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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 hostile LLM evangelists the article criticizes. 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.

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?

No.

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.

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.

We're well past ad nauseum now. Let's talk about anything else.

"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?"

It takes practice to figure out which things the LLM handles well and how best to present your problems to the LLM to get a good result.

It take luck that the specific things you're trying to get results for are things the LLM actually can handle well.

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 work a lot with doctors (writing software for them), I am very envious of their system of specialisation, eg this dude 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.

Instead of psychoanalyzing each other, people should share concrete examples

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.

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

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.

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 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)

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.

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.

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.

Yeah, this.

I sucked (still sucks?) at it too, I spent countless hours correcting them. And throwing away hours of "work" they made, And even had them nuking the workplace a couple times (thankfully, they were siloed). I still feel like I'm wasting too much time way too often and trying new things constantly.

But I always thought I can learn and improve on this tool and its associated ecosystem as much as the other programming tools and languages and frameworks I learned over the years.

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.

That said, practically speaking, you can just ignore the evangelists and even use it as a signal for people you should ignore.

The unfortunate thing is how it's affecting our careers, since it's apparently so alluring for Management types to buy the hype and start trying to micro manage devs.

Map-reading evangelists, are you willing to admit that you just might not be that good at driving a car? Maybe you once were. Maybe you never were."

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 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.

spend 60% on A.I, 30% on Humans and 10% on operations but I can bet you my sole penny that's not happening - so we know someone is tryna sell us a polished turd as a diamond

It's fear, but of different kind. Those who are most aggressive and pushy about it are those who invested too much [someone else's] money in it and are scared angry investors will come for their hides when reality won't match their expectations.

We need to drop this competition paradigm ASAP.

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.

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.

> You see a lot of accomplished, prominent developers claiming they are more productive without it.

You also see a lot of accomplished, prominent developers claiming they are more productive with it, so I don't know what this is supposed to prove. The inverse argument is just as easy to make and just as spurious.

Or maybe the author is bad at programming AND bad at agentic coding.

That’s more likely than the possibility that all llm evangelists are terrible coders.

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.

I'll remain skeptical and let the technology speak for itself, if it ever does.

What I use LLMs for

I don't use Claude Code, Codex or any other hyped-up, VC-approved, buzzword-compliant, productivity-theater SF tool for actual day-to-day coding. I just use ChatGPT outside of my editor in browser.

The only tasks that I feel LLMs right now can do somewhat-reliably without human supervision include:

1. Search: To search and compare different things; that is boring to do manually. As an example, I checked how M1 Pro compares to M4. ChatGPT crawls the web to compare both the processors on similar specs and provides a conclusion but I need to check and verify the claims it reached by briefly reading the sources.

2. Documentation: I have to do things on time so I offload reading documentations for syntax or how-to-do-it-with-this-library to ChatGPT.

3. Single task scripts: I let ChatGPT create me boring and one-time-use python scripts for trivial tasks.

I don't spend ChatGPT's limited but free GPT-5-(latest-model) tokens for such trivial tasks - I have a special keybinding on my mac that fires up ChatGPT in incognito for me.

But don't get me wrong, I find LLMs pretty useful for PoC-level stuff and I do like instructing my agents to make me a half-baked PoC that makes me feel like a revolutionary SF-founder for an afternoon before I delete the repo and go back to writing real code like a responsible adult. Because nothing says ‘disruptive genius’ like ctrl+z and moving on.

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Why I find vibe coding in production not useful

Like sean goedecke, I feel that using LLMs in production is not useful and (in fact) anti-productive because it gives junior engineer's free pass to push code without actually learning/reading it (Humans often tend to chose the easiest path to accomplish given problem). And due to this, its a pain to review junior PR these days.

I have two reasons why LLMs are not good at production coding:

1. Context: There are enough discussions out on the internet that says LLMs are limited by context. Humans are better because they can handle and work with multiple contexts in mind - an LLMs knows how to solve the user problem, but the engineer knows what business problem this piece of code solves, in which context the code will run, with other process on same service and in which context of other services, what company code idioms to follow while writing code and what will make your pair-programmer think that you are smart.

2. Lack of deterministic output: I would not over-simply by saying its just a probabilistic system but you can feel it right? It would sometimes give correct, decent and sometimes wrong answers for the same prompt. Sometimes it would enlighten you with a very interesting insight/perspective and other times it would be as dumb as a toaster trying to file your taxes - consistently inconsistent (when you reached context rot), yet somehow still sold as “intelligent.”

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A brief rant

My manager fear-mongers me nearly everyday saying LLMs are really good at coding, there are at-most 10 years left to earn as programmer and stuff. The fact he is hasn't touched coding in last 8 years.

He thinks his half-baked trivial ideas translated to code by an overpriced probabilistic system is a breakthrough for his company.

I am non-native english speaker and you would have probably sensed that by now. I often get tasks to write documentations, and every time my manager doesn't understand something, he takes it as a chance to break my confidence saying that you are bad at using LLM for language related tasks because my english is poor.

But (at-least right now) I strongly feel this is just a hype:

- CEOs want to get-rich-quick or they want to increase company profits by "ai-enabling" it and getting salary hikes.

- Companies are hyping this tech to recalibrate engineer salaries that has skyrocketed since covid.

- My manager wants to make me feel replaceable.

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.

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.

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.