The code it generated was awful. The kind of garbage that people who don’t know any better would ship: it looked right and it worked. But it was instantly a maintenance dead end. But I had an effortless time converging on a design that I wouldn’t have been able to do on my own (I’m not a designer). And then I had a reference design and I manually implemented it with better code (the part I am good at).

Sure, Claude Code and Codex can write (most of) the code for me - but the amount of technical knowledge I need to decide what and how to build remains enormous.

As an example: I'm working on a system right now that works like Claude Artifacts, allowing custom HTML+JS apps to safely run in an iframe sandbox inside a larger application.

Just understanding why that's a useful thing that can be built requires deep knowledge of sandboxing, security threats, browser security models, and half a dozen different platform features that have been evolving over a couple of decades.

A vibe coded without that technical understanding would have zero chance of prompting such a thing into existence, no matter how much guidance the LLMs gave them.

It really saddens me to see some developers talk about literally quitting their careers over AI, right when the benefits of existing deep technical experience have never been more valuable.

There's an interesting repository with 63600 stars on GitHub (1). The developer of the repository is No 1 at the GitHub's trending contributors list (2). However, it seems like the application isn't what it's described to be (3), and the developers, on their end, are unable to clearly answer whether this is real or not, as it's just messy LLM output.

Proof that the suit alone doesn't make anyone Iron Man.

1. https://github.com/ruvnet/RuView

2. https://github.com/trending/developers?since=weekly

3. https://github.com/deletexiumu/wifi-densepose

It's a reducer of time.

For less experienced developers, it's an immediately reduction at the start of a project. But then they will almost certainly have problems later when their initial decisions come back to haunt them.

For senior devs, it's like having a junior or mid-level dev that will instantly do things within their capability, so long as it's explained to them well enough. This junior dev will do things fairly smartly, but any important decisions left to them will be wholly or subtly wrong. And the subtle ones are the worst ones, because they're so hard to detect.

But if that senior dev sets the guidelines well enough, and notices the problems, development is so, so, so much faster. It's wild.

Better headline: "Why AI Multiplies Developer Skills Rather Than Replacing Them"

What folks seem to avoid is that a Junior (in ANY subject) has the ability to LEARN so much faster with an AI research assistant, and that becoming an expert has accelerated for those with the personal stamina to dig deep (this as a requirement hasn't changed). I spend just as much time with my AI tooling asking questions as I do asking it to "build" or "fix" things. "How does this work?". "Can you suggest other tools?".

I think some people always think about AI as an input / output relationship, when a lot of the time, the fiddling in between, with or without AI was always the important part. Yes people will suck in the beginning, against they always did. I think the good folks though will suck for a MUCH shorter time than I did getting into things.

A lot of people will drop out and get discouraged. That happened before too. Learning things requires persistence. I think the only real case to be made is that AI's sense of immediate pleasure can neuter people away from running into friction. AI natives likely won't understand friction and question it.

What’s not clear to me is: if writing more code per engineer is possible, does that result in fewer engineers or just more software, especially in areas that traditionally got squeezed: UX, testing, DevEx, documentation, etc. Perhaps the bar just gets raised?

Me: Isn’t it crazy that X is better than Y.

Claude: what an insightful critique, Y is better than X because of x, y, and z reasons.

- And this answer from Claude was good. Thoughtful, well-reasoned. But it was opposite the point I wanted to make so I said :

Me: “oh, you heard me say Y is better than X, I actually was being counter/intuitive and said X is better than Y”

And Claude responded:

Claude: oh you’re absolutely right, X is better than Y for the following reasons (and Claude again provided a well reasoned response here)

And this is sort of that dumb smart genius meme.

“It’s just autocomplete” “No it’s way more than that it has a model in its mind” “It’s just autocomplete”

I liken it to the library of babel. All the genius in the world, but only if you have the right index keys.

Someone needs to watch iron man 3...

I like to think of it as a normal distribution, the further away a programmer is to the right of the mean, the more their benefit. It's almost like it's their standard deviation squared (σ²). So someone like Matt Perry (as OP mentioned), who is a >99.99% programmer for argument's sake and is therefore four standard deviations away from the mean... Matt gets a (4×4) 16x multiplying effect on their productivity.

Someone who is a slightly above average programmer might see a 2 or 3x boost on their productivity, which is huge(!) and might also make them fear for their job. Which tracks with the level of moral panic we are seeing and experiencing. This math kinda still holds up for "bad programmers" too (i.e. left of the mean), as in they still see a boost to their productivity (negative squared is a positive number)... but there's something iffy about their results. The technical debt is unmaintainable and because they don't _understand_ the systems that they're operating in, they end up in the "3 hour" prompt loops that the OP refers to.

> Similarly, if Matt Perry handed me the keys to the Motion repository and told me to take over, I wouldn’t have the same results even though I have access to the same set of LLM tools.

The question is -- how long is this multiplier going to exist for? Some people would wager "for the foreseeable long-term future"; some people think it will widen further; and some people think it will diminish or god forbid even collapse. It feels like most arguments at the moment (like this article's) are that the humans who "know what they are doing" will be able to baton the hatches and avoid being usurped by ever-capable models. I saw it in a café yesterday: someone was using a coding agent to build a marketing website for their project, getting more and more frustrated by not getting the outcome they wanted. Their friend typed a couple of sentences on their keyboard and got a "Dude! How did you do that? That was sick!" a minute or so later. "I used to build websites" the friend said. -- The friend 'knew what they were doing'.

How much longer is knowing what you're doing going to be a moat?

I work with two pretty green developers. The rate that they can make a mess is now phenomenal. And the sense of confidence the tools give them with early successes, means any experience I might have to offer means less now. Which is ok, I’m not going to be that “my experience has to be useful to you so I still fell relevant” old guy. But I do find myself curious how “lessons are learned” that lead to greater and greater tool exploitation in this brave new world.

And still worth repeating that AI has net negative gain in team settings but is a booster for lone wolves like me.

1. AIs aren't yet good at architecture.

2. AIs aren't yet good at imagining technically exciting stuff to build.

And I agree that there's still space there to build a career in the short to medium term (plus Jevons Paradox). When both those points are no longer true we are certainly much closer to, dear I say it, agi. I suspect that (1) will be solved for somewhat limited domains in the near future using harnesses. And it could snowball from there.

Those are people who weren't making it to the MVP stage before LLMs.

There is no doubt that highly technical people are getting A LOT more out of LLMs than people without dev experience, in an absolute sense. I think it's less clear in a relative sense.

A question I also ask myself a lot: What are the skills I'm leveraging, exactly, as a highly experienced developer that's now doing a lot of vibe coding?

1) I'm choosing good technology for the task, and thinking about what LLM-agents are good at and choosing technology that they can work well with.

2) I'm choosing good workflows for the LLM-agent, starting a new context at the right time, having it test things, making sure it has logging that it can inspect, making sure it can operate the application in a way that it can debug and inspect it.

3) I'm thinking about the code even though I'm not looking at it, I'm telling it how I want things implemented, I'm telling it how to debug things.

I think these are all hard things for non-developers to do, but I also think non-developers will be able to replicate a large chunk of #1 and #2 relatively quickly. I only have to figure out that it's valuable to tell the LLM-agent to use playwright when working on web page visuals once, and then I can tell you to do that too. Or the coding agents will come with that knowledge built-in (to the model or as a builtin skill or whatever). Knowledge around this will accumulate and become easier for non-developers to access, and in many cases be builtin to the models or harnesses.

Even early stage startups have folks slapping together PoCs / MVPs, etc. that most would consider trash after seeing it years later. The trash creates some technical debt (and job security maybe) but you actually have something that can ship and be validated as being worth further investment sooner than later.

One can always go back and do better V2. In fact, surely in a few years models would be good enough to even do that V1->V2 as well.

I've found I can prevent the LLM, in many cases, from thrashing on a bug/feature for long periods of time by switching into plan mode and, even in the middle of a conversation, having it reassess the structure around the problem, first. If you keep prompting about the same bug, it may keep producing variations of the problem code. But forcing it to stop and 'think' for a bit, has yielded much better results.

It can allow a skilled engineer to have multiplied effect of repeating their skills HOWEVER it would take away their ability to question, think and improve themselves. The syntax highlighting by editors is a good example, most engineers cant work without it, however its a static skill which does not needs constant improvement so its an acceptable support risk.

Y’all sound the same:

> Let’s start with an uncomfortable truth: AI models have become shockingly good at completing a wide variety of programming tasks. They’re certainly not perfect, but in many cases, they’re good enough. I’m not happy about this, for a wide variety of ethical/environmental/safety reasons, but it is what it is.

More Inevitabilism posting with the “not happy with” but is-what-it-is washing of your hands. At a distance you all look the same: an army of posts insisting the obvious, the inevitable; who knows why you all need to sound the same and say the same thing, but I guess it is to keep it top-of-mind for us alls. It is what it is.

> [...] It’s never been easier to learn about new topics, with tools like ChatGPT that can answer any questions you have. But that only works when you know what questions to ask. My course offers a curated curriculum that will introduce you to all sorts of new techniques. I think you’ll be amazed at what you can build after taking the course.

Okay, sure. I ask these LLMs things too (c.f. outright --be coding) so that’s not necessarily incongruent with the stance of being not-happy-about-this.

But the general argument of 'we will need skilled operators' still holds.

For every 'junior' displaced by AI, there will be some other kind of relevant role they're needed for.

Agentic workflows, integration, all the data science stuff, new UX paradigms.

I don't think the job numbers will dwindle, just shift.

The software is a tool specifically designed around my requirements of managing lectures that need to be prepared, managed, have presentations, grading etc. I wanted one big space where i can quickly access all related data in a workspace, fold and unfold important aspects while also editing and moving contents across multiple days/lectures.

The first version is a vscode plugin, which i now use since about 4 months without or with minor modifications to manage my lectures and private data. The second version is a standalone application which improves on the ideas of the first version and goes a few steps further.

AI can make you something that looks like its running quickly. But when you try to finish it takes way longer then you'd think. You need to specify every little detail. You need to make its KISS and DRY etc. You let it analyze the application structure and simplify and cleanup nearly the same amount of times as you add features. While fixing bugs you might need to run the same thing multiple times and revert any unrequired changes. You need to think about good level of debug logs and ways that the program can help you find errors and report them quickly.

I hope my project will be ready in about 2 to 3 months. The current version is according to a quick analysis over 850 files with 250'000 lines of code.

I spent about 2000$ on ai subscriptions in that time. 200$ claude for a while down to 100$ a month now. 20$ to openai which is very important for architecture and reviews. 20$ on tests with other ai's, but i rarely use them in the works. I also spent 1500$ on 2 * 3090's to hopefully have a local ai agent in the future.

I spend about 2 to 4 hours each day (including weekends) to check that app and write prompts.

I would never have been able to create such a large and complex project next to my other tasks and i am very confident that the final product will be good enough for productive work.

When you see rising inequality, don't just cheer because you happen to win for now.. maybe think about the future and also others..

You cannot hold a computer liable for any of those reasons. You can, however, sue the human that built or used the AI. So those concerns shoudn't be any different with or without AI. The same problems will be here either way. If you really care about those problems, you would demand your representatives in government actually enshrine those things in law, with some teeth, to ensure companies prevent problems with them. If you don't do something about those problems (with or without AI), then it's clear by your actions that ethical/environmental/safety concerns aren't actually that important to you.

It also has a multiplying effect on technical deficits.

If you habitually demonstrate poor attention to detail when developing software, AI will amplify that too.

LLMs project who we are back at us, amplified, for good or ill, and I’m starting to wonder exactly how deep that runs.

Seemingly every AI pilled programmer who writes a blog post on AI's impact on software engineering has the same philosophical argument, and it's wording changes slightly every 6-12 months to reflect the newest models capabilities.

In 2023 it was: "AI is just autocomplete. It can't code whole blocks on it's own."

In 2024 it was: "AI is only good for scaffolding new projects, or boiler plate code. It can't write the application whole sale."

Since November 2025 it's been: "AI is only writing the code for us. It can't manage architecture, or do the long term planning required for real world applications."

In 6-12 months when the AI is doing an increasing amount of the architecture and high level planning, what will AI pilled programmers fall back on then?

AIs have skills humans aren’t good at like nerding out on technical details.

That’s not a perfect map because I’m spitballing. However there is a symbiosis.

I am not sure I am productive anymore with AI as I am up to 125 repos and agents most of which are tools for managing AIs and things break frequently that it feels like spinning plates.

I spent two months in November and December last year writing by hand a fundamental library to constrain how the AIs build clis. That did make things move a lot faster but for those two months I felt the slowness.

I think it will always be like this. It’s the nature of paradigm shift to shift.

So while the author's points are completely true and valid, an executive will say "True, but Claude will get smarter faster than these problems and in 3 years it'll fix everything" and there's absolutely nothing you can say or do in response to this.

The problem is just that the question is not whether "human developers will be necessary in the near future", it's "how many human developers will be necessary in the near future" - managers wanting to exploit the efficiency gains by deciding that fewer developers can now do more work "thanks" to AI.

The guy uses Claude Code, same as me… it’s like a highly skilled mason with a chisel, against me with a chisel. I’m not going to produce the same masterpiece, because there is SME that underpins the accelerant.

- Lesser overall engineers needed -> lesser demand of human engineers -> lower compensations

- insufficient training at junior levels.

- longer time to productive human engineering skill.

These are playing out right now, and a concern for all engineers in the industry. IronMan amplification don't address the above

Now I can just get to it. I know what I want and can organize the codebase, whatever the code is I can generate it.

I think most understand that their jobs are going away because we will need fewer engineers to build the software their companies currently need.

Now - huge opportunity for new companies and markets, but not sure if they will be as profitable.

Not the most talented developer, but this has been pretty much my experience as well. Just keep it under control, know what and why its doing at every step, read the code, and then it will boost your productivity.

If you know what good looks like, the tools are incredible. If you don't, they help you produce plausible wrong things faster.

What can Tony Stark do if all is done by the suit? What can he do after a year, two years, five years?

* it cheats at verification. Even with specific instructions how to verify, it still cheats.

* generating UX(CLI tool) that is absolute garbage and inconsistent, even with specific instructions to minimize unnecessary flags, use convention over configuration ,etc.

* it absolutely will not go 'above and beyond' to solve problems - if task is hitting a permission or dependency barrier, it'll likely cheat or handwave the problem away. (gpt 5.5 xhigh)

There is maybe this hope/hubris that we can figure out just the right incantations or agent workflows to eliminate these issues - I was optimistic about this too but after trying for awhile and seeing them not only not go away but in some cases regress with newer models, I am less sure.

One is augmentation and the other is whack-a-mole.

Excellent read.

Yes this is what I have been experiencing as well. I kind of live in a bubble around my own technical expertise, but it seemed only natural that you would have to guide the model. I'm new to AI as of February. Someone suggested I use it to upgrade my Hugo site and that was all I needed. No I am converting a CRM to obsidian and Rewriting some Obsidian plug-ins to do what I want them to do.

For example, I am using an Obsidian plug-in to do presentations from my markdown files "Advanced Obsidian Slides" but it styles the slides using absolute styling. Every time I tried to the prompt model to improve display elements in that styling I spent a lot of time asking it to go back and try something else to get the results I wanted.

Finally I decided to rewrite the plugin with a fluid design with in the fixed boundary of the slides. Instead of using floats I switched to Flexboxes and all my problems were solved in one-shot situations.

The moral of the story is don't expect the model to do what you want it to do without the proper framework. And research correct terms, you will save yourself a lot of time prompting/writing instruction docs that the LLM will end up implementing.

This sentiment will stray further from the truth as time goes on.

Sure, it's a multiplier for those who are already skilled, but for those who are unskilled, it is capable of taking you from 0 -> 1+.

The ones currently benefiting from AI are the ones who (i) have a general understanding of how an AI works and experience with using it and (ii) have a very generic understanding of what it is they're trying to do (programming, most likely) and know the limits of their tools, but don't know how to actually do anything meaningful.

The whole point of AI is to open the door of complexity to normies; they are the ones benefiting most from it. For a skilled developer, it may make a 1hr task -> 5 mins; for a normie, it makes something which was utterly impossible into -> now within his reality to achieve. the difference for normies is just more life-changing.

If you think of skilled developers as the ceiling and normies as the floor, AI raises the floor higher by giving normies more capability, which makes the ceiling seem less impressive. But eventually the floor will surpass the ceiling, and then it'll be a matter of who can operate AI better/how good AI is.

Guitars do not think. AI does. The analogies that try to paint AI as "just another inanimate tool" are way off base, and so is the conclusions of this article.

I didn't think this 6 months ago but today after what I've seen these models debug and accomplish in established, messy production monoliths, I'm fully convinced even the worst vibe coders are only a year or two away from being able to actually create something from scratch and have it not blow up 50 files in.

So I guess I take the totally opposite stance, today's AI is the worst AI will ever be at coding, and I believe the vested interests behind AI do not plan on making it any worse at this task, so...

Everything these days is either the greatest thing ever or the worst thing ever. All the stuff in the middle has vanished. Very few it seems acknowledge AI as being a useful tool. It's either "We're all being replaced" or "The technology is all slop" and everyone talks over each other like it's the Super Bowl and their teams are battling it out.

It would be nice if we could just look to the opportunities this tech offers and focus on that.

I used to be a PM and am technically literate enough but can only very minimally write code. I have been using LLMs to build (or try to, at least) internal tools for my business since GPT-4.

In the early days, I'd get a little ways, then the LLM would start breaking things, and I'd try but fail to get it to fix things. But over successive generations, I was increasingly able to get it unstuck by offering suggestions on where it may have gone wrong. With Opus 4.7, I don't even really have to do that - if something isn't working it's usually sufficient to just tell it what's broken. It can figure out how to fix it without my input. And of course fewer things are broken in the first place.

So I think I'm very well positioned to understand how these things are improving - better able to get the LLM to do what I want than the post OP quoted from /vibecoding (though I am 99% sure that post is actually AI slop), but less so than most of the people posting in this thread. As they've improved, whatever ability I have to guess at the causes of problems based on my experience having seen things go wrong with products I've PMed has become less necessary to getting the right outcome.

I expect that trend to continue - increasingly the LLM won't need the guidance of people with a great deal of technical expertise. I basically no longer have to attempt to diagnose problems in order to get them fixed, though with the caveat that I am building internal tools for which I am the only user, so certainly much simpler in scope than the stuff OP is talking about.

> Without guidance, LLMs tend to paint themselves into a corner, because they’re generating code to solve individual prompts, not thinking holistically about an application’s architecture.

The crux of what I'm trying to say here is that I absolutely believe that this line is 100% true today, but I would be deeply cautious about assuming that it will continue to be true given the improvements in LLMs over the past few years.

Maybe not the same agency you would expect from a human being, but if you put them in a ralph loop they can go far, far away, and mostly because on how we build our world in the pre-llm era: do you need to order something (or you want to hire a hitman)? -> you can go do it on a web site or via whatsapp or by calling some API.