Hearing people on tech twitter say that LLMs always produce better code than they do by hand was pretty enlightening for me.
LLMs can produce better code for languages and domains I’m not proficient in, at a much faster rate, but damn it’s rare I look at LLM output and don’t spot something I’d do measurably better.
These things are average text generation machines. Yes you can improve the output quality by writing a good prompt that activates the right weights, getting you higher quality output. But if you’re seeing output that is consistently better than what you produce by hand, you’re probably just below average at programming. And yes, it matters sometimes. Look at the number of software bugs we’re all subjected to.
And let’s not forget that code is a liability. Utilizing code that was “cheap” to generate has a cost, which I’m sure will be the subject of much conversation in the near future.
> These things are average text generation machines.
Funny... seems like about half of devs think AI writes good code, and half think it doesn't. When you consider that it is designed to replicate average output, that makes a lot of sense.
So, as insulting as OP's idea is, it would make sense that below-average devs are getting gains by using AI, and above-average devs aren't. In theory, this situation should raise the average output quality, but only if the training corpus isn't poisoned with AI output.
I have an anecdote that doesn't mean much on its own, but supports OP's thesis: there are two former coworkers in my linkedin feed who are heavy AI evangelists, and have drifted over the years from software engineering into senior business development roles at AI startups. Both of them are unquestionably in the top 5 worst coders I have ever worked with in 15 years, one of them having been fired for code quality and testing practices. Their coding ability, transition to less technical roles, and extremely vocal support for the power of vibe coding definitely would align with OP's uncharitable character evaluation.
After a certain experience level though, I think most of us get to the point of knowing what that difference in quality actually matters.
Some seniors love to bikeshed PRs all day because they can do it better but generally that activity has zero actual value. Sometimes it matters, often it doesn't.
Stop with the "I could do this better by hand" and ask "is it worth the extra 4 hours to do this by hand, or is this actually good enough to meet the goals?"
LLM generated code is technical debt. If you are still working on the codebase the next day it will bite you. It might be as simple as an inconvenient interface, a bunch of duplicated functions that could just be imported, but eventually you are going to have to pay it.
All code is technical debt though. We can't spend infinite hours finding the absolute minima of technical debt introduced for a change, so it is just finding the right balance. That balance is highly dependent on a huge amount of factors: how core is the system, what is the system used for, what stage of development is the system, etc.
A human SWE can use an LLM to refactor and reduce some of the debt just as easily too. I think fundamentally, the possible rate of new code and new technical debt introduced by LLMs is much higher than a human SWE. Left unchecked, a human still needs sleep and more humans can't be added with more compute.
There's an interesting aspect to the LLM debt being taken on though in that I'm sure some are taking it on now in the bet/hopes that further advancements in LLMs will make it more easily addressable in the future before it is a real problem.
So I can tell you don’t use these tools, or at least much, because at the speed of development with them you’ll be knee deep in tech debt in a day, not a month, but as a corollary can have the same agentic coding tools undergo the equivalent of weeks of addressing tech debt the next day. Well, I think this applies to greenfield AI-first oriented projects that work this way from the get go and with few humans in the loop (human to human communication definitely becomes the rate limiting step). But I imagine that’s not the nature of your work.
Claude Code is way better than I am at rummaging through Git history, handling merge conflicts, renaming things, writing SQL queries whose syntax I always forget (window functions and the like). But yeah. If I give it a big, non-specific task, it generates a lot of mediocre (or worse) code.
That's funny that's all the things I don't trust it to do. I actually use it the other way around, give it a big non-specific task, see if it works, specify better, retry, throw away 60% - 90% of the generated code, fix bugs in a bunch of places and out comes an implemented feature.
I worked for a relatively large company (around 400 employees there are programmers). The people who embraced LLM-generated code clearly share one trait: they are feature pushers who love to say "yes!" to management. You see, management is always right, and these programmers are always so eager to put their requirements, however incomplete, into a Copilot session and open a pull request as fast as possible.
The worst case I remember happened a few months ago when a staff (!) engineer gave a presentation about benchmarks they had done between Java and Kotlin concurrency tools and how to write concurrent code. There was a very large and strange difference in performance favoring Kotlin that didn't make sense. When I dug into their code, it was clear everything had been generated by a LLM (lots of comments with emojis, for example) and the Java code was just wrong.
The competent programmers I've seen there use LLMs to generate some shell scripts, small python automations or to explore ideas. Most of the time they are unimpressed by these tools.
As someone who: 1.) Has a brain that is not wired to think like a computer and write code. 2.) Falls asleep at the keyboard while writing code for more than an hour or two. 3.) Has a lot of passion for sticking with an idea and making it happen, even if that means writing code and knowing the code is crap.
So, in short, LLMs write better code than I do. I'm not alone.
I've been playing with vibe coding a lot lately and I think in most cases, the current SOTA LLM's don't produce code that I'd be satisfied with. I kind of feel like LLM's are really really good at hacking on a messy and fragile structure, because they can "keep track many things in their head"
BUT
An LLM can write a PNG decoder that works in whatever language I choose in one or a few shots. I can do that too, but it will take me longer than a minute!
(and I might learn something about the png format that might be useful later..)
Also, us engineers can talk about code quality all day, but does this really matter to non-engineers? Maybe objectively it does, but can we convince them that it does?
I kinda like Theo's take on it (https://www.youtube.com/watch?v=Z9UxjmNF7b0): there's a sliding scale of how much slop should reasonably be considered acceptable and engineers are well advised to think about it more seriously. I'm less sold on the potential benefits (since some of the examples he's given are things that I would also find easy by hand), but I agree with the general principle that having the option to do things in a super-sloppy way, combined with spending time developing intuition around having that access (and what could be accomplished that way), can produce positive feedback loops.
In short: when you produce the PNG decoder, and are satisfied with it, it's because you don't have a good reason to care about the code quality.
> Maybe objectively it does, but can we convince them that it does?
I strongly doubt it, and that's why articles like TFA project quite a bit of concern for the future. If non-engineers end up accepting results from a low-quality, not-quite-correct system, that's on them. If those results compromise credentials, corrupt databases etc., not so much.
I tried vibe coding a BMP decoder not too long ago with the rationale being “what’s simpler than BMP?”
What I got was an absolute mess that did not work at all. Perhaps this was because, in retrospect, BMP is not actually all that simple, a fact that I discovered when I did write a BMP decoder by hand. But I spent equal time vibe coding and real coding. At the end of the real coding session, I understood BMP, which I see as a benefit unto itself. This is perhaps a bit cynical but my hot take on vibe coders is that they place little value on understanding things.
Just out of curiousity (as someone fairly familiar with the BMP spec, and also PNG incidentally): what did you find to be the trickiest/most complex aspects?
Mind explaining the process you tried? As someone who’s generally not had any issue getting LLMs to sort out my side projects (ofc with my active involvement as well), I really wonder what people who report these results are trying. Did you just open a chat with claude code and try to get a single context window to one shot it?
In my view they’re great for rough drafts, iterating on ideas, throwaway code, and pushing into areas I haven’t become proficient in yet. I think in a lot of cases they write ok enough tests.
It'd be rather surprising if you could train an AI on a bunch of average code, and somehow get code that's always above average. Where did the improvement come from?
We should feed the output code back in to get even better code.
AI generally can improve through reinforcement learning, but this requires it to be able to compare its output to some form of metric. There aren't a lot of people I'd trust to RLHF for code quality, and anything more automated than that is destined to collapse due to Goodhart's Law.
It's let me apply my general knowledge across domains, and do things in tech stacks or languages I don't know well. But that has also cost me hours debugging a solution I don't quite understand.
When working in my core stack though it's a nice force multiplier for routine changes.
How are you judging that you'd write "better" code? More maintainable? More efficient? Does it produce bugs in the underlying code it's generating? Genuinely curious where you see the current gaps.
- it adds superfluous logic that is assumed but isn’t necessary
- as a result the code is more complex, verbose, harder to follow
- it doesn’t quite match the domain because it makes a bunch of assumptions that aren’t true in this particular domain
They’re things that can often be missed in a first pass look at the code but end up adding a lot of accidental complexity that bites you later.
When reading an unfamiliar code base we tend to assume that a certain bit of logic is there for a good reason, and that helps you understand what the system is trying to do. With generative codebases we can’t really assume that anymore unless the code has been thoroughly audited/reviewed/rewritten, at which point I find it’s easier to just write the code myself.
This has been my experience as well. But, these are things we developers care about.
Coding aside, LLM's aren't very good at following nice practices in general unless explicitly prompted to. For example if you ask an LLM to create an error modal box from scratch, will it also implement the ability to select the text, or being able to ctrl c to copy the text, or perhaps a copy message button? Maybe this is a bad example, but they usually don't do things like this unless you explicitly ask them to. I don't personally care too much about this, but I think it's noteworthy in the context of lay people using LLM's to vibe code.
> if you’re seeing output that is consistently better than what you produce by hand, you’re probably just below average at programming
even though this statement does not mathematically / statistically make sense - vast majority of SWEs are “below average.” therein lies the crux of this debate. I’ve been coding since the 90’s and:
- LLM output is better than mine from the 90’s
- LLM output is better than mine from early 2000’s
- LLM output is worse than any of mine from 2010 onward
- LLM output (in the right hands) is better than 90% of human-written code I have seen (and I’ve seen a lot)
The most prolific coders are also more competent than average. Their proliferations are what have trained these models. These models are trained on incredibly successful projects written by masters of their fields. This is usually where I find the most pushback is that the most competent SWEs see it as theft and also useless to them since they have already spend years honing skills to work relentlessly and efficiently towards solutions -- sometimes at great expense.
I'd assume most of the code visible on the web leans amateur. A huge portion of github repos seem to be from students these days. You'll see GitHub's Education page listing "5 million students" (https://github.com/education), which I assume is an under-estimate, as that's only the formal program.
> The most prolific coders are also more competent than average
This is absolutely not true lol, as anyone who's worked with a fabled 10X engineer will tell you. It's like saying the best civil engineer is the one that builds the most bridges.
I've worked with a 10x engineer and indeed they were significantly more competent than the rest of the team in their execution and design. They've seen so many problems and had a chance to discard bad patterns and adopt/try out new ones.
Simon Wilson (known for Django) has been doing a lot of LLM evangelism on his blog these days. Antirez (Redis) wrote a blog post recently with the same vibe.
I doubt they are not good programmers. They are probably better than most of us, and I doubt they feel insecure because of the LLMs. Either I'm wrong, or there's something more to this.
edit: to clarify, I'm not saying Simon and Antirez are part of the group the article criticizes (the hostile LLM evangelists). Although the article does generalize to all LLM evangelists at least in some parts and Simon did react to this here. For these reasons, I haven't ruled him out as a target of this article, at least partly.
The author claims it's not just that one evangelizes it, but that they become hostile when someone claims to not have the same experience in response. I don't recall Either Willison or Antirez scaring people by saying they will be left behind or that they are just afraid of becoming irrelevant. Instead they just talk about their positive experiences using it. Willison and Antirez seem to be fine to live and let live (maybe Antirez a bit less, but they're still not mean about it).
My gut says that is not a property of LLM evangelists, but a property of current internet culture in general. People with strong, divisive, and engaging opinions seem to do well (by some definition of well) online.
This. For every absurd LLM cheerleader, there’s a corresponding LLM minimalist who trots out the “stochastic parrot” line at every possible occasion along with the fact that they do CrossFit and don’t own a TV.
I think the actual problem is everyone tries to assert how capable or not coding agents currently are, but how useful they are depends so much on what you are trying to get them to do and also on your communication with the model. And often its hard to tell whether you're just prompting it wrong or if they're incapable of doing it.
By now we at least agree that stochastical parrots can be useful.
It would be nice if the debate now was less polarized so we could focus on what makes them work better for some and worse for others other than just expectations.
And yeah, as I laid out in the article (that of course, very few people actually read, even though it was short...), I really don't mind how people make code. It's those that try so hard to convince the rest of us I find very suspect.
You seem to be mistaking set and members here. The piece's critique is against the set (LLM Evangelists), not against specific members of the set (the ones you mentioned). One can agree with the point of the piece while still acknowledging there are good programmers who are also LLM evangelists.
You are right, I went a bit too quickly so let me expand a bit my chain of thoughts.
The article is against the set of LLM evangelists who are hostile towards the skeptics.
I 100% agree with the part that basically says fuck you to them.
However, explaining the hostile part with there being the feeling of insecurity (which is plausible but would need evidence) is not fully convincing and it seems dangerous to accept this conclusion and stop looking for the actual reasons this quickly.
And the fact that there are actually good programmers persuaded that LLMs help them weakens the "insecurity" argument quite a bit, at least as the only explanation.
As someone currently pretty much hostile to LLMs, I'm quite interested in what's currently at play but I'm suspicious of claims that initially feel good but are not strongly backed.
Like, if these hostile people were actually shills, we would want to know this and not have closed the eyes too early because of some explanation that felt good, right? Or any actual reason.
Look into the Motte-and-bailey fallacy.
That set seriously doesn't exist. Even the people on youtube doing "vibe coding" benchmarks mostly say it's crap. (Well they probably exist on twitter/linkedin.)
This article just functions as flamebait for people who use LLMs to implement whole features to argue the semantics of "vibe coding". All while everyone is ignoring the writing on the wall. That we will soon have boxes going through billions of tokens every second. At that point slopcoding WILL be productive, but only if you build up the skill to differentiate yourself from the top 10% of prompters.
I don't think it's fair to compare people who are already seemingly at the peak of their career, a place to which they got by building skill in coding. And in fact what they have now that's valuable isn't mostly skill but capital. They've built famous software that's widely used.
Also they didn't adopt the your-career-is-ruined-if-you-don't-get-on-board tone that is sickeningly pervasive on LinkedIn. If you believe that advice and give up on being someone who understands code, you sure aren't gonna write Redis or Django.
I suspect for truly talented people, they just like talking to LLMs. And they're also not 100% focused on programming anymore, so the async nature of it matters more than it does to people who write code full time for a living.
> And then, inevitably, comes the character evaluation, which goes something like this:
I saw a version of this yesterday where a commenter framed LLM-skepticism as a disappointing lack of "hacker" drive and ethos that should be applied to making "AI" toolchains work.
As you might guess, I disagreed: The "hacker" is not driven just by novelty in problems to solve, but in wanting to understand them on more than a surface layer. Messing with kludgy things until they somehow work is always a part of software engineering... but the motive and payoff comes from knowing how things work, and perceiving how they could work better.
What I "fear" from LLMs-in-coding is that they will provide an unlimited flow of "mess around until it works" drudgery tasks with none of the upside. The human role will be hammering at problems which don't really have a "root cause" (except in a stochastic sense) and for which there is never any permanent or clever fix.
Would we say someone is "not really an artist" just because they don't want to spend their days reviewing generated photos for extra-fingers, circling them, and hitting the "redo" button?
We have a hard enough time finding juniors (hell, non-juniors) that know how to program and design effectively.
The industry jerking itself off over Leetcode practice already stunted the growth of many by having them focus on rote memorization and gaming interviews.
With ubiquitous AI and all of these “very smart people” pushing LLMs as an alternative to coding, I fear we’re heading into an era where people don’t understand how anything works and have never been pushed to find out.
Then again, the ability of LLMs to write boilerplate may be the reset that we need to cut out all of the people that never really had an interest in CS that have flocked to the industry over the last decade or so looking for an easy big paycheck.
> to cut out all of the people that never really had an interest in CS
I had assumed most of them had either filtered out at some stage (an early one being college intro CS classes), ended up employed somewhere that didn't seem to mind their output, or perpetually circle on LinkedIn as "Lemons" for their next prey/employer.
My gut feeling is that messy code-gen will increase their numbers rather than decrease them. LLMs make it easier to generate an illusion of constant progress, and the humans can attribute the good parts of the output to themselves, while blaming bad-parts on the LLM.
This is still a thing today. There have been multiple times I oneshot some project that leadership had been waiting on some team forever to finish, and 90% of it was them refusing to touch a "noob" lang like Python or JS.
Is there enough of new blood on HN? For me it was the best place, my favorite website, when I was entering startup scene. Loved it. I don't think a lot of young founders I know ever go here...
Whatever your personal feeling, judgement, or conviction on this matter; do not dismiss the other side because of a couple wingnuts saying crazy stuff (you can find them on both extremes as well as the middle). Stay curious as to why people have their own conviction, and seek the truth!
Humans are tribal, which has both benefits and costs.
In technology, the historical benefits of evangelizing your favorite technology might just be that it becomes more popular and better supported.
Even though LLMs may or may not follow the same path, if you can get your fellow man on-board, then you'll have a shared frame of reference, and someone to talk through different usage scenarios.
> But doing "prompt-driven development" or "vibe coding" with an Agentic LLM was an incredibly disapointing experience for me. It required an immense amount of baby sitting, for small code changes, made slowly, which were often wrong. All the while I sat there feeling dumber and dumber, as my tokens drained away.
Yeah I find they are useful for large sweeping changes, introducing new features and stuff, mostly because they write a lot of the boilerplate, granted with some errors. But for small fiddly changes they suck, you will have a much easier time doing these changes your self.
I have been through this before (wherever/whenever the money seems to flow) - databases are bad, you should use couchbase etc, I was a db expert, the people advocating weren't, but they were very loud. The many, many evangelistic web development alternatives that come and go, all very loud. Now the latest - LLM's, like couchbase et al they have their place but the evangelists are not having any of it.
I work a lot with doctors (writing software for them), I am very envious of their system of specialisation, eg this dude did is such and such a specialist - he knows about it, listen to him. IT seems to be anyone who talks the loudest has a podium, separating the wheat from the chaff is difficult. One day we will have a system of qualifications I hope, but it seems a long way off.
I see how cool and powerful they're getting, but agree there is a huge insecurity element in the evangelism. Everyone wants to be seen as the one who will get a seat running the llms when the music stops playing.
I think that coding assistants tend to quite good as long as what you ask is close to the training data.
Anything novel and the quality if falling off rapidly.
So, if you are like Antirez and ask for a Linenoize improvement that has already be seen many times by the LLM at training time, the result will seem magical, but that is largely an illusion, IMO.
I see no point in making this a numbers game. (Like, I was supposed to say "five" or something?)
Let's make it more of a category thing: when AI shows itself responsible for a new category of life-saving technique, like a cure for cancer or Alzheimer's, then I'd have to reconsider.
(And even then, it will be balanced against rising sea levels, extinctions, and other energy use effects.)
"LLM evangelists - are you willing to admit that you just might not be that good at programming computers?"
The people who were the best at something don't necessarily be the best at a new paradigm. Unlearning some principles and learning new ones might be painful exercise for some masters.
Military history has shown that the masters of the new wave are not necessarily the masters of the previous wave we see the rise and downfall of several civilizations from Roman to Greek for being too sure of their old methods and old military equipments and strategy.
To be fair to both sides, it really is hard to tell if we're in the world of
"you'll be left behind if you don't learn crypto" with crypto
or
"you'll be left behind if you don't learn how to drive" with cars
One of those statements is made in good faith, and the other is made out of insecurity. But we'll probably only really be able to tell looking backwards.
I don't really see the point in worrying about prompting, and agents and mcps, and skills and all this as a skill to learn. They're trivial if you're already a developer and if it gets good enough that you don't need to know software engineering, there's not going to be anything to learn. Setting it up is a subset of software engineering so it will be able to do that its self once it can solve problems reliably without someone to understand what's going on to check it.
I remember a similarly aggressive evangelism about self-driving cars several years ago. I suppose it's not so pleasant, when you feel like you've seen a prophetic glimpse of a brilliant future, to deal with skeptics who don't understand your vision and refuse to give your predictions the credit they deserve.
Of course we need a few people to get wildly overexcited about new possibilities, so they will go make all the early mistakes which show the rest of us what the new thing can and cannot actually do; likewise, we need most of us to feel skeptical and stick to what already works, so we don't all run off a cliff together by mistake.
> It required an immense amount of baby sitting, for small code changes, made slowly, which were often wrong.
Can’t speak for others but that’s not what I’d understand (or do) as vibecoding. If you’re babysitting it every inch of the way then yeah sure I can see how it might not be productive relative to doing it yourself.
If you’re constantly fighting the LLM because you have a very specific notion of what each line should look like it won’t be a good time.
Better to spec out some assumptions, some desired outcomes, tech to be used, maybe the core data structure, ask the llm what else it needs to connect the dots, add that and then let it go
"I find LLMs useful as a sort of digital clerk - searching the web for me, finding documentation, looking up algorithms. I even find them useful1 in a limited coding capacity; with a small context and clear guidelines."
I am curious why the author doesn't think this saves them time (i.e. makes them more productive).
I never had terribly high output as a programmer. I certainly think LLMs have helped increased the amount of code that I can write, net total, in a year. Not to superhuman levels or even super-me levels, just me++.
But, I think the total time spent producing code has gone down to a fraction and has allowed me more time to spend thinking about what my code is meant to solve.
I wonder about two things:
1. maybe added productivity isn't going to be found in total code produced, because there is a limit on how much useful code can be produced that is based on external factors
2. do some devs look at the output of an LLM and "get the ick" because they didn't write it and LLM-code is often more verbose and "ugly", even though it may work? (this is a total supposition and not an accusation in any way. i also understand that poorly thought out, overly verbose code comes with problems over time)
The first of those is about taste, and it's real, and engineers with bad taste write unstable buggy systems.
The second of those is about priority. If all you want is functional code, any old thing will do. That's what I do for one-off scripts. But if you plan to support the code at 2am when exposed to production requests on the internet, you need to understand it, which is about legibility and coherence.
I hope you do have taste, and I hope you value more than simple "it works" tests. But it might be worth looking there for why some struggle with LLM output.
For what it's worth, I use coding agents all the time, but almost never accept their output verbatim outside of boilerplate code.
It seems you find LoC as a measure of productivity. This would answer your question as to why the author does not find it makes them more productive. If total output increases, but quality decreases (which in terms of code means more bugs) then has productivity increased or has it stayed the same?
To answer my own question, if you can pump out features faster but turn around and spend more time on bugs than you do previously then your productivity is likely net neutral.
There is a reason LoC as a measure of productivity has been shunned from the industry for many, many years.
I didn't mean to imply LoC as a measurement of productivity. What I really mean is more "amount of useful code produced to a level the human-using-the-llm determines to be useful".
To try and give an example, say that you want to make a module that transforms some data and you ask the LLM to do it. It generates a module with tons of single-layer if-else branches with a huge LoC. Maybe one human dev looks at it and says, "great this solves my problem and the LoC and verbosity isn't an issue even though it is ugly". Maybe the second looks at it and says, "there's definitely some abstraction I can find to make this easier to understand and build on top of."
Depending on the scenario and context, either of them could be correct.
LoC is a terrible metric for comparing productivity of different developers, even before you get to Goodhart's Law.
OTOH, for a given developer to implement a given feature in a given system, at the end of the day, some amount of code has to be written.
If a particular developer finds that AI lets him write code comparable to what he would have written, in lieu of the code he would have written, but faster than he can do it alone, then looking at lines written might actually be meaningful, just in that context.
I also feel like it makes me more productive but measuring software engineering productivity is famously difficult. If there was an easy way to measure it, managers at bigco would have employed it with abandon years ago.
I just want some externally verifiable numbers. If AI is a 10x improvement, we should be seeing new operating systems. If it’s 5x we should see new game engines. If it’s 2x we should see massive amounts of new features for popular open source projects.
If it’s less than that, then it’s more like adding syntax highlighting or moving from Java to Ruby on Rails. Both of those were nice, but people weren’t breathlessly shouting about being left behind.
I think it goes further than this. Some people - some developers, even - do not _like_ programming computers. In fact, many hate it. Those people welcome the LLM agent stuff because it delivers the end product without going through the necessary pain (from their pov) of programming.
While I believe this may be true, there are also just people that get more reward from building than from the act of writing code. That doesn't mean they hate writing code, but that the building comes first. I count myself in that camp.
If I can build better/faster with reasonably equal quality, I'll trade off the joy of programming for the joy of more building, of more high level problem solving and thinking, etc.
I've also seen the opposite: those that derive more joy from the programming and the cool engineering than from the product. And you see the opposite behavior from them, of course--such as selecting a solution that's cool and novel to build, rather than the simple, boring, but better alternative.
I often find this type of engineer rather frustrating to work with, and coincidentally, they seem to be the most anti-AI type I've encountered.
Yeah, that's pretty much what I think too. I'm much more of the latter type you mention, but I think I have the enough acumen to be practical most times.
Its always been the case that engineers come in many flavors, some more and some less business-inclined. The difference with AI imo is that it will (or already is) putting its trillion-dollar finger on the scale, such that there is less patience and space for people like me, and more for people like you.
I see a lot more insecurity from people who refuse to use AI coding tools. My teammates amd I use this stuff all the time, and it's not making a statement, it's just an easier path sometimes.
I like OP's representation, but I feel like a lot of people arent saying 'LLMs are the bomb dot com _right now_' (though some are), but rather the trend is evident: these things will keep getting better, and the writing is on the wall.
Personally I think the rate of improvement will plateau: in my experience software inevitably becomes less about tech and more about the interpersonal human soup of negotiating requirements, needs, contradiction, and feedback loops, at lot of which is not signal accessible to a text-in-text-out engine.
I feel no strong need to convince others. I've been seeing major productivity boosts for myself and others since Sonnet 3.5. Maybe for certain kinds of projects and use cases it's less good, maybe they're not using it well; I dunno. I do think a lot of these people probably will be left behind if they don't adopt it within the next 3 years, but that's not really my problem.
Until some days / weeks ago, LLM's for coding was more hype than actually real code producing. That is gone now. They clearly leveled up, things will not be the same anymore. And of course this is not just for coding, this is just the beginning. A month ago it really seemed that the models were hitting a complexity wall and that the architecture would need to be improved. Not anymore.
It's different this time. You can see that at the same time, they are finally, definitely solving hard mathematical problems. They passed the phase of just being like good search engines to being actual generators of new data from their generalizations. I can give you a simple example. Any code they generated before brought frustration and they would loop with feedback. Now they actually produce "human level" code.
The anti-LLM side seems much more insecure. Pro-LLM influencers are sometimes corny, but it's sort of like any other influencer, they are incentivized to make everything sound exciting to get clicks. Nobody was complaining about 3d printer influencers raving about how printing replacement dishwasher parts was going to change everything.
LLMs have also become kind of a political issue, except only the "anti" side even really cares about it. Given that using and prompting them is very much a garbage in/garbage out scenario, people let their social and political biases cloud their usage, and instead of helping it succeed, they try to collect "gotcha" moments, which doesn't reflect the workflow of someone using an LLM productively.
I see it only as a threat to those who have a deep hook into their role as a SWE.
If as a SWE you see the oncoming change and adapt to it, no issue.
If as a SWE you see the enablement of LLMs as an existential threat, then you will find many issues, and you will fail to adapt and have all kind of issues related to it.
This is a fun piece to dissect because it's self-aware about being uncharitable, yet still commits the very sin it's criticizing.
The author's central complaint is that LLM evangelists dismiss skeptics with psychological speculation ("you're afraid of being irrelevant"). Their response? Psychological speculation ("you're projecting insecurity about your coding skills").
This is tu quoque dressed up as insight. Fighting unfounded psychoanalysis with unfounded psychoanalysis doesn't refute anything. It just levels the playing field of bad arguments.
The author gestures at this with "I am still willing to admit I am wrong" but the bulk of the piece is vibes-based counter-psychoanalysis, not engagement with evidence.
It's a well-written "no u" that mistakes self-awareness ("I know this isn't charitable") for self-correction.
This is also bad evangelism, but on opposite side.
Just because LLMs don't work for you outside of vibe-coding, doesn't mean it's the same for everyone.
> LLM evangelists - are you willing to admit that you just might not be that good at programming computers?
Productive usage of LLMs in large scale projects become viable with excellent engineering (tests, patterns, documentation, clean code) so perhaps that question should also be asked to yourself.
It starts from the premise that the author finds LLMs are good for limited, simple tasks with small contexts and clearly defined guidelines, specifically not good for vibe-coding.
And the author literally mentions that they aren't making universal claims about LLMs, but just speaking from personal experience.
"And it made me think - why are these people so insistent, and hostile? Why can't they live and let live? Why do they need to convince the rest of us?"
Same could be said about the anti-AI crowd.
I'm glad the author made the distinction that he's talking about LLMs, though, because far too many people these days like to shout from the rooftops about all AI being bad, totally ignoring (willfully or otherwise) important areas it's being used in like cancer research.
> You see a lot of accomplished, prominent developers claiming they are more productive without it.
Demonstrably impossible if you’re actually properly trying to use them in non-esoteric domains. I challenge anyone to very honestly showcase a non-esoteric domain in which opus4.5 does not make even the most experienced developer more productive.
How would one set this sort of test up? I surely have example domains where LLMs routinely do poorly (for example, custom bazel rules and workspaces), but what would constitute a "showcase" here?
To change my
mind I’ll be satisfied with a thorough description of the domain and ideally a theory on why it does poorly in that domain. But we’re
not talking LLMs here, we’re talking opus4.5 specifically.
A theory besides... not enough training data? Is it even possible to formulate a coherent theory about this? I'm talking about customizing a widely-used build system, not exactly state-of-the-art cryptography. What could I possibly say that you wouldn't counter with "skill issue" (which goes back to the author's point)?
If you say it's demonstrably impossible that someone can't be made more productive with opus4.5, then it should probably be up to you to demonstrate impossibility.
"LLM evangelists - are you willing to admit that you just might not be that good at programming computers? Maybe you once were. Maybe you never were."
lol. is this supposed to be like some sort of "gotcha"! yes? like maybe i am a really shitty programmer and always just wanted to hack things together. what it has allowed me to do is prevent burnout to some extent, outsource the "boring" parts and getting back to building things i like.
also getting tired of these extreme takes but whatever, it's #1 so mission accomplished. llms are neither this or that. just another tool in the toolbox, one that has been frustrating in some contexts and a godsend in others and part of that process is figuring out where it excels and doesn't.
hmm, maybe you are not as good at using llms as you think then? lol jk.
i mean if you have imposter syndrome then this feeling will always be prevalent. how do you know what you are good at or not? i might be competent enough to have progressed this far in my career as in "results", but comparison to people i consider "good" devs always puts in that doubt.
i guess it strikes a chord when someone in the same breath of claiming to be open minded makes a backhand comment where people who like llms might just must be a shitty programmer or whatever. i get the point, but that line doesn't quite land the way you think it does.
I'm dubbing this "podcast driven development" because so many of them aren't building things to build things, they just want to _have built something_ so they can go on podcasts and talk about how great it is.
For what it's worth, I think most of them are genuine when they say they're seeing 10X gains,they just went from, like, a 0.01X engineer (centi-swe) to to a 0.1X engineer (deci-swe).
I somewhat agree with this poster. However, I think the unfortunate reality of programming for money is that a mediocre programmer that pumps out millions of lines of slop that seems to drive the business forward and manages to hide disastrous bugs until after the contract / promotion cycle is over will get further ahead than the more competent programmer that delivers better, less buggy, less spaghetti code.
I mean, isn't driving the business forward really what matters (outside of academia, open source, and other such endeavors). We live in a hyper competitive market. All else being equal, if company A can produce "millions of lines of slop", constantly living on the knife-edge of disaster but not falling over it, they will beat company B that artificially slows themselves down. Up until the point company A implodes, but that's not necessarily a given if pre-LLM companies are any indication.
Huh? Where did I say that's what I like? I'm just trying to discuss for discussion's sake. Personally, I want a world that rewards the people who put their thought, care, and craftsmanship into something more than those that don't. In order to live in that world, I think we need to discuss the parts (maybe the whole) that don't and why that might be.
I don't mind weighing in as someone who could fairly be categorized as both an LLM evangelist and "not an experienced dev".
It's a lot like why I've been bullish on Tesla's approach to FSD even as someone who owned an AP1 vehicle that objectively was NOT "self-driving" in any sense of the word: it's less about where the technology is right now, or even the speed the technology is currently improving at, and more about how the technology is now present to enable acceleration in the rate of improvement of performance, paired with the reality of us observing exactly that. Like FSD V12 to V14, the last several years in AI can only be characterized as an unprecedented rate of improvement, very much like scientific advancement throughout human society. It took us millions of years to evolve into humans. Hundreds of thousands to develop language. Tens of thousands to develop writing. Thousands to develop the printing press. Hundreds to develop typewriters. Decades to develop computers. Years to go from the 8086 to the modern workstations of today. The time horizon of tasks AI agents can now reliably perform is now doubling every 4 months, per METR.
Do frontier models know more than human experts in all domains right now? Absolutely not. But they already know far more than any individual human expert outside that human's domain(s) of expertise.
I've been passionate about technology for nearly two decades, working in the technology industry for close to a decade. I'm a security guy, not a dev. I have over half a dozen CVEs and countless private vuln disclosures. I can and do write code myself - I've been writing scripts for various network tasks for a decade before ChatGPT ever came into existence. That said, it absolutely is a better dev than me. But specialized harnesses paired with frontier models are also better security engineers than I am, dollar for dollar versus my cost. They're better pentesters than me, for the relative costs. These statements were not true at all without accounting for cost two years ago. Two years from now, I am fully expecting them to just be outright better at security engineering, pentesting, SCA than I am, without accounting for cost, yet I also expect they will cost less then than they do now.
A year ago, OpenAI's o1 was still almost brand new, test-time compute was this revolutionary new idea. Everyone thought you needed tens of billions to train a model as good as o1, it was still a week before Deepseek released R1.
Now, o1's price/performance seems like a distant bad dream. I had always joked that one quarter in tech saw as much change as like 1 year in "the real world". For AI, it feels more like we're seeing more change every month than we do every year in "the real world", and I'd bet on that accelerating, too.
I don't think experienced devs still preferring to architect and write code themselves are coping at all. I still have to fix bugs in AI-generated code myself. But I do think it's short sighted to not look at the trajectory and see the writing on the wall over the next 5 years.
Stanford's $18/hr pentester that outperforms 9/10 humans should have every pentester figuring out what they're going to be doing when it doubles in performance and halves in cost again over the next year, just like human Uber drivers should be reading Motortrend's (historically a vocal critic of Tesla and FSD) 2026 Best Driver Assistance System and figuring out what they're going to do next. Experienced devs should be looking at how quickly we came from text-davinci-003 to Opus 4.5 and considering what their economic utility will look like in 2030.
LLMs are really great at copy/pasting answers from stack overflow and fitting them to work in a given system. If your work is outside what is answerable on stack overflow you're going to end up fighting the results constantly.
Front end pages like a user settings page? Done. One shottable.
Nuanced data migration problems specific to your stack? You're going to be yelling at the agent.
> LLM evangelists - are you willing to admit that you just might not be that good at programming computers? Maybe you once were. Maybe you never were.
A bit harsh considering that many of us used knowledge bases like SO for so long to figure out new problems that we were confronting.
From my heavy experience using every frontier model for a year now, LLMs are actually probably much, much better at nuanced data migration problems specific to your stack than at a frontend user settings page. (Though still pretty good at both. And the user settings page will work, sure.)
Can we just agree that both the pro- and anti-llm faction mostly contribute noise? And go back to discuss actual achievements?
It's trivial to share coding sessions, be they horrific or great. Without those, you're hot air on the internet, independent of whatever specific opinions on LLMs you voice.
LLMs can produce better code for languages and domains I’m not proficient in, at a much faster rate, but damn it’s rare I look at LLM output and don’t spot something I’d do measurably better.
These things are average text generation machines. Yes you can improve the output quality by writing a good prompt that activates the right weights, getting you higher quality output. But if you’re seeing output that is consistently better than what you produce by hand, you’re probably just below average at programming. And yes, it matters sometimes. Look at the number of software bugs we’re all subjected to.
And let’s not forget that code is a liability. Utilizing code that was “cheap” to generate has a cost, which I’m sure will be the subject of much conversation in the near future.
Funny... seems like about half of devs think AI writes good code, and half think it doesn't. When you consider that it is designed to replicate average output, that makes a lot of sense.
So, as insulting as OP's idea is, it would make sense that below-average devs are getting gains by using AI, and above-average devs aren't. In theory, this situation should raise the average output quality, but only if the training corpus isn't poisoned with AI output.
I have an anecdote that doesn't mean much on its own, but supports OP's thesis: there are two former coworkers in my linkedin feed who are heavy AI evangelists, and have drifted over the years from software engineering into senior business development roles at AI startups. Both of them are unquestionably in the top 5 worst coders I have ever worked with in 15 years, one of them having been fired for code quality and testing practices. Their coding ability, transition to less technical roles, and extremely vocal support for the power of vibe coding definitely would align with OP's uncharitable character evaluation.
Some seniors love to bikeshed PRs all day because they can do it better but generally that activity has zero actual value. Sometimes it matters, often it doesn't.
Stop with the "I could do this better by hand" and ask "is it worth the extra 4 hours to do this by hand, or is this actually good enough to meet the goals?"
There's an interesting aspect to the LLM debt being taken on though in that I'm sure some are taking it on now in the bet/hopes that further advancements in LLMs will make it more easily addressable in the future before it is a real problem.
There's "okay for now" and then there's "this is so crap that if we set our bar this low we'll be knee deep in tech debt in a month".
A lot of LLM output in the specific areas _I_ work in is firmly in that latter category and many times just doesn't work.
Just like writing assembly is today.
The first iteration of Claude code is usually a big over-coded mess, but it's pretty good at iterating to clean it up, given proper instruction.
The worst case I remember happened a few months ago when a staff (!) engineer gave a presentation about benchmarks they had done between Java and Kotlin concurrency tools and how to write concurrent code. There was a very large and strange difference in performance favoring Kotlin that didn't make sense. When I dug into their code, it was clear everything had been generated by a LLM (lots of comments with emojis, for example) and the Java code was just wrong.
The competent programmers I've seen there use LLMs to generate some shell scripts, small python automations or to explore ideas. Most of the time they are unimpressed by these tools.
So, in short, LLMs write better code than I do. I'm not alone.
BUT
An LLM can write a PNG decoder that works in whatever language I choose in one or a few shots. I can do that too, but it will take me longer than a minute!
(and I might learn something about the png format that might be useful later..)
Also, us engineers can talk about code quality all day, but does this really matter to non-engineers? Maybe objectively it does, but can we convince them that it does?
In short: when you produce the PNG decoder, and are satisfied with it, it's because you don't have a good reason to care about the code quality.
> Maybe objectively it does, but can we convince them that it does?
I strongly doubt it, and that's why articles like TFA project quite a bit of concern for the future. If non-engineers end up accepting results from a low-quality, not-quite-correct system, that's on them. If those results compromise credentials, corrupt databases etc., not so much.
What I got was an absolute mess that did not work at all. Perhaps this was because, in retrospect, BMP is not actually all that simple, a fact that I discovered when I did write a BMP decoder by hand. But I spent equal time vibe coding and real coding. At the end of the real coding session, I understood BMP, which I see as a benefit unto itself. This is perhaps a bit cynical but my hot take on vibe coders is that they place little value on understanding things.
That's hilarious LLM code is always very bad. It's only merit is it occasionally works.
> LLMs can produce better code for languages and domains I’m not proficient in.
I am sure that's not true.
We should feed the output code back in to get even better code.
It's let me apply my general knowledge across domains, and do things in tech stacks or languages I don't know well. But that has also cost me hours debugging a solution I don't quite understand.
When working in my core stack though it's a nice force multiplier for routine changes.
what's your core stack?
- it adds superfluous logic that is assumed but isn’t necessary
- as a result the code is more complex, verbose, harder to follow
- it doesn’t quite match the domain because it makes a bunch of assumptions that aren’t true in this particular domain
They’re things that can often be missed in a first pass look at the code but end up adding a lot of accidental complexity that bites you later.
When reading an unfamiliar code base we tend to assume that a certain bit of logic is there for a good reason, and that helps you understand what the system is trying to do. With generative codebases we can’t really assume that anymore unless the code has been thoroughly audited/reviewed/rewritten, at which point I find it’s easier to just write the code myself.
Coding aside, LLM's aren't very good at following nice practices in general unless explicitly prompted to. For example if you ask an LLM to create an error modal box from scratch, will it also implement the ability to select the text, or being able to ctrl c to copy the text, or perhaps a copy message button? Maybe this is a bad example, but they usually don't do things like this unless you explicitly ask them to. I don't personally care too much about this, but I think it's noteworthy in the context of lay people using LLM's to vibe code.
even though this statement does not mathematically / statistically make sense - vast majority of SWEs are “below average.” therein lies the crux of this debate. I’ve been coding since the 90’s and:
- LLM output is better than mine from the 90’s
- LLM output is better than mine from early 2000’s
- LLM output is worse than any of mine from 2010 onward
- LLM output (in the right hands) is better than 90% of human-written code I have seen (and I’ve seen a lot)
This is absolutely not true lol, as anyone who's worked with a fabled 10X engineer will tell you. It's like saying the best civil engineer is the one that builds the most bridges.
The best code looks real boring.
Simon Wilson (known for Django) has been doing a lot of LLM evangelism on his blog these days. Antirez (Redis) wrote a blog post recently with the same vibe.
I doubt they are not good programmers. They are probably better than most of us, and I doubt they feel insecure because of the LLMs. Either I'm wrong, or there's something more to this.
edit: to clarify, I'm not saying Simon and Antirez are part of the group the article criticizes (the hostile LLM evangelists). Although the article does generalize to all LLM evangelists at least in some parts and Simon did react to this here. For these reasons, I haven't ruled him out as a target of this article, at least partly.
By now we at least agree that stochastical parrots can be useful. It would be nice if the debate now was less polarized so we could focus on what makes them work better for some and worse for others other than just expectations.
And yeah, as I laid out in the article (that of course, very few people actually read, even though it was short...), I really don't mind how people make code. It's those that try so hard to convince the rest of us I find very suspect.
The article is against the set of LLM evangelists who are hostile towards the skeptics.
I 100% agree with the part that basically says fuck you to them.
However, explaining the hostile part with there being the feeling of insecurity (which is plausible but would need evidence) is not fully convincing and it seems dangerous to accept this conclusion and stop looking for the actual reasons this quickly.
And the fact that there are actually good programmers persuaded that LLMs help them weakens the "insecurity" argument quite a bit, at least as the only explanation.
As someone currently pretty much hostile to LLMs, I'm quite interested in what's currently at play but I'm suspicious of claims that initially feel good but are not strongly backed.
Like, if these hostile people were actually shills, we would want to know this and not have closed the eyes too early because of some explanation that felt good, right? Or any actual reason.
This article just functions as flamebait for people who use LLMs to implement whole features to argue the semantics of "vibe coding". All while everyone is ignoring the writing on the wall. That we will soon have boxes going through billions of tokens every second. At that point slopcoding WILL be productive, but only if you build up the skill to differentiate yourself from the top 10% of prompters.
Also they didn't adopt the your-career-is-ruined-if-you-don't-get-on-board tone that is sickeningly pervasive on LinkedIn. If you believe that advice and give up on being someone who understands code, you sure aren't gonna write Redis or Django.
I saw a version of this yesterday where a commenter framed LLM-skepticism as a disappointing lack of "hacker" drive and ethos that should be applied to making "AI" toolchains work.
As you might guess, I disagreed: The "hacker" is not driven just by novelty in problems to solve, but in wanting to understand them on more than a surface layer. Messing with kludgy things until they somehow work is always a part of software engineering... but the motive and payoff comes from knowing how things work, and perceiving how they could work better.
What I "fear" from LLMs-in-coding is that they will provide an unlimited flow of "mess around until it works" drudgery tasks with none of the upside. The human role will be hammering at problems which don't really have a "root cause" (except in a stochastic sense) and for which there is never any permanent or clever fix.
Would we say someone is "not really an artist" just because they don't want to spend their days reviewing generated photos for extra-fingers, circling them, and hitting the "redo" button?
We have a hard enough time finding juniors (hell, non-juniors) that know how to program and design effectively.
The industry jerking itself off over Leetcode practice already stunted the growth of many by having them focus on rote memorization and gaming interviews.
With ubiquitous AI and all of these “very smart people” pushing LLMs as an alternative to coding, I fear we’re heading into an era where people don’t understand how anything works and have never been pushed to find out.
Then again, the ability of LLMs to write boilerplate may be the reset that we need to cut out all of the people that never really had an interest in CS that have flocked to the industry over the last decade or so looking for an easy big paycheck.
I had assumed most of them had either filtered out at some stage (an early one being college intro CS classes), ended up employed somewhere that didn't seem to mind their output, or perpetually circle on LinkedIn as "Lemons" for their next prey/employer.
My gut feeling is that messy code-gen will increase their numbers rather than decrease them. LLMs make it easier to generate an illusion of constant progress, and the humans can attribute the good parts of the output to themselves, while blaming bad-parts on the LLM.
No.
or
no, you, as an LLM evangelist, are not not willing to admit this?
"Why are you using Go? Rust is best! You should be using that!" "Don't use AWS CDK, use Terraform! Don't you know anything?"
https://knowyourmeme.com/videos/433740-just-coffee-black
> want to feel normal, to walk around and see that most other people made the same choice they made
In technology, the historical benefits of evangelizing your favorite technology might just be that it becomes more popular and better supported.
Even though LLMs may or may not follow the same path, if you can get your fellow man on-board, then you'll have a shared frame of reference, and someone to talk through different usage scenarios.
Yeah I find they are useful for large sweeping changes, introducing new features and stuff, mostly because they write a lot of the boilerplate, granted with some errors. But for small fiddly changes they suck, you will have a much easier time doing these changes your self.
I work a lot with doctors (writing software for them), I am very envious of their system of specialisation, eg this dude did is such and such a specialist - he knows about it, listen to him. IT seems to be anyone who talks the loudest has a podium, separating the wheat from the chaff is difficult. One day we will have a system of qualifications I hope, but it seems a long way off.
I think that coding assistants tend to quite good as long as what you ask is close to the training data.
Anything novel and the quality if falling off rapidly.
So, if you are like Antirez and ask for a Linenoize improvement that has already be seen many times by the LLM at training time, the result will seem magical, but that is largely an illusion, IMO.
We're well past ad nauseum now. Let's talk about anything else.
Let's make it more of a category thing: when AI shows itself responsible for a new category of life-saving technique, like a cure for cancer or Alzheimer's, then I'd have to reconsider.
(And even then, it will be balanced against rising sea levels, extinctions, and other energy use effects.)
The people who were the best at something don't necessarily be the best at a new paradigm. Unlearning some principles and learning new ones might be painful exercise for some masters.
Military history has shown that the masters of the new wave are not necessarily the masters of the previous wave we see the rise and downfall of several civilizations from Roman to Greek for being too sure of their old methods and old military equipments and strategy.
"you'll be left behind if you don't learn crypto" with crypto
or
"you'll be left behind if you don't learn how to drive" with cars
One of those statements is made in good faith, and the other is made out of insecurity. But we'll probably only really be able to tell looking backwards.
Of course we need a few people to get wildly overexcited about new possibilities, so they will go make all the early mistakes which show the rest of us what the new thing can and cannot actually do; likewise, we need most of us to feel skeptical and stick to what already works, so we don't all run off a cliff together by mistake.
Can’t speak for others but that’s not what I’d understand (or do) as vibecoding. If you’re babysitting it every inch of the way then yeah sure I can see how it might not be productive relative to doing it yourself.
If you’re constantly fighting the LLM because you have a very specific notion of what each line should look like it won’t be a good time.
Better to spec out some assumptions, some desired outcomes, tech to be used, maybe the core data structure, ask the llm what else it needs to connect the dots, add that and then let it go
I am curious why the author doesn't think this saves them time (i.e. makes them more productive).
I never had terribly high output as a programmer. I certainly think LLMs have helped increased the amount of code that I can write, net total, in a year. Not to superhuman levels or even super-me levels, just me++.
But, I think the total time spent producing code has gone down to a fraction and has allowed me more time to spend thinking about what my code is meant to solve.
I wonder about two things: 1. maybe added productivity isn't going to be found in total code produced, because there is a limit on how much useful code can be produced that is based on external factors 2. do some devs look at the output of an LLM and "get the ick" because they didn't write it and LLM-code is often more verbose and "ugly", even though it may work? (this is a total supposition and not an accusation in any way. i also understand that poorly thought out, overly verbose code comes with problems over time)
> even though it may work?
The first of those is about taste, and it's real, and engineers with bad taste write unstable buggy systems.
The second of those is about priority. If all you want is functional code, any old thing will do. That's what I do for one-off scripts. But if you plan to support the code at 2am when exposed to production requests on the internet, you need to understand it, which is about legibility and coherence.
I hope you do have taste, and I hope you value more than simple "it works" tests. But it might be worth looking there for why some struggle with LLM output.
For what it's worth, I use coding agents all the time, but almost never accept their output verbatim outside of boilerplate code.
To answer my own question, if you can pump out features faster but turn around and spend more time on bugs than you do previously then your productivity is likely net neutral.
There is a reason LoC as a measure of productivity has been shunned from the industry for many, many years.
To try and give an example, say that you want to make a module that transforms some data and you ask the LLM to do it. It generates a module with tons of single-layer if-else branches with a huge LoC. Maybe one human dev looks at it and says, "great this solves my problem and the LoC and verbosity isn't an issue even though it is ugly". Maybe the second looks at it and says, "there's definitely some abstraction I can find to make this easier to understand and build on top of."
Depending on the scenario and context, either of them could be correct.
OTOH, for a given developer to implement a given feature in a given system, at the end of the day, some amount of code has to be written.
If a particular developer finds that AI lets him write code comparable to what he would have written, in lieu of the code he would have written, but faster than he can do it alone, then looking at lines written might actually be meaningful, just in that context.
If it’s less than that, then it’s more like adding syntax highlighting or moving from Java to Ruby on Rails. Both of those were nice, but people weren’t breathlessly shouting about being left behind.
If I can build better/faster with reasonably equal quality, I'll trade off the joy of programming for the joy of more building, of more high level problem solving and thinking, etc.
I've also seen the opposite: those that derive more joy from the programming and the cool engineering than from the product. And you see the opposite behavior from them, of course--such as selecting a solution that's cool and novel to build, rather than the simple, boring, but better alternative.
I often find this type of engineer rather frustrating to work with, and coincidentally, they seem to be the most anti-AI type I've encountered.
Its always been the case that engineers come in many flavors, some more and some less business-inclined. The difference with AI imo is that it will (or already is) putting its trillion-dollar finger on the scale, such that there is less patience and space for people like me, and more for people like you.
Note I also think AI is bad for philosophical/ethical reasons.
Personally I think the rate of improvement will plateau: in my experience software inevitably becomes less about tech and more about the interpersonal human soup of negotiating requirements, needs, contradiction, and feedback loops, at lot of which is not signal accessible to a text-in-text-out engine.
LLMs have also become kind of a political issue, except only the "anti" side even really cares about it. Given that using and prompting them is very much a garbage in/garbage out scenario, people let their social and political biases cloud their usage, and instead of helping it succeed, they try to collect "gotcha" moments, which doesn't reflect the workflow of someone using an LLM productively.
If as a SWE you see the oncoming change and adapt to it, no issue.
If as a SWE you see the enablement of LLMs as an existential threat, then you will find many issues, and you will fail to adapt and have all kind of issues related to it.
The author's central complaint is that LLM evangelists dismiss skeptics with psychological speculation ("you're afraid of being irrelevant"). Their response? Psychological speculation ("you're projecting insecurity about your coding skills").
This is tu quoque dressed up as insight. Fighting unfounded psychoanalysis with unfounded psychoanalysis doesn't refute anything. It just levels the playing field of bad arguments.
The author gestures at this with "I am still willing to admit I am wrong" but the bulk of the piece is vibes-based counter-psychoanalysis, not engagement with evidence.
It's a well-written "no u" that mistakes self-awareness ("I know this isn't charitable") for self-correction.
Just because LLMs don't work for you outside of vibe-coding, doesn't mean it's the same for everyone.
> LLM evangelists - are you willing to admit that you just might not be that good at programming computers?
Productive usage of LLMs in large scale projects become viable with excellent engineering (tests, patterns, documentation, clean code) so perhaps that question should also be asked to yourself.
The article starts from the premise that LLMs are only good for vibe-coding.
It starts from the premise that the author finds LLMs are good for limited, simple tasks with small contexts and clearly defined guidelines, specifically not good for vibe-coding.
And the author literally mentions that they aren't making universal claims about LLMs, but just speaking from personal experience.
Same could be said about the anti-AI crowd.
I'm glad the author made the distinction that he's talking about LLMs, though, because far too many people these days like to shout from the rooftops about all AI being bad, totally ignoring (willfully or otherwise) important areas it's being used in like cancer research.
Demonstrably impossible if you’re actually properly trying to use them in non-esoteric domains. I challenge anyone to very honestly showcase a non-esoteric domain in which opus4.5 does not make even the most experienced developer more productive.
If you say it's demonstrably impossible that someone can't be made more productive with opus4.5, then it should probably be up to you to demonstrate impossibility.
Not enough training data couldn’t be the problem - Bazel is not an esoteric domain. Unless you’re trying to do something esoteric.
lol. is this supposed to be like some sort of "gotcha"! yes? like maybe i am a really shitty programmer and always just wanted to hack things together. what it has allowed me to do is prevent burnout to some extent, outsource the "boring" parts and getting back to building things i like.
also getting tired of these extreme takes but whatever, it's #1 so mission accomplished. llms are neither this or that. just another tool in the toolbox, one that has been frustrating in some contexts and a godsend in others and part of that process is figuring out where it excels and doesn't.
I use LLMs for things I am not good at. But I also know I am not good at them.
No one is good at everything. 100% fine.
i mean if you have imposter syndrome then this feeling will always be prevalent. how do you know what you are good at or not? i might be competent enough to have progressed this far in my career as in "results", but comparison to people i consider "good" devs always puts in that doubt.
i guess it strikes a chord when someone in the same breath of claiming to be open minded makes a backhand comment where people who like llms might just must be a shitty programmer or whatever. i get the point, but that line doesn't quite land the way you think it does.
For what it's worth, I think most of them are genuine when they say they're seeing 10X gains,they just went from, like, a 0.01X engineer (centi-swe) to to a 0.1X engineer (deci-swe).
It's a lot like why I've been bullish on Tesla's approach to FSD even as someone who owned an AP1 vehicle that objectively was NOT "self-driving" in any sense of the word: it's less about where the technology is right now, or even the speed the technology is currently improving at, and more about how the technology is now present to enable acceleration in the rate of improvement of performance, paired with the reality of us observing exactly that. Like FSD V12 to V14, the last several years in AI can only be characterized as an unprecedented rate of improvement, very much like scientific advancement throughout human society. It took us millions of years to evolve into humans. Hundreds of thousands to develop language. Tens of thousands to develop writing. Thousands to develop the printing press. Hundreds to develop typewriters. Decades to develop computers. Years to go from the 8086 to the modern workstations of today. The time horizon of tasks AI agents can now reliably perform is now doubling every 4 months, per METR.
Do frontier models know more than human experts in all domains right now? Absolutely not. But they already know far more than any individual human expert outside that human's domain(s) of expertise.
I've been passionate about technology for nearly two decades, working in the technology industry for close to a decade. I'm a security guy, not a dev. I have over half a dozen CVEs and countless private vuln disclosures. I can and do write code myself - I've been writing scripts for various network tasks for a decade before ChatGPT ever came into existence. That said, it absolutely is a better dev than me. But specialized harnesses paired with frontier models are also better security engineers than I am, dollar for dollar versus my cost. They're better pentesters than me, for the relative costs. These statements were not true at all without accounting for cost two years ago. Two years from now, I am fully expecting them to just be outright better at security engineering, pentesting, SCA than I am, without accounting for cost, yet I also expect they will cost less then than they do now.
A year ago, OpenAI's o1 was still almost brand new, test-time compute was this revolutionary new idea. Everyone thought you needed tens of billions to train a model as good as o1, it was still a week before Deepseek released R1.
Now, o1's price/performance seems like a distant bad dream. I had always joked that one quarter in tech saw as much change as like 1 year in "the real world". For AI, it feels more like we're seeing more change every month than we do every year in "the real world", and I'd bet on that accelerating, too.
I don't think experienced devs still preferring to architect and write code themselves are coping at all. I still have to fix bugs in AI-generated code myself. But I do think it's short sighted to not look at the trajectory and see the writing on the wall over the next 5 years.
Stanford's $18/hr pentester that outperforms 9/10 humans should have every pentester figuring out what they're going to be doing when it doubles in performance and halves in cost again over the next year, just like human Uber drivers should be reading Motortrend's (historically a vocal critic of Tesla and FSD) 2026 Best Driver Assistance System and figuring out what they're going to do next. Experienced devs should be looking at how quickly we came from text-davinci-003 to Opus 4.5 and considering what their economic utility will look like in 2030.
Front end pages like a user settings page? Done. One shottable.
Nuanced data migration problems specific to your stack? You're going to be yelling at the agent.
> LLM evangelists - are you willing to admit that you just might not be that good at programming computers? Maybe you once were. Maybe you never were.
A bit harsh considering that many of us used knowledge bases like SO for so long to figure out new problems that we were confronting.
It's trivial to share coding sessions, be they horrific or great. Without those, you're hot air on the internet, independent of whatever specific opinions on LLMs you voice.