over time, the amount of slop the agent produces goes down by orders of magnitude.
I feel like we're still very much programming, but we're now doing it at a "higher level" where we are not writing the code ourselves but instructing the agent to. And IMHO, properly instructing an agent on a production codebase is not a trivial task.
I know how to distinguish good maintainable code from garbage. I have known for quite a few years. But knowing how to train someone, or an AI? I'm a good coder, not necessarily a good teacher. And there are things about code that I _feel_, not that I can rationally explain.
SonarQube
That's why companies were interviewing people on tasks that had nothing to do with writing maintainable software. /s
I understand the concern and it should be addressed and researched. But, simply saying "humans were writing code themselves" doesn't provide any evidence for better quality.
I for one, have far more rigorous quality checks in my hobby projects (where AI coded), than I ever could justify when I hand-coded them.
I'm not claiming to be everybody, but surely a good portion of the population are using these technologies similarly.
Yet.
I'm sure in few years, as new criteria enter benchmarks, AI will be creating the clearest and smartest code people every seen, by default.
Most of proprietary software is just crap, and always has been. The agents are not producing worse code than typical, demotivated, i-dont-care-what-i-am-building-i-wont-try-using-it corporate development teams have over the years. I'd even bet that because now making changes and fixes is so much easier, the user perceived quality will trend upwards for popular stuff.
The code itself may or may not be spaghetti. Not that I care as a user. User experience and code quality had never a particularly strong correlation even before AI.
Code examples are literally bread and butter when it comes to learning.
How can you learn without looking at code? That's like saying that you can learn to be an architect without looking at drawings...
It is fault tolerant, distributed broker which gurantees durable queues, pub/sub and RPC all into one easy to use programming model. The application is in production and passing millions of messages every day with sub-millisecond performance, you can crash a server and replica set invokes within seconds without losing any messages. The entire project is created in less than a month with part time working, just because of AI. Its in production and already proven.
Yes, you're running it in production, but to put it in perspective: PHP 5 was also proven production software at one point, running way more production instances than you.
~41k SLOC, ~11k lines of comments
I also don't care the exact cpu instructions my compiler emits. In fact I have no idea what the "stuff" it emits means (beyond the most elementary)... That doesnt stop me from creating very useful software that maybe even billions of people rely on (once you include the users of products that use our software)
This is the opposite of really striving to understand what your AI is producing.
Those with 20 years of experience are crushing it.
My worry is not about us. The worry is about the kids. I'm tech lead. I don't think the juniors at my company are learning anything from me. I'm not certain they're learning anything about _software_. Idk, I could be wrong. We barely talk because everyone has become so siloed.
But why though?
This may be Dunning–Kruger effect in action. (this is not an insult, but showing cognitive biases)
1: https://en.wikipedia.org/wiki/Dunning%E2%80%93Kruger_effect
lol
and 9/10 times project open source, project bad slop
You can make a choice not to become a button pusher and still do things by hand. You dont have to fry your brain. You're falling for a massive trap to strip you of your value.
Handmade watches are a tiny, niche market; they survive only because they've positioned themselves as a status symbol. Quartz watches are both cheaper and more accurate.
There is not room in the world for more handmade watchmakers, and there's not going to be room for much "artisan software" either.
I understand why people are resistant to this from an emotional perspective, but I really don't see a plateau in sight. RLVR is clearly still cooking and narrow RSI seems to be on the horizon.
But I'm also a realist. If the technology exists, it will be used to the maximum economical extent.
Economics depend on that a lot.
It's non-existent. LLMs still suck at writing code just as much as they did at the beginning of 2026, or 2025 for that matter. LLM proponents are always trying to hype everyone up on the supposed improvements, but they have never yet been real. That means they are unlikely to be real in the future either.
Why would companies employ human engineers then? What is the value addition to justify high human salaries. If AI is going to get so good (and I am not saying it won’t happen, that’s a separate debate), why can’t AI figure out the prompts itself?
Yes, AI for now has significant code quality issues, but that's mostly because it lacks agency to take care of code quality unless you explicitly tell it to. It is good at refactoring its own messes when you even vaguely ask for it. So while something like Astra still needs supervision to produce decent code, I expect that in a year or two it will be unnecessary.
Also a confirmation to people who have the same inner thoughts and are ashamed to admit in public that they think the exact same thing.
I think we need such kind of posts to combat the influx of AI news.
What you don't see from most perspectives are the silent masses who simply don't engage, don't care about the discussion, and/or are too busy doing what they enjoy.
It has been a fairly painful experience for me to shift my thinking on this, but it's a much better mindset. I still care about many of the same code quality concerns I always have, but I'm thinking a lot more about why I care than I once did.
That mindset has also deteriorated my working environment due to some coworkers buying into it.
So it's kind of nice to see some sanity checks that align with my beliefs too. I can share articles like this with my teammates. I can see that I'm not alone in thinking most LLM code is slop.
I think too junior devs NEED to see this. My team had a couple of promising juniors who are now completely brain rotted by AI and can't even write "Hello World" without consulting Claude anymore.
Anyone could say the same about any post they don't agree with, doesn't seem very helpful.
If you're just writing code to fuck around or automate a small part of your life, whatever. But if you're making a big system or wanting other people to use your product, these things about how to make good software become more relevant.
Just look at the growing gap in traffic accident rates between human and AI driven cars. Humans are losing.
"We're not going to use tractors to plow the fields"
"We're not going to use trucks to deliver our produce"
"We're not going to use planes to meet with our global partners"
"We're not going to use computers to run our business"
"We're not going to open a web shop, brick and mortar forever"
"We're not going to use AI to make decisions for us"Pet peeve - luddites had never been against technology. They had been against using low paid inexperienced grunt workers to displace well paid experts.
See: https://www.smithsonianmag.com/history/what-the-luddites-rea...
As the Industrial Revolution began, workers naturally worried about being displaced by increasingly efficient machines. But the Luddites themselves “were totally fine with machines,” says Kevin Binfield, editor of the 2004 collection Writings of the Luddites. They confined their attacks to manufacturers who used machines in what they called “a fraudulent and deceitful manner” to get around standard labor practices. “They just wanted machines that made high-quality goods,” says Binfield, “and they wanted these machines to be run by workers who had gone through an apprenticeship and got paid decent wages. Those were their only concerns.”"We're not going to wear Google Glass"
"We're not going to use Blockchain for every single transaction"
"We're not going to connect every single object and device we have to IoT"
https://archive.nytimes.com/www.nytimes.com/books/97/05/18/r...
And why are the people calling people Luddite some of the least intelligent people I meet, and seem to be complete sheep fighting some psychological war on behalf of their billionaire lords who own the machinery.
Not wanting to hand off all your labor to a machine does not make you a luddite, nor should it be acceptable to call people that because you dont know how to have a real conversation.
So, I think if you know just a little bit of film making and writing and LLMs you know you could not prompt any of them into existence with just saying ”write lord of the rings”
But if you have an idea for a creative project, using an LLM to explore ideas can definitely be a fruitful endeavour.
The gods don’t give gifts by ghost goblins cannot be dismissed as slop. I’ve personally used LLM to explore large sets of photographs to use as a backdrop for a DJ set, essentially to have the set tell a story.
So, as most things, it’s complicated. But at the same time I’m curious what will happen next
Maybe you are right, maybe not, let's see. Unfortunately I kinda agree, because humans are really good at being lazy and going in the path of least resistance (including me). It's genuinely difficult to not use AI even if it makes my work worse, as long as it's easier and faster.
I personally don’t trust coding agents to have enough context to write domain-specific table schemas, and I don’t have the patience to transcribe all of the context into a natural language prompt. If I ask it to, it’ll write something for sure, and maybe that can be a jumping point for me, but at some point I have to physically write what the columns will be.
But reading code? What does that accomplish, other than to slow your dev process down enormously? Serious question.
I'll let you know how it goes... My new VP of engineering is a 'no looking at code' type of guy and is ripping 10K LOC PRs / Docs / plans against our 25 year old codebase and I would not say that they're 'good' PRs.
Maybe I'm completely wrong, but I think reading the code is more valuable than ever when working in a full-stack / small company role. I can tell you exactly what the business logic or functionality is for a certain piece of our system, in truth, without having to step through and make sense of ambiguous docs (that were also AI generated).
(I have a sneaking suspicion that in two years or less, my small team is going to significantly compromise the integrity of this codebase. Maybe by then we can refactor with GPT 12.)
This is the classic "make no mistakes".
On a serious note, I might set as criteria "avoid code duplication". Does that mean that the model/agent will actually follow it?
> What does that accomplish, other than to slow your dev process down enormously?
I am an OSS developer and I often see PRs (i.e. from the general public) that look correct, pass all CI checks, are heavily documented and they are still wrong.
Most of the times either they duplicate code that already exists somewhere else, or they implement a "feature" by opening a can of worms for subsequent "features" in the same area.
I mean, people have been dreaming about this for decades. The whole reason UML was so overdesigned in the first place was in hopes that people could code by drawing boxes and lines. The full COBRA spec had software autonomously buying components in digital marketplaces and installing without human supervision.
It is funny to see engineers insisting that there's no way a machine could do this better. If anything, the surprise is that it took good old human language -- that second L in LLM -- to get the computer to sling code. The assumption was that a computer would just "speak code" like some kind of native tongue, but instead it just understands human language and associates that with code, and relies upon things like compilers and tests to see if it's right. Just like humans.
Predictably, now that it's actually happening, engineers are worried. As they should be, but I think it's a short-term worry. The job is changing, productivity is leaping, but its still a world where computers don't need to do stuff, humans do, so humans will be making it happen one way or another.
One set goes into haxe files and I use reflaxe macros to write tiny compilers that generate docs, clients, servers, cli's, test cases, serializers and deserializers, etc in whatever language is appropriate. That leaves gaps, which the AI can then fill in.
So I iterate on the haxe stuff. If the AI is struggling to "draw the rest of the owl," I change the source of truth until it has enough guidance re: type related errors, failing tests, and documentation. As requirements change, these things change.
As for the rest of the owl, it's disposable. Every few months I'll delete it and have an AI rewrite it from scratch (now with a smarter model and better docs and API specs and tests to guide it). This keeps the cruft from accumulating while preserving human contact with the code.
How can we prevent and manage the mess if we're not actively working in the codebase though? (Sometimes, I'll get a 'feel' for when something needs changed or will become unmaintainable, but that required consistently interacting with the thing)
Currently, we look at code once during the PR and then we never touch it again until it comes up in the PR.
I'm still handling a few tickets a week without AI, because sometimes it's faster to make a 2 line fix than to write + review a prompt, but increasingly it's just to make sure I still 'got it'.
2) not even sure what you mean by this
3) probably shouldn't admit that. It implies the reviewer has a less than average understanding of the code they're reviewing.
4) preventing and managing vibe code devolving into a pile of slop requires programmers not use AI. 80% accuracy repeated in more and more layers === more and more failures. In other words, the skill required is exactly the skill of being a good programmer without AI.
5) e) no AI all the time or only use AI as search. You're almost there with a and b. With c, it just doesn't understand well enough to "quiz you". With d, how are you going to know if the docs are correct if you aren't reading the slop?
Meanwhile, the people who work on actual products that matter have a valid criticism that can’t be dismissed with “some humans don’t even manage to do that.”
[1] There is probably a more fitting word, I am just reusing incompetence here, but that is not a really fitting description, I think. I would maybe say carelessness or something like that, but a single word is not going to capture the issue accurately.
LLMs commit crap, and read the "pattern" back, and consider it as gospel and repeat it all across the code base...
Without domain knowledge, the dev will miss critical simplifications. That is one way the code base accrue complexity.
The Vespa popularized by Hollywood's Roman Holiday propped up a post-War Italy.
Ping-Pong Diplomacy of the 70s gifted China its electronics manufacturing (think: PONG) turning it from a 3rd world backwater to what it is today. The exchange was in removing the 'Gang of Four' and the drugs associated with it.
---
The US was put into a privileged position as reserve currency of the WORLD in Bretton Woods. This is with the understanding/responsibility that the US would act as World Police and prop up foreign economies.
That is the system that is ending.
Even with how long Russia has been an antagonist, they are still seen as a “wayward brother.”
The passage criticizing the "invention of writing" is a quote from a legend where a Pharaoh passes negative judgement to a god, by the way, who had invented writing in the story.
Ironically, the dialogue in question, Phaedrus, is about the dangers of misreading text. It discusses writing political speeches not writing per se.
An AI that knows how to keep the documentation accurate and up to date, and does it by default, would, all other things equal, rot your codebase less. An AI that changes the code without checking whether it obsoleted a bunch of examples in the docs would rot your codebase more.
While I think that you can reduce "AI-induced code rot" with good prompting and steering, you could also make headway against it at model level, by making the AI "well-behaved" by default.
> "the scaffolding produced by the team" also includes the scaffolding produced by past AIs.
> "the scaffolding produced by the team" also includes the scaffolding produced by past engineers.
Better yet, extract docs from code comments. Even better when the docs are spatially co-located and line of sight as the agent crawls through code.
After a certain point, people would be forced to refactor, because they find themselves unable to handle the complexity.
With LLMs, there is no such friction. So the complexity get piled upon complexity in the form of a million best practices that is indiscriminately followed...
> After a certain point, people would be forced to refactor, because they find themselves unable to handle the complexity.
What you say here is not always the case.
Tooling is a high-skill trade. America used to be amazing at tooling. What's more, tooling isn't super cost sensitive because one tool can make thousands of parts.
When outsourcing to China began the tools would be made in the USA and shipped to China for the low-skill work. But over time those Chinese manufacturers figured out the tooling. What's more they realized that controlling the tooling would let them control the whole process.
They started doing things like, for instance, including the tooling in the price of the product so you don't even see it, if you use their tools. Just send them the cad file and they'll do the rest. So American tool making went away. But this was a conscious decision on the part of the Chinese manufacturer and an unconscious decision on the part of American importers who didn't really value their in-house expertise.
I didn't miss it.
> The problem was that substantial subcomponent or process vendors only exist in china.
He found vendors in USA. They didn't call him back.
USA has manufacturing. But Americans want to drive cars and own their own home. In order to achieve that American manufacturers need high volume or high margin - better BBQ scrubber isn't either of those.
> What's more, tooling isn't super cost sensitive because one tool can make thousands of parts.
Tool making is highly sensitive to labor cost. Every article is a one-off. It can't be automated, often not even duplicated. There are tool and die makers in the USA but if you want to make consumer products you can't afford American tool and die because American tool and die makers want to drive cars and own their own homes. Chinese tool and die makers don't have the same expectations.
> So American tool making went away.
It didn't go away. You just don't know about it.
Is AI going somewhere?
You can split a small program into piece A, B, C All of them look correct on their own. But they duplicate something in 3 different ways and person/agent who can "see" all of them can see the duplication and refactor.
Current model context is simply not enough for large projects.
Same problem for letting AI review code. A PR might look correct on its own and be small enough to fit into context. But somebody who has access to the whole code of the project again sees the duplication.
I am an OSS developer and when reviewing PRs I actually look at how the same problem was solved in other popular OSS projects. No AI can check this today because there is simply not enough context.
Basically if we had unlimited context what you said might be true. But context size is limited today.
Yes a plugin system is great, but it only works if that plugin API/interface it designed correctly and gives plugins what they need while still enforcing good practices.
But somebody needs to design a plugin system that does this first. And designing a plugin system (for large projects) brings us back to square 1 :-) (that you need a large enough context to see what the code does in order to anticipate plugin needs).
Some other ideas
1) Enforce architecture decisions (see archunit). But somebody needs to write them down first.
2) Check that tests actually break if the code that accompanies them is removed (several LLMs/agents today create tests that don't actually test the code they "guard against)
3) Automated performance testing. An LLM/agent might create a change that is "correct" but increases latency for 3x (best case) and 20x (worst case)
The hardest part that I see no solution for today is to understand when a change breaks backwards compatibility. LLMs/agents are trigger-happy and will happily refactor/remove stuff without any care about who is using that.I don't have a proposal for that, but the problem is there and is not covered by 12-factor config.
Free trade / globalism was the larger "true" move, and yes getting away from unions and high wages to maximize profits was a big part.
Corporations made a change from valuing stakeholder value (employees, communities, customers, suppliers, the nation) to pure shareholder value (profits over everything else, despite the long term result that profits by any and all means hurts all stakeholders and eventually can cannibalize the company unless you have monopolistic moats).
Was the New Yorker crowd clinking glasses together talking about how good it was to get the dirty steel mills out of the country? Sure maybe but this argument never resonated with anyone on the ground.
Were companies actively moving factories offshore so they could be dirtier because they didn't have to comply with American environmental laws? Absolutely.
I definitely wouldn't want AI to be autonomously using that as a guide without doing some fairly serious internal A/B testing first. Kind of like for cyclomatic complexity, it's just too easy to find ways to maliciously comply. And if that's what you ask AI to do then that's likely what you're going to get.
I like this framing. Humans invented software (and engineering in general) as a means to solve problems with methods that work best for us. There may be entirely different, and parallel, problem-solving methodologies outside of human best-practices.
So are you agreeing with me or not? Because it's not black and white. Having a few non-deskilled developers around who do the equivalent of "compete in mental calculations" is the same as having none at all.
It's sort of like the retort "AI won't take all the jobs from humans, some will be left [at the very top and very bottom]." Even if true, fat lot of good it does most people who would be unemployed in that scenario.
Also, if you get widespread deskilling, but massive increases in code production due to AI, you're probably still going to get "organizations falling into the trap" like the OP describes and the remaining skilled people getting burned out trying to hold it all together. Modern American business culture (in aggregate) is incapable of learning to not burn people out until everyone is already burned out, only then will it pay attention to the problem (and then probably forget what they learned and start repeat it in 10 years).
You make it A bit black or white.
Llm will not take jobs, at worst it will shift jobs.
If we will allow current llm to develop code without inspecting it, we will still want to provide direction and expect certain quality level. So in “worst” case you will need at least design/QA people.
If code will be free, and it will increase 1000x, you probably will need 100x design/QA people. (Unit tests written by same llm, if not reviewed are worthless as assurance). I’m absolutely certain that humans will have plenty of software related jobs.
As to programmer deskilling, another parallel: lot of programmers have very little knowledge of how to deal with databases correctly/efficiently, because they are somewhat lazy and good tooling/orms allow to get away with it, but there are also plenty of devs who do know how db works and how to design a db schema for their app access as it improves overall system performance and correctness. Same with LLM case, having knowledge of what happens in code, gives an edge, and competitive/curious/overly-responsible people will continue reading things in depth (llms also make learning somewhat easier). And its definitely not 1%.
Furthermore, llm prices are subsidised a lot now, so it’s yet to be seen if llms are economically viable as vibe coding agents on a large scale.
All will be ok, don’t worry too much.
feels like pure copium to believe that won't change rapidly.
I have no data for neither, so I’ll wait for numbers.
Will LLMs stay being useful? Definitely. Will in 10 years touching/changing code be somewhat niche, like ASM development is now? Not clear yet.
And optimisations are not a given, a lot of low hanging fruit are already taken, because it actually saves lots of money already now.
Try using an LLM to rewrite an LLM output without the slop (vs asking for no slop to begin with) or sandboxed subagents that critique a parent's draft.
There is absolutely a step-function improvement in quality but: 1) not everyone wants to explode their cost by adding extra calls 2) this can't just be "trained in" to a system as obviously they have attempted this but the technique still provides an uplift.
I guarantee you will pass the gate, and the code will still be dumb, except now you'll spend 10x the tokens.
You might write good, maintainable code, but they will prefer the slop generator who delivers quicker.
It's not that hard: treat people with dignity and take their contributions seriously, not as a disposable meat mass. In fact, not only will this improve code quality, it's likely to improve employee retention too.
The most passionate people with the best reputation will suceed, they will continue to be in the most demand and will be the most valuable, like a swiss watchmaker.
Sorry LLMs arent replacing anyone who's got strong skills and cultivates them. You're so wrong here. These more passionate builders will however replace the lazy people who offload all their skill and brain capability to llms, and they'll use AI to help them.
Being lazy and letting llms do everything for you is not a good strategy.
I agree that not every class of software or problem or this or that is going to be decimated/revolutionized whatever, but to not see any improvement since 2025 just sounds like you're not looking.
In general, I believe learning usually needs to be driven by the learner.
- ignore that it is annoying to some. (To certain degree, it is unavoidable and it is ok)
- reserve 1 hour meeting every month(week?) to demo/review/present.
- get buy-in and support words from manager
I do think this gets to the heart of the matter. I think many programmers have missed the forest for the trees on why things like data structure and code complexity matter. They do matter, but they don't matter in and of themselves. They matter because they are the best techniques we have for making software that is of high quality (the software, that is, not the code) and which remains so over time, while continuing to be developed and adapted.
I strongly believe that it is now much easier to create software that is of high quality and adaptability, orthogonally to the data structure and code complexity concerns. Those concerns remain relevant, but it's a mistake to think of them as the primary thing rather than things that support the primary thing
I don't disagree with you, but now what? Seriously - if we accept humans are just generally losing to AI now, what's the right thing for us to do now?
I dunno. Lobotomy? Heroic amounts of debauchery?
Any suggestions?
When I was in college or going to parties with New Yorker Reader types I'd try to argue against globalization and they would start smugly dropping their theories on me. I was too young and naive to refute them in any kind of convincing way, but my life experience told me that it was wrong.
But it was pervasive in the zeitgeist. Basically anyone trying to argue against it was backwards and stupid, or shrill if they were lefty.
It was so very weird. Even in Ireland (which was the target of the first wave of US outsourcing), people seemed to believe that everyone could be "knowledge workers" and that industry was unimportant.
I still don't really get why everyone believed this, but they did. Something something Upton Sinclair I guess.
However I'm not sure it could really be stopped or whether it was inevitable from containerization. Can you really prevent people from getting cheaper products forever?
What technology was invented in 2000 that allowed everything to go to China? No technology. That was the year the US adopted Permanent Normalized Trade Relations with China. It was a government policy change!
By the way the US already had trade relations with China. All PNTR did was promise not to change the policy in the future. That was the starting gun that signaled to all American companies that they should start investing in production in China.
So would globalization happen on some scale just because of the march of technology? Sure. But the way it happened, the speed at which it happened and the extent to which it happened, were the result of policy choices.
Do you even want cheap products? We're all trapped in low-quality-product hell together, and some of us have realized we'd be less frustrated and less poor with something at least not-quite-so-low-quality.
If people could have realized that sooner, and if there had been some vision or political will at the top, stopping this would've been a matter of very boring policy decisions. It wasn't inevitable. Containerization might have made it easier, but it didn't make it irresistible.
Even back then people on the left had a problem with the term globalization. On the left it was wildly recognized that the term came from the right to decorate global capitalism and (what was then called) Neo-colonialism in a more palatable light for liberals. In Iceland we used to make fun of it and said it should instead be called Americanization.
Right-wing anti-globalization is basically just hating foreigners (except white Americans), and supporting border fascism. Interestingly the right wing types in Europe love American culture, and have no problem with America spreading their capitalism and Neo-liberalism world wide (they only hate it when the EU does it).
Yes, economists famously do, and they also lack intelligence and knowledge given that their theories keep being proven wrong by reality but they never update them.
A trained economist either sells out to the Overlords or he gets a job as a barkeep. Or he ends up as some crank in a half-forgotten nonprofit somewhere. There's no middle ground.
Human intelligence is a remarkable adaptation, but "tendency towards delusion" might be the decidedly maladaptive trait that comes with it.
It's like saying that the Titanic was at risk of sinking after it hit the iceberg.
> A trained economist either sells out to the Overlords or he gets a job as a barkeep.
That's what training is for.
> Human intelligence is a remarkable adaptation, but "tendency towards delusion" might be the decidedly maladaptive trait that comes with it.
I'd say the reason is more external than internal - personal interest is a mind bender.
Could you elaborate on this? It sounds like something any anthropologist would laugh at; there's always an oral culture.
When he described those limits, he always frustrated computer scientists and analytic philosophers because he spoke in a kind of informal philosophical vocabulary and didn't really formalize his ideas. So he would say computers didn't have things like "tacit knowledge" or "insight" and he railed against "symbol manipulation". Famously he declared chess would never surpass human expert play. He wrote a book called "What Computers Can't Do" and another called "What Computer's Still Can't Do".
I personally think his argument was laughably wrong if well intentioned. But some people think it was respectable. I think in the present day, he's often rehabilitated with a kind of apologetic reinterpretation, such that things like transformers, weights, vectors, etc were what he really meant all along.
I think he was not wrong that some higher layer of sophistication would prove to be necessary, but he was wrong, I think definitively, to think that "symbol manipulation" of computers was a kind of category error. Even today's best models are still running on logic gates over 1's and 0's, and it was his failure of imagination to doubt that those could be the conceptual bedrock for AI, tacit knowledge and all.
He passed away in 2017, which is too bad because I would have loved to have seen his interpretation of things like GPT-6 Astra.
> think that "symbol manipulation" of computers was a kind of category error
This is where we do drift off into the semantic bog. If there is tacit knowledge in an LLM, then where is it? It must be in the weights, and it must have somehow come from the training data. Therefore the weights represent "compressed" knowledge. Is that then not "tacit" since it's explicitly encoded?
He never made such a claim. In the introduction to the 1972 print of this book he discusses the forecasts from Turing to his time of computers' abilities to play chess and the then state of the art. He criticizes the early optimism in 1950s mentioning that in 1957 H. Simon though in 10 years computers would excel in chess.
Dreyfus goes on to discuss the history of forecasts and progress in computer chess in a nuanced and highly informative analysis.
Whatever your opinion of his work I don't think it's fair to say his arguments are "laughably wrong" at any turn. I haven't read his books in detail but from what I know he made great contributions to the dialogue about technology and I don't know any instance of his making crass predictions or anything that he wrote that could be labelled "laughable".
This is probably true.
Many artisan fabric techniques were lost when industrial looms displaced those jobs. But heavy industry enabled cloth to be manufactured at a rate that people were free to spend their time and money on other priorities - and now (centuries later) the society is in a sufficiently advanced stage of development that the old techniques are being rediscovered.
Perhaps the example underscores the importance of thoughtful preservation of insitutional knowledge.
Values are 2021 because that's the most recent available for US at the same source, to keep consistent.
World Manufacturing output: 16.16 trillion https://data.worldbank.org/indicator/NV.IND.MANF.CD
US Manufacturing output: 2.5 trillion (15.47% of world) https://data.worldbank.org/indicator/NV.IND.MANF.CD?location...
China Manufacturing output: 4.85 trillion (30.01% of world) https://data.worldbank.org/indicator/NV.IND.MANF.CD?location...
Whatever analysis you make, let's use the correct numbers, from which you were off by about 1/5th. Should you think that is a negligible margin, you'd have to consider the US participation in global manufacturing output as similarly significant.
I'm sure fresh numbers are available, they're just unpublished.
We could do some educated guessing here - in the last 5 years China's economy grew at the rates of 8.6%, 3%, 5.4%, 5%, 5%, the US grew at 6.5%, 2.5%, 2.9%, 2.8%, 2.2%. We can extrapolate the manufacturing numbers based on that, which would give some advantage to the US because the US manufacturing lags the average, while the Chinese leads it.
The math gives us the current values for
World: $19.4 T, (%6.5,%3.4,3x%2.9)
China: $6.3 T - 32.5%,
US: $2.9 T - 15%.
Four. 2021 vs 2025. Unless you have numbers from 2026 from the future.
> and you’re going to hassle me about not having accurate figures?
Merely pointing out. The subjective interpretation of the experience is entirely on you.
> What the fuck.
Get more recent numbers that show I'm wrong and your 3 trillion & 1/5th of world total is right then.
Hint: not here. Even basic machining is cooked here. Go on Xometry and compare the pricing of any simple design made in USA vs. China and you’ll see, we can’t competitively do the basics anymore (you conflate basic with crap).
Europe owned high end quality, especially Germany.
Most Radar technology was invented in the UK and then sent to the USA where American engineers would redesign it for mass production. You'd have a magnetron that the British machined at great expense and the Americans would redesign it so it could be stamped out by the thousands.
Germans would look at the wrecks of bombers they shot down. Early on they'd look at a B17 and say "this thing is crude" but by 1944 they'd look at a late model bomber and say "we don't even know how they made some of this stuff".
People have it entirely backwards. They think if you can do high end manufacturing the low end is easy. In fact if you can do mass production of low end parts with consistent quality then high end stuff is easy.
So who's going to buy the low-end stuff at high-end prices?
You can try tariffs and the like (as we're foolishly doing right now) - that usually ends up having the opposite intended effect. You make everything more expensive domestically, including manufacturing (b/c your inputs got more expensive), and hurt our ability to export, even the things we did well.
Meanwhile, the rest of the world unencumbered with protectionism reaps the benefits of free trade and out competes us.
All this eventually leads to a loss of manufacturing jobs and output and manufacturers that can only sell to the domestic market (instead of the whole world). The whole sector starts to shrink. So that's no good.
We're not the largest in absolute output, but we are literally the best manufacturer in the world, and we are the 2nd largest.
We manufacture lots of stuff, often the most state of the art and difficult things to manufacture. We're very good at it.
And we've sat around close to full employment for so long, the only way to convert more of the economy to manufacturing is to either import workers, cannibalize other sectors of the economy, or automate even more.
Yes, a larger share of the economy is services, but is there some objective optimal ratio of services to manufacturing we should be shooting for? If so, what is it? No one ever says.
Sending emails from an ergonomic chair for eight* hours a day and having to be civil to female coworkers and polite to your boss inherently degrades the male spirit in a way that permanently damaging and poisoning your body with 12-hour factory shifts with a foreman yelling at you doesn't. This is so self-evident and obvious that nobody even bothers to state it, it's just an assumed undercurrent in discussions of "bullshit jobs" etc.
The fact that leadership is focused on extracting profits out of the enterprise is also not a show of strength. It's a demonstration that they're trying to drain whatever value is left in it.
This is pretty much true of any of the American "greats:" Boeing, Intel, GE, GM, Corning. They're producing (or have recently been producing) great profits because they're eating their own seed corn. Nobody expects them to still be the businesses they currently are in another few decades.
We can make planes and engines because we kept the expertise, and we can make rockets because of herculean efforts of a few people. This simply isn't good enough, we need to make creating new physical things as easy as possible.
Massive numbers of people on production lines is not coming back. The rest of the economy is too well paid for that and automation too advanced. VW will retain car manufacturing in Europe by automating away its workforce, at the cost of high redundancies.
When people think of manufacturing they generally think of going from basic materials to something useful, rather than 'just' assembling quite advanced parts. Like if I go buy some parts and 'build' a PC that'd technically be manufacturing, and quite high value manufacturing if I was able to sell it at a good markup.
[1] - https://www.visualcapitalist.com/cp/boeing-737-global-supply...
Americans cars are falling behind, they rely heavily on American cultural exports which relies on the world not thinking Americas are twats because the voted for Trump. Germany alone is over twice the size of America in terms of car exports. Including domestic destinations then china is three times the size of America.
America planes fall out of the sky far too often as Boeing pivoted from engineering to politics
Musk does good rockets. Not America. China is catching up and lost to overtake unless starship proves itself; and if it does China will copy that too.
Chinese planes are increasing very year, comac now about 15% of traffic, and heading towards about 20% in a decades time. and Boeing is about the same size as airbus at 40% each which will shrink as china catches up, won’t surprise me in 2050 is the largest provider of passenger planes is Chinese.
You literally are praising a system that condemned tens of millions of Americans to abject poverty because the rich wanted slightly better returns.
Americans are better off than they've ever been. The improvement has disproportionately gone to the wealthy, but even the poorest have seen gains, not losses.
I don't understand this kind of comment. It's like some sort of disaster fantasy. Like you need it to be a crapsack world full of slums, so you come up with absurd takes like American companies employing huge number of Americans not contributing to American prosperity, and just make up troubles.
Boeing builds their planes in America, F150s are largely built in Michigan, iPhones are manufactured in China, but California is much richer because of the taxes that Apple employees pay. Millions of Americans are in poverty according to the latest census, but I don't know how you establish cause for that poverty being "that rich people wanted higher returns".
This conflating US financializing manufacturing vs actual industrial capacity, i.e. the entire manufacturing by value / MVA "cope". US objectively doesn't make much relative to past and a lot of shit US makes, US doesn't actually make, others does the actual making while US financializes and capture via spreadsheet maxxing, hedonic adjustments etc, and do insert "I made this meme" on things US fundamentally did not "make"
Like US does make some legitimate frontier stuff in house - the reality is is in aggregate industrial base terms, US MAKES / FABRICATES very little vs pre industrial chain hollow out, US does import a lot of subcomponents that others make, integrates, or outsource actual MAKING abroad while capturing intangibles like IP/patent fees that get bundled up into value add stats, but when people talk about making they're talking about extraction to factory gate pipeline. Not US can sell a bunch of foreign made goods with some CONUS final assembly or IP accounting because muh smile curve that attributes disproportionate "value" to US "makers". Consider a US widget co imports $10 of components assembles and sells for $40, the statistically manufacturing value add is $30 even though US did very little making. Now add tariffs, regulatory capture etc and widget sells for $50 and that's $40 of MVA for basically doing minimal M. Said US widget factory makes 10 widgets and adds $400 to MVA. Meanwhile a PRC widget company makes 100 identical widgets at $2 each... but their MVA is only $200, but they did literally all the making. These two industrial bases are not the same. Then a few years later, US BLS/BEA applies a statistical adjustment because widget X is slightly more performant... (prevalent in tech), so that widget is now listed as $100 equivalent utility, so hey manufacturing expanded (it didn't).
SpaceX... doesn't make that much... they make a few thing that are reusable and due to techstack can lift a lot, i.e. operational improvement (not discounting how game changing resuable is). But compare to PRC, who made like 2x-3x more first stages, i.e. their output is significantly higher, which is to say SpaceX is "making" at about American scale. For reference Boeing like ~30% domestic content, US auto ~50%. Reality is the ONLY thing US is REALLY good at making is refined fossil, agri, wood products etc. Almost everything else US very good at making money, but not the stuff.
TLDR US got much better at MAKING money from other people MAKING things. The other caveat is regulatory capture = US grossly overpay for mediocre shit, a lot of stuff that makes in US are just... not good, i.e. vehicles, but people trapped paying domestic premium which inflates value add but not actual relevant when people discuss US making things. US broadly makes a lot of mediocre overpriced things while population stuck shopping in company store for large items, that looks good on value add / productivity stats... but it's not reflection of industrial capacity which is what people think about in terms of real manufacturing.
With that in mind, writing bad code is bad.
It does by, attributing a change in quality to a "testing and validation problem".
If there is a change in programming methodology, as far as we know no change in testing and validation methodology and at the same time an increase in the production of bugs, it's not reasonable to attribute that increased error rate to testing and validation getting worse.
It's like adding spinning saw blades to the fronts of cars and then attributing the increased fatality rates to pedestrians not wearing safety reflectors.
They might take six courses at a time.
So you are saying that you learned more than 400 times as much content as a university course?
I'm sorry but this just sounds hilarious And unlikely to most people, and it comes off a bit crazy sounding... For example, do you actually think you could even pass a single university course exam on introductory relational algebra without AI I assistance at all?
I think what is more likely is that you feel confident trying to solve problems with AI's help.
That is not learning.
To learn something, it has to be in your brain, not the AI.
Learning is not the same as becoming capable.
There's an ocean of difference between those two
I apologize for being blunt, and I don't mean to sound argumentative here, I'm just trying to give you an outsider's perspective:
You should not tell people you learned more in 2 months than in 20 years.
That's all I meant.
It makes you sound crazy, and its also wrong.
Maybe you built more or you feel more capable than you have felt for 20 years, but you surely did not learn more.
I don't need to hear anything from you about how the capability of the average developer has changed. I'm well aware of that already.
But you did not LEARN Relational Algebra. You learned OF relational algebra. And there's a pretty stark difference there.
It's fairly standard for undergrad CS programs and most textbooks include at least a section on relational algebra.
Developers, however, are still responsible for the code! We must review the AI....all 80k lines of code it generated yesterday. If we don't then we are at fault. And we must go full throttle of course. So ....not be picky and retrograde about accepting what is generated.....
IOW we know who is going to get screwed and it isn't them.
Trends over time will drive more observable changes. If a whole generation of programmers picks up bad habits that their managers don't care about (think very junior), that will take some time to play out. It's like children's literacy. You don't notice overnight, but a decade of neglect and you have a reading problem in kids.
To be as frank as possible--you "understand" these topics in the same way someone who watches pop-science videos about astronomy "understands" astronomy. That is to say, you have a surface level understanding at best.
This is the sort of behavior people have been talking about over the last few years. People confuse what the AI "understands" with what they understand, and it makes them say delusional stuff like "I learned more in the last 2 months than in 20 years."
Yes, the also called lots of actual crazy people crazy. In fact the probability of actually being crazy, given people are calling you crazy, is very high.
You don't go to a magazine website for the current common meaning or understanding of a word, you go to the dictionary And in a dictionary you'll read that a luddite is someone who is opposed to new technology, automation, or industrial change.
There are countless examples where people resist new technology for various reasons other than "using low paid inexperienced grunt workers to displace well paid experts". And this is what we call luddites, not the historical original definition of the word.
No, the LLM doesn’t know our product strategy and why certain things matter and certain things don’t. That’s very much what I get paid to do. There’s not even agreement within our team about what path we should take through the domain-product space, there’s no chance in hell that the LLM will choose a profitable random walk through that domain-product space.
If it were able to do that, then AGI would have already been achieved and we’re only compute power away from OpenAI or Anthropic making the marginal utility of any piece of code $0.
LLMs are not a "random walk", they take in information and they explore the space according to the way they've been instructed.
Hmm, I have deep concerns about what and who is going to cease to exist.
But the point OP was trying to make is that because LLM harnesses output code that is at least better than the worst, say, 20% of developers then we should be fine with it. Meanwhile, I’ve worked in shops where you have to be way above the worst 20% in order to keep your job or even be hired in the first place. And it isn’t some crazy lose-sleep-over-it, stressful requirement. It’s just that literally if you’re not good enough your work will be identified as a liability and you’ll be let go. It’s only happened to 3 people fwiw.
I think what it comes down to, is trying to turn the specialness of human intelligence into undefinable magic, essentially playing god of the gaps with the concept of human insight. So by design, it has to be something that can't be amenable to any formal representation like weights.
In your analogy, what I would say is that the saw blades on the front of cars situation exposed a weakness in the regulatory regime for what can be put on the front of cars, which should be improved.
I remind you that the belief you are defending is that an increase in the number of bugs produced per some unit of implementation "implies that testing and validation has gotten worse", not the general idea that you can or should improve software quality by improving testing and validation.
If we adopt the realistic perspective that the testing and validation process can only catch some fraction of bugs, then an increase in the number of bugs produced in the implementation stage is bad irrespective of what that fraction is. You can improve the testing and validation process so as to minimize the fraction of bugs that get past QA, but when you multiply that by the number of bugs produced in implementation you will be worse off if you produce 7 bugs per month during implementation than if you produce 5 bugs, regardless of whether you can also improve the testing and validation process. You will on average, over time, have fewer bugs in production if you produce fewer bugs during implementation given any testing and validation process that isn't completely fool proof.
> Isn't this a testing and validation problem?
One way for it to be that kind of problem is by exposing weakness in the existing approach. Another way would be if the approach was worsened. But both things fit my original contention.
I don't disagree with your concluding paragraph at all. But what I think is that there is no reason that implementing with llm based tooling implies an increased defect rate. Rather, I think it implies that there are existing or new holes in the quality process. That doesn't imply that I think a defect rate of zero is possible. But I think holding the defect rate steady is possible.
He thought that the complexity of chess rendered it solvable in principle but "uncomputable" in practice. You're right that he was speaking to the times he was familiar with, and what he meant by "impossible in practice" was something like letting a 1Mhz computer explore all the possible chess moves from now until the heat death of the universe. Relying on that to insist that computers defeating humans in his lifetime was consistent with what he envisioned stretches past charitability and into sophistry.
And I don't think you can extend him that charity without doing the same in the other direction, which would also collapse his basic thesis. Dreyfus was disproportionately preoccupied with retelling the failures of the 1950s over and over again using them to represent the whole of computing while the world moved on, and extending the same charitable repairs in favor of AI research make it something less easily caricatured, and still based on the same logic gates and 0s and 1s he was criticizing.
If that's not enough, Dreyfus explicitly said that what was lacking in chess programs was (1) any practical ability to do the brute forcing needed, (2) any kind of nim-style logical shortcuts around brute forcing or (3) any kind of expert level heuristics because he categorically believed those simply weren't programmable. And he believed that those exhausted the options. [1]
There's no version of this that can be correct because even if you think he's right that chess engines got better by progressing to some different conceptual paradigm, that paradigm is still embodied in same logic gates and 1's and 0's that he thought only pertained to prior paradigms he was criticizing. He was wrong to assume such things as "heuristics" were outside of that scope.
1. https://repository.essex.ac.uk/42372/1/Martin%20and%20Willia...
I do object to calling his writing "laughable".
Thank you for the article, it seems quite interesting on skimming and I will save it for later.
I think that's true but it's a special case. AI is here to stay and with AI coding IS faster and quality is better than ever before. Ideally you want your luddite fired along with the slop generators and keep the ones who are using AI and taking their time to deliver a maintainable code.
Is this claim based on something?
I'm not against or "for" AI (whatever that means), I try to use it as effectively I can, but for me it's not at all obvious that quality is better than ever before.
Speed I can buy, especially in new projects and utilities, but quality? At least I haven't seen this in practice, if anything I'm just seeing more code, issues, PR's and pressure ==> more slop, more bugs, less quality.
You can always say "skill issue" and "process issue", but that's partly my point here, AI doesn't magically solve this.
Speaking for myself, based on my own personal experiences, quality is by far the bigger advantage of these tools. It has never ever been easier to write automated tests and to automate tedious manual validation. I'm running my code through like 10x more paces than I ever did before, because I can just say "hey try running this in these twenty different ways" (including with browser automation, if that's relevant), without needing to either do the tedious steps to run all that or to take the time to write a script to do it, and to compile and attach the findings to the PR. This saves me hours to days of work on validation, but the reality is that I just wouldn't have spent that time in the past, I just stopped at a lower bar for quality, because I couldn't justify the ROI for spending all that time on it. But now the ROI is huge, so it's a no brainer.
If people are not taking advantage of this, then yes, that is literally a skill issue.
Maybe it's true that lots of people aren't taking advantage of this and are shipping trash, but that's their own problem, and there have always been people who do the job poorly.
Localised quality is great; whole-program (even small ones) are not usually readable by me.
That's true, but it doesn't have to stay in this form.
> with AI coding IS faster and quality is better than ever before.
Citation needed, because the last study I read about was painting a completely different picture about code quality. Also, just because the AI pulling and remixing code from a known repository with high quality doesn't mean your code will be at the same quality automatically. Passing tests is not enough.
> Ideally you want your luddite fired along with the slop generators.
The thing is it's not possible to see who generates slop and who generates code, and if you fire the only people who knows about the codebase intimately, you'll be on a very exciting, possibly fatal ride. I don't recommend this. AI doesn't know your history and trade-offs. These guys do, and can guide you to clear.
AI can't.
Believing that AI will create bug-free code from start is believing that Rust is the silver bullet.
There are no silver bullets.
But I totally agree with you about the measurement problem. I think it's a very difficult time to be a hiring and firing manager.
This is exactly what I said in my top comment. This is a huge problem.
Ive seen soooo many people burnt out, or "ive given years to the company and i got hit with layoffs", or "$200 software would have saved $1000000 when I brought it up to them". And companies will throw you away the MOMENT your usefulness is gone, even if just perceived. So, use them just as much as they use you.
And that idea of slacker is ALSO a way to generate more money for you, by slyly withholding or slowing work. I didnt get my paltry 3% last year. Inflation up 15% or whatever stupid number. But I can control how much work I do, so my effective wage/hour stays with inflation.
Save your caring for your personal projects, nonprofits you help at, your and family/friends labor you help with.
Go fast and break things has been a mantra for how long?
I think a lot of the laments about "good" code are really about "ownership" - and as someone who spent most of my working career in OTHER peoples code bases I have seen some things. There are a lot of you who think that your code bases are "great" when they are NOT. Personal understanding is not a good measure of quality.
The increased cadence from AI is just speed running to the legacy code base.
The answer: express the concern, and reiterate it after every issue that arises because of increased complexity. Start building a plan on how to "unravel" the mess, how to migrate things in place, how to start drawing boundaries in your systems. The system is designed to reward heroes who fix problems - you want to be super man who stops the bridge from falling apart, not the engineer who pushes the costly fixes it before it does.
Practically since eternity, but just as we learnt to manage current rate of "fast" and "breakage", somebody attached a solid booster behind us. So we're trying to understand what happened and what's happening and what will happen.
> I think a lot of the laments about "good" code are really about "ownership"
People owning what they did, have responsibility and initiative about doing better is always a good thing, yes.
> and as someone who spent most of my working career in OTHER peoples code bases I have seen some things.
I can understand that, I'm sorry you had to go through this.
> There are a lot of you who think that your code bases are "great" when they are NOT. Personal understanding is not a good measure of quality.
My codebases are as great as my knowledge. I love when someone reads my code and points where I f'ed up. I also love to show what I did has achieved something I was aiming for and discuss how to achieve it betterer.
> The system is designed to reward heroes who fix problems...
And this is the problem. Because I work silently and diligently build something looking unimpressive while working like an atomic clock without any problems.
World total: 17.6 trillion (https://data.worldbank.org/indicator/NV.IND.MANF.CD?location...) US: 2.961 trillion, 16.82% of world China: 4.82 trillion, 27.38% of world total
So you were very much closer to the 3 trillion figure (again, 2026 annualized data is not current, it's forecast). Still quite off from the 1/5th of total. About the same as the EU - 3.03 trillions for 2025 (https://www.macrotrends.net/global-metrics/countries/euu/eur...)
In terms of participation in world total the US has come down from 22.44% in 1997 to 16.82% in 2025 losing 5.38 percentage points or a loss of over 1/5th in less than 30 years.
I'd say there is some justification to claim a decline in US manufacturing.
Again thank you for engaging productively and providing up to date data. I feel like I should offer praise as I chastised you for cursing at me for using data 4 years out of date.
There’s a decline in the relative share, but that’s down to the rest of the world growing. Adjusted for inflation, 1997 was slightly below the current figure. That’s nothing to brag about, but it’s a far cry from the “we don’t make anything anymore” narrative I see so much.
Where it has declined substantially is employment. That peaked at almost 20 million at the end of the 70s and is now under 13 million.
Thats a really bad website too btw, design and functionality. Maybe use your brain before you lose it.
For a 100x engineer with 40 yrs exp that you claim to have, you make some real basic high schooler projects lol.
And AI does it all for me while I sip coffee and play mahjong (built by AI of course)
Probably a vast majority of software written never actually gets interacted with by a user. A vast majority of VC-funded software probably never gets interacted with by a user.
Feels pretty great to work on a product that actually has a user, and that user isn’t actually the product (and that user isn’t being manipulated or exploited). Tiny, tiny minority of the software industry I work in.
If you don’t think about it as a per-software criteria, but per-feature criteria, the vast majority of software features developed are probably completely ignored or even untouched compared to the “critical few”.
Very, very long tail distribution. The most executed 100 lines of code (maybe some Java class init snippet, or some Linux kernel snippet) is probably executed more per day than the per-day bottom 80% of all code combined. Maybe the bottom 99.9% of all code combined.
I am equating the nerve that develops in people that get lost bikeshedding (wasting time on inconsequential parts of the problem) with fighting an llm on inconsequential implementation details.
We most certainly agree: what matters should always be the actual requirements (functional, security, performance, etc.) You can't bikeshed an important topic. Everything else is implementers decision. In my experience an experienced engineer understands the difference and trusts implementers to make the decisions that they do own.
You use the intentionally vague word "implementers" to abstract whether you're delegating to a human or to an AI. But the key point is that these are not the same thing. If I'm delegating to a person, that person is the "implementer". If I'm using an AI to generate an implementation, I am still the "implementer", it is merely a computer program working on my behalf.
This sort of thing takes time, and by the 00s, enough time (and a recent Hong Kong transfer that did not result in mass liquidation) passed, and foreign investors were gaining confidence.
The world, and especially China has agency of its own, it isn't sorely driven by whatever American policy is it's flavour of the week.
The point is that it wasn't technology it was policy, on the Chinese and American side, that drove the China Shock.
This was also what a lot of anti-globalization people were say at the time. The pro-globalization people were saying not only is it good but it's inevitable. There Is No Alternative.
The anti globalization people were saying if it's inevitable why does it need all these policy changes, trade agreements, and bigwig conferences?
China has to import much of their raw materials. Wood from SEA and Russia, oil from the middle east and Russia, even coal, ore and beef cattle from Australia! They don't have the water supply, forests, mines or oil that exist in North America.
There is no good reason for them to be the manufacturing center of the globe and there were rational reasons why the US was the center of manufacturing in the globe in the 20th century.
The reason they became the global center of manufacturing was policy choices in China and the US at the turn of this century, and the reason why they remain the center of manufacturing is they developed a massive amount of capital and know-how in an era where the USA was actively destroying its own.
This whole subthread is from like an alternate timeline where the world actually found out or agreed that globalization was bad and stopped or slowed it somehow? Did the US meaningfully take back manufacturing or something?
If globalization is not inevitable, what's the alternative?
No, but there definitely was a much slower gradual repression over a longer period, with a final round of protest repression: https://en.wikipedia.org/wiki/2019%E2%80%932020_Hong_Kong_pr...
> The world, and especially China has agency of its own, it isn't sorely driven by whatever American policy is it's flavour of the week
Amen. A two-pole model is simple and attractive but the rest of the world is also, you know, doing stuff and buying things. America can, for example, preserve its car industry by refusing to allow Chinese EVs, but will gradually discover it has become uncompetitive overseas.
Australia was never very interested in being a manufacturing power house, they simply don’t have the people for it anyways, and it’s easier to ship raw material than finished goods vast distances. Russia has the same problem in population so China is convenient for them. China would have happened even if the USA didn’t help it along in 80s-90s.
Presently there's a lot of yap how it didn't or wasn't the reason Australia lost its way to retain its manufacturing sector. Indirectly though it was bouncing around the walls of Govt and industry's board rooms and pushed decisions to reduce or discontinue ongoing training in regard to upskilling, or simply improving skill sets that might be needed in the unemployed working class.
However much of the shift way from general manufacturing was the inevitable result of the declaration, the inability for a small manufacturer in places like Australia to compete with cheap imports - I recall a relative back in 1979 - 80 having a long rant about a manufacturing company near him - the whole problem was the small dry cells the company purchased at quantity to install into an electronic device - the exact same branded dry cells were available to their oversea competition for next to nothing and could / did sell their version cheaper than the purchase price the company was paying for the batteries per unit.
The other part of the issue was those industries that opted to hang on - the bogey man here again was the thinking that it would be better / overall cheaper for manufacturing sector to be elsewhere overseas. Thus with this mindset setting fruit in the higher branches of the wealth market, there was not much help from Industry heads or Govt at the time to encourage or provide affordable means for whichever industry still hanging on, to modernise - textile being one that comes to mind. I actually had a strong belief (I knew no better from the propaganda being dribbled out,) 80s / early 90s that the reason the textile and clothing industry was falling off was overseas workers were paid a pittance, which made it hard for textile companies here to stay competitive - that was until someone who'd had travelled and had business interests though Asia and the US dispelled the usual propaganda I'd been suckling on, she corrected me on that it's not all sweat shops, and much of the textile base over in Asia had modernised quickly and been computerised for years.
[1] https://www.unido.org/sites/default/files/2012-10/Lima%20Dec... [pdf]
> The idea that the US lost its manufacturing is not based in reality.
But I disagree with your claim that people are right in being extremely dismissive of losses in US manufacturing.
>> Absolutely people were extremely dismissive to anyone saying that we're losing the ability to make things in this country!
Interpreting that as a complete loss of manufacture capacity I agree with you, with the qualification that concerns of relative decline were justified since there was a definite decline in aggregate.
I have to say that overall I don't think economies and citizens of these economies should prefer certain economic activities over others. In principle I think specialization is a good thing. On the other hand, I do think that the US has committed some strategy mistakes in terms of how much manufacturing activity was moved out of the country and how dependent it has come to be on foreign supplies of, for instance, rare earths and advanced IC.
But that analysis is predicated on US strategic goals and geopolitical intent. The coming impasse over Taiwan with China is almost completely self imposed due to the prioritization of short term corporate profit over strategic goals.
But that is a different conversation altogether. I think data from the past 30 years show that alarmism over de-industrialization was overblown as you said AND that there has been nonetheless a significant decline in the previously absolutely dominant position of the US economy in global manufacturing. If that is good or bad, to whom or to what ends, is way outside the scope of internet forum messages.
1. Caring about the company when you are a worker and not owner?
2. Companies will throw away/layoff people with no notice?
3. Work slowage (work-to-rule) as a counter to low/no pay raises in accordance to general inflation
4. Invest emotional and physical labor in ventures you gain completely out of
1. Caring about the company when you are a worker and not owner?
Yes and no. I don't care about the company. I do care about what I do. It's a self-respect matter. I do good work not because I'm a slave to company, but because of self respect. My deal is simple: "I'll do my best to produce the best artifact and push the company further as long as it doesn't conflict with my personal principles, you'll buy that time for that amount of money".
I have a simple, foundational rule: I'll sleep sound at night, and this rule is rooted in my ethics. So, I don't shortchange anyone, incl. my employer. If terms change between us, we will discuss, but this probability is not a reason to do shitty work (or optimize for money, or which sugarcoated absurdity others name this).
2. Companies will throw away/layoff people with no notice?
Yes, this is bad. This is life. It's not nice, fair or acceptable, but without unionization, you can't act against this. So, you either try to change this or you just accept it. Realities of work life is not a predicament to shortchange your employer again.
This is as absurd as saying "I'll die anyway, why do all these things? I can just die on-demand".
Meaningless...
3. Work slowage (work-to-rule) as a counter to low/no pay raises in accordance to general inflation
We can accept that, but you all shall really unionize. It's not scary. Try organizing. It's a force multiplier.
4. Invest emotional and physical labor in ventures you gain completely out of
Everybody should have hobbies either productive or unproductive. I can't find the question.
There are companies where you can spend years doing as you describe. More and more, though, you’re competing with people who care even though they don’t own, put in the same effort YoY, and invest in their job. Companies love these employees.
So I guess it really depends on your values and what you want out of life. If you enjoy hobbies and time outside of work, sure find a job where you can coast. Don’t get frustrated when you get laid off just find another place to work. Etc.
Plenty of people want to grow within the industry and build a career, though. And many have what we call basic self-respect and care about how they are perceived.
AI maxxers know they're doing harm to themselves by overusing llms. The way they react so defensively when you point it out to them lets you know its real. Everyone wants to pretending like they have superpowers and are evolving into "100x" engineers, but in reality they're devolving.
AI can be a multiplier of both your intelligence and your stupidity, arrogance is a byproduct
> Ideally you want your luddite fired...
The luddite is the person holding everything together.
Like the office maid who looks like doing nothing but keeping morale high and everybody's sanity intact.
The answer to that is "Fuck you, no. I have worth, and you will respect it". Gilded Age paternalisms are not something that needs to be brought back into vogue unchallenged, especially when the intent is to keep the rabble quiet, and the checks rolling in and up.
I labeled the person who uses AI to generate code and uses their brain and wisdom about the system to refine that code as the luddite since they will work slower when compared to other "higher performers" who don't care about the code quality.
In my framing I'm aware that the person is not a luddite per-se, but will look like it since they will be slower while trying to create better code, albeit using AI in the process as well.
Citing myself:
> Any employee who cares about code quality will become a poor performer with a red luddite label because they dare to change what the AI has emitted for them.
I don't think companies are living and dying, by how much code they can produce. Even tech companies.
For some reason this reminds me A LOT of past discussions about microservices, most wonderful on paper and forever debated, but I've never seen it work out perfectly in practice, for me it's mostly been a cluster F in most companies that adopted them.
Currently I see AI similarly, in theory perfect, in practice I don't see the claimed effects. So yes, skill issue, but skills are relevant and your company probably can't hire a rockstar team (if that matters in the future).
But your comment on personal experiences was very good! Spot on, we are all biased, easy to forget. Thank you for that.
...or domain. You said "including with browser automation", so you do web or web-adjacent development.
Not all of us are doing that. What I work on doesn't have any UI or output besides a log file most of the time, but it connects to many places and does many things like an octopus, but nobody sees that, but feels that it's there because their environment keeps on working.
The octopus you just described sounds to me like an excellent example of what having the ability to more easily do tedious validation is most useful for. If you know that the "environment keeps on working", there must be some way for you to observe that fact. And if it is an octopus, it is likely difficult and/or to change the conditions and observe the correctness with respect to those changes. I find it so much easier to do this exact kind of thing now. Or, "easier" really isn't the right word. It's that the activation energy is low enough now that I'm able to do a lot of things up front that I used to rely on runtime monitoring to validate.
I guess YMMV, and it's not magic, but for me it totally changes the calculation on when it makes sense to automate something (like that chart from the old xkcd about how many times you'll do the thing and how long it takes to automate) in a way that means I'm doing a bunch of things that are useful for quality that just would never have passed the bar in the past.
I didn't. I made a guess. I might be wrong, that's OK. I love to be wrong, because I learn things by being wrong. Also no offense was intended, and I don't consider webdev inferior anything. What I tried to mean is, if AI has more training data for a domain, it does better. If you fire the same model on a niche domain, it falls flat.
> If you know that the "environment keeps on working", there must be some way for you to observe that fact.
Yes.
> And if it is an octopus, it is likely difficult and/or to change the conditions and observe the correctness with respect to those changes.
Nope. On the contrary, because there's so much innate knowledge that is required to know what to do, simulating in mind, deploying and testing on real world is much easier and faster than letting loose an ML model on it. You need real data, real data comes in slow, but you can catch problems early and easily.
Considering it's a niche area, AI also doesn't have much training on that domain, so it's doubly inapplicable for what we do.
> but for me it totally changes the calculation on when it makes sense to automate something ... (snipped for brevity)
It's great that if it works for you, but YMMV part is way more correct than people want to accept and want to learn. AI is a pneumatic hammer, but not everything is a nail which can be driven in with that.
When it works, it works. When it doesn't, well people still pretend it does or insists it shall. We must accept the limitations.
I understand the boundaries of ownership and responsibility. That’s why I can tell you if you are spending inordinate amounts of time correcting AI code then you’re doing something wrong. Either write the code yourself or reassess your ownership boundaries. You’re acting as a manager of a team of agents in an agentic coding paradigm. Managers don’t tell me how to write code.
AI slop can actually help with this, because it reduces the cost of replacing their enshittificated software.
I think this entire analogy about being a manager of a team of agents that is in vogue is completely misguided. Have I always been the manager of a team of bash scripts? No. These tools are way more capable, but they are still just tools that I'm using to do my own work, they are not people that I'm delegating responsibility to.
Why are we even arguing then? You and I agree. Did I ever say "don't share a single preference with the AI"? You set the guardrails and preferences and the AI follows them. This is how it's always worked so it's reasonable for me to assume that people "fighting the AI" have already done this and are being overly pedantic about the output. Otherwise it wouldn't be eating up inordinate amounts of time...
> Have I always been the manager of a team of bash scripts? No.
No. You're not even remotely close here. Let's revisit this once you've figure out how to have a team of bash scripts implement 100k lines of code and build entire systems in 2 weeks based on high level instructions and requirements shared in context and prompts. You have responsibility at a different level and scale in an AI native workflow.
No this is what you're not getting. It is "doing the things I would do to deploy and test in the real world, but faster and in the background while I do other things", it is not "letting loose an ML model on it". This is the new capability. If you have any process like "do {action}, wait until {something}, check {something}, determine if it matches expectation", it is now possible to run that loop way more times in way more variants without either spending the time on it synchronously oneself or writing a script to do it. (If you do that specific action loop often enough, it's probably worth writing the script anyway, but that's also much quicker to do now.)
The AI doesn't need training on the domain, it just needs to be told "these are the things I would do, please do them for me and report back".
I'm sympathetic to not everything being nail-like, but I really think you're leaving a lot of chips on the table if you can't imagine any of this kind of action-check-evaluate loop you have that you could offload.
Everything is already at the background. That thing doesn’t need a CI, not the classical or AI enabled kind.
Maybe I was not clear about that part of what I do. Three minutes of something not working correctly doesn’t burn our world down.
Obviously I have no idea what your work looks like! But what I'm saying is that time savings are not just time savings. There can be a point at which the time savings bring you under an "activation energy" such that it unlocks a new capability, not just a speedup. And some of those unlocked capabilities can be directed toward improving the quality of software. And I think that's awesome and useful, is my prevailing point here. I won't claim that it will usher in an industry wide improvement in quality or anything, but for me personally, I'm making better software more quickly now, and I'm very pleased that I can do that.