This has been my experience almost every time I use AI: superficially it seems fine, once I go to extend the code I realize it's a disaster and I have to clean it up.

The problem with "code is cheap" is that, it's not. GENERATING code is now cheap (while the LLMs are subsidized by endless VC dollars, anyway), but the cost of owning that code is not. Every line of code is a liability, and generating thousands of lines a day is like running up a few thousand dollars of debt on a credit card thinking you're getting free stuff and then being surprised when it gets declined.

But if your job is to assemble a car in order to explore what modifications to make to the design, experiment with a single prototype, and determine how to program those robot arms, you’re probably not thinking about the risk of being automated.

I know a lot of counter arguments are a form of, “but AI is automating that second class of job!” But I just really haven’t seen that at all. What I have seen is a misclassification of the former as the latter.

But my biggest objection to this "engineering is over" take is one that I don't see much. Maybe this is just my Big Tech glasses, but I feel like for a large, mature product, if you break down the time and effort required to bring a change to production, the actual writing of code is like... ten, maybe twenty percent of it?

Sure, you can bring "agents" to bear on other parts of the process to some degree or another. But their value to the design and specification process, or to live experiment, analysis, and iteration, is just dramatically less than in the coding process (which is already overstated). And that's without even getting into communication and coordination across the company, which is typically the real limiting factor, and in which heavy LLM usage almost exclusively makes things worse.

Takes like this seem to just have a completely different understanding of what "software development" even means than I do, and I'm not sure how to reconcile it.

To be clear, I think these tools absolutely have a place, and I use them where appropriate and often get value out of them. They're part of the field for good, no question. But this take that it's a replacement for engineering, rather than an engineering power tool, consistently feels like it's coming from a perspective that has never worked on supporting a real product with real users.

This is simply not how expert programmers work. Programming is planning, and programming languages are surprisingly good planning tools. But, of course, becoming an expert is hard and requires not only some general aptitude but also substantial time, experience and effort.

My theory is that this is a source of diverging views on LLMs for programming: people who see programming languages as tools for thought compared to people who see programming languages as, exclusively, tools to make computers do stuff. It's no surprise that the former would see more value in programming qua programming, while the latter are happy to sweep code under the rug.

The fundamental problem, of course, is that anything worth doing in code is going to involve pinning down a massive amount of small details. Programming languages are formal systems with nice (well, somewhat nice) UX, and formal systems are great for, well, specifying details. Human text is not.

Then again, while there might be a lot of details, there are also a lot of applications where the details barely matter. So why not let a black box make all the little decisions?

The question boils down to where you want to spend more energy: developing and articulating a good conceptual model up-front, or debugging a messy system later on. And here, too, I've found programmers fall into two distinct camps, probably for the same reasons they differ in their views on LLMs.

In principle, LLM capabilities could be reasonably well-suited to the up-front thinking-oriented programming paradigm. But, in practice, none of the tools or approaches popular today—certainly none of the ones I'm familiar with—are oriented in that direction. We have a real tooling gap.

I'm pretty sure the way I was doing things in 2005 was completely different compared to 2015. Same for 2015 and 2025. I'm not old enough to know how they were doing things in 1995, but I'm pretty sure there very different compared to 2005.

For sure, we are going through some big changes, but there is no "as it has been done for decades".

Also credit where credit is due. Origin of this punchline:

https://nitter.net/jason_young1231/status/193518070341689789...

https://programmerhumor.io/ai-memes/code-is-cheap-show-me-th...

But actually good code, with a consistent global model for what is going on, still won't come from Opus 4.5 or a Markdown plan. It still comes from a human fighting entropy.

Getting eyes on the code still matters, whether it's plain old AI slop, or fancy new Opus 4.5 "premium slop." Opus is quite smart, and it does its best.

But I've tried seriously using a number of high-profile, vibe-coded projects in the last few weeks. And good grief what unbelievable piles of shit most of them are. I spend 5% of the time using the vibe-coded tool, and 95% of the time trying to uncorrupt my data. I spend plenty of time having Opus try to look at the source to figure out what went wrong in 200,000 lines of vibe-coded Go. And even Opus is like, "This never worked! It's broken! You see, there's a race condition in the daemonization code that causes the daemon to auto-kill itself!"

And at that point, I stop caring. If someone can't be bothered to even read the code Opus generates, I can't be bothered to debug their awful software.

I'm sorry, but this is an indicator for me that the author hasn't had a critical eye for quality in some time. There is massive overlap between "bad" and "functional." More than ever. The barrier-to-entry to programming got irresponsibly low for a time there, and it's going to get worse. The toolchains are not in a good way. Windows and macOS are degrading both in performance and usability, LLVM still takes 90% of a compiler's CPU time in unoptimized builds, Notepad has AI (and crashes,) simple social (mobile) apps are >300 MB download/installs when eight years ago they were hovering around a tenth of that, a site like Reddit only works on hardware which is only "cheap" in the top 3 GDP nations in the world... The list goes on. Whatever we're doing, it is not scaling.

> Thus, the extreme proponents of manic “agentic” vibe coding,[19] and the outright denouncers of LLMs, are both missing the forest for the trees. That there is a pragmatic middle path, where people who have the experience, expertise, competence, and the ability to articulate can use these tools to get the outcomes they desire with the right sets of trade-offs.

LLMs are tools. They are not gods to replace man, nor are they exclusively means of harm. It is entirely possible to denounce the blatant attempt of rent extraction in the form of OpenAI, Google Gemini, Microsoft CoPilot, Anthropic, and others, while still running Qwen and Ministral and their like on local hardware. You can, in fact, have it “both ways”.

As fun as it is to poke at cloud services to see their new features and advancements, I (in IT terms) personally could never recommend them in any serious enterprise context for the very reason that they’re fundamentally insecure; you will never, ever have full E2EE with these services because it would nullify their ability to evolve, improve, monetize, and exploit.

That said? I can truly be a one dinosaur army in an enterprise now, as a generalist with a modest Mac Mini (or Studio) and a local LLM to fill in edge cases as needed. I can query these local tools for questions on database schemas or build a one-off API integration for me, so I can focus on the substance of work in safeguarding and accelerating the enterprise. I don’t need fleets of specialists unless I’m running a huge conglomerate or have specific needs - and even then, it’s going to be cheaper to retain one or two seniors to direct the larger army of generalists when needed. The landscape has changed, and it’s why I target leadership and management roles accordingly with my sales pitch (“One senior generalist can do the work of three mid-level specialists”).

Don’t get me wrong, I still have immense grievances regarding theft of work, reductions in force, rent extraction, and the seeming attempt at destroying local general compute, but local LLMs as a tool have been in my kit for years, and that’s not going away.

  Historically, it would take a reasonably long period of consistent effort and many iterations of refinement for a good developer to produce 10,000 lines of quality code that not only delivered meaningful results, but was easily readable and maintainable. While the number of lines of code is not a measure of code quality—it is often the inverse—a codebase with good quality 10,000 lines of code indicated significant time, effort, focus, patience, expertise, and often, skills like project management that went into it. Human traits.

  Now, LLMs can not only one-shot generate that in seconds,

Both Code and talk are cheap. Show me the trust. Show me how I can trust you. Show me your authenticity. Show me your passion.

Code used to be the sign of authenticity. This is whats changing. You can no longer guarantee that large amounts of code let's say are now authentic, something which previously used to be the case (for the most part)

I have been shouting into the void many times about it but Trust seems to be the most important factor.

Essentially, I am speaking it from a consumer perspective but suppose that you write AI generated code and deploy it. Suppose you talked to AI or around it. Now I can do the same too and create a project sometimes (mostly?) more customizable to my needs for free/very-cheap.

So you have to justify why you are charging me. I do feel like that's only possible if there is something additional added to value. Trust, I trust the decision that you make and personally I trust people/decisions who feel like they take me or my ideas into account. So, essentially not ripping me off while actively helping. I don't know how to explain this but the most thing which I hate is the feeling of getting ripped off. So justifiable sustainable business who is open/transparent about the whole deal and what he gets and I get just gets my respect and my trust and quite frankly, I am not seeing many people do that but hopefully this changes.

I am curious now what you guys of HN think about this & what trust means to you in this (new?) ever-changing world.

Like y'know I feel like everything changes all the time but at the same time nothing changes at the same time too. We are still humans & we will always be humans & we are driven by our human instincts. Perhaps the community I envision is a more tight knit community online not complete mega-sellers.

Thoughts?

After using AI to code, I came to the same conclusion myself. Interns and juniors are fully cooked:

- Companies will replace them with AI, telling seniors to use AI instead of juniors

- As a junior, AI is a click away, so why would you spend sleepless nights painstakingly acquiring those fundamentals?

Their only hope is to use AI to accelerate their own _learning_, not their performance. Performance will come after the learning phase.

If you're young, use AI as a personal TA, don't use it to write the code for you.

Yes and no. Code is not art, but software is art.

What is art, then? Not something that's "beautiful", as beauty is of course mostly subjective. Not even something that works well.

I think art is a thing that was made with great care.

It doesn't matter if some piece of software was vibe-coded in part or in full, if it was edited, tested, retried enough times for its maker to consider it "perfect". Trash is something that's done in a careless way.

If you truly love and use what you made, it's likely someone else will. If not, well... why would anyone?

We practically found one thought. The measure is: how well can an AI operate in/with your codebase.

I regularly find myself wondering if skeptics throwing around their empirical failure are obscuring their bad code/docs/setup.

AI is a useful tool, agreed - but it will always be reliant upon humans writing code.

Let's take a very simple example to prove my point. Let's say that everybody is now using AI to write code, and someone develops a new programming language with it. Cool! We have a new language now.

What we don't have, however, are any examples of how that programming language is used. Nothing that the AI can learn from.

How well do you think AI is going to handle being able to code in this new language which doesn't appear at all in its training data?

How much of this is mass financial engineering than real value. Reading a lot of nudges how everyone should have Google or other AI stock in their portfolio/retirement accounts

> Proceeds to write literal books of markdown to get something meaningful

>> It requires no special training, no new language or framework to learn, and has practically no entry barriers—just good old critical thinking and foundational human skills, and competence to run the machinery.

> Wrote a paragraph about how it is important to have serious experience to understand the generated code prior to that

>> For the first time ever, good talk is exponentially more valuable than good code. The ramifications of this are significant and disruptive. This time, it is different.

> This time is different bro I swear, just one more model, just one more scale-up, just one more trillion parameters, bro we’re basically at AGI

Also, that projects page on his website is atrocious; hate to be "that guy" but I don't trust the author's insight since "personal projects" seems to include a lot more than just his work; the first several PRs I looked at where all vibed.

I'm not interested in re-implementations of the same wheel over and over again telling me and people who know how to write real software (have been doing it since I was 12) that we are becoming unnecessary bc you can bully an extremely complex machine built on a base theory of heuristics abstracted out endlessly (perceptually) to re-invent the same specs in slightly different flavors.

> 100% human written, including emdashes. Sigh. If you can't write without emdashes, maybe you spend too much time with LLMs and not enough time reading and learning on your own. Also people can lie on the Internet, they do it all the time, and if not then I'm doing it right now.

The hubris on display is fascinating.

What soft-skill buzzword will be the next one as the capital owners take more of the supposed productivity profits?

This quote is from Torvalds, and I'm quite sure that if he weren't able to write eloquent English no one would know Linux today.

Code is important when it's the best medium to express the essence of your thoughts. Just like a composer cannot express the music in his head with English words.

Put the agent on the wheel and observe it as it tries ruthlessly to pass the test. These days, likely it will manage to pass the tests after 3-5 loops, which I find fascinating.

Close the loop, and try an LLM. You will be surprised.

Classical indicators of good software are still very relevant and valid!

Building something substantial and material (ie not an api wrapper+gui, to-do list) that is undeniably well made, while being faster and easier than it used to be, still takes a _lot_ of work. Even though you don't have to write a line of code, it moves so fast that you are now spending 3.5-4 days of your work week reading code, using the project, running benchmarks and experimental test lanes, reviewing specs and plans, drafting specs, defining features and tests.

The level of granularity needed to get earnestly good results is more than most people are used to. It's directly centered at the intersection between spec heavy engineering work and writing requirements for a large, high quality offshore dev team that is endearingly literal in how they interpret instructions. Depending on the work, I've found that I average around one 'task' per 22-35 lines of code.

You'll discover a new sense of profound respect for the better PMs, QA Leads, Eng Directors you have worked with. Months of progress happen each week. You'll know you're doing it right when you ask an Agent to evaluate the work since last week and it assumes it is reviewing the output of a medium sized business and offers to make Jira tickets.

The real "cost" of software is reliance: what risk do your API clients or customers take in relying on you? This is just as true for free-as-in-beer software as for SaaS with enterprise SLA's.

In software and in professions, providers have some combination of method and qualifications or authority which justifies reliance by their clients. Both education and software have reduced the reliance on naked authority, but a good track record remains the gold standard.

So providers (individuals and companies) have to ask how much of their reputation do they want to risk on any new method (AI, agile, ...)? Initially, it's always a promising boost in productivity. But then...

So the real question is what "Show me" means - for a quick meet, an enterprise sale, an enduring human-scale consumer dependence...

So, prediction: AI companies and people that can "show me" will be the winners.

(Unfortunately, we've also seen competitive advantage accrue to dystopian hiding of truth and risk, which would have the same transaction-positive effect but shift and defer the burden of risk. Let's hope...)

- A good and experienced developer who knows how to organize and structure systems will become more productive.

- An inexperienced developer will also be able to produce more code but not necessarily systems that are maintainable.

- A sloppy developer will produce more slop.

I hate this trend of using adjectives to describe systems.

Fast Secure Sandboxed Minimal Reliable Robust Production grade AI ready Let's you _____ Enables you to _____

But somewhat I agree, code is essentially free, you can shit out infinite amounts of code. Unless it's good, then show the code instead. If your code is shit, show the program. If your program is shit, your code is worse, but you still pursing an interesting idea (in your eyes), show the prompt instead of the slop generated. Or even better communicate an elaborate version of the prompt.

>One can no longer know whether such a repository was “vibe”

This is absurd. Simply false, people can spot INSTANTLY when the code is good, see: https://news.ycombinator.com/item?id=46753708

expertise and effort is and will continue to be for the forseeable future essential.

talk, like this, still cheap.

Usually one can tell quite easily, actually...

There is a net positive gain on the automated testing side of things but I think a bad developer, even with AI will not be able to out-compete a good 10x developer without AI. The costs of incorrect abstractions is just too high and LLMs don't really help avoid those.

You have to ask the right questions. It's a bit like in Hitchhiker's Guide to the Galaxy... Where a super-intelligent computer took millions of years to calculate the meaning of life as the number 42. The wrong question will always waste computation.

I think that's always been true. The ideas and reasoning process matter. So does the end product. If you produced it with an LLM and it sucks, it still sucks.