There's a time for cognition and a time for just moving quickly and getting shit done.
Downloading a bunch of open source libraries and putting together some project with a bunch of glue code also requires little thinking, but before AI no one really said that was "cognitive surrender". People even got pats on the back... for using other people's work to build something.
I use AI all the time for coding, but a tool that non-deterministically reaches potentially a different output than I would have produced myself is not analogous to typewriters.
It’s not exactly determinism, it’s authenticity. If we rewind time, of course the words would be different. It’s not relevant to my argument.
There’s a reason why the top comments of HN blog posts whine about articles that are clearly written by AI. I use AI constantly as a coding agent, but I care deeply about the authenticity of the words I write intended for other human beings to be my own.
> It’s not exactly determinism, it’s authenticity.
I hope you understand, by so glibly switching from "determinism" to "authenticity" you've just provided more evidence that this is just the latest excuse to hate.
But that aside - what happens in a couple years, when LLMs inevitably improve, and their writing is objectively superior to human writing? And people who just want to read well-written material won't care about the authenticity?
I suspect there will be another glib switch. Because the reality here is just that AI is hated. Full stop.
That's a terrible analogy. It's more like using a typewriter where the job is calligraphy.
I use LLMs at work all the time, and it truly does feel like somebody else coding for me, and me reviewing, cleaning up, and asking for changes. It's really not my work, nor is it my code, but I'm still fine bringing it in.
I also don't use LLMs on my personal code done in my free time because I want my brain to still be fully functional in a few years.
Yes, and all of them involve exercising your mind regularly. I could let LLMs do my personal projects for me and then try to make up the difference with brain teasers, language learning, etc. I choose to exercise my mind because I still love programming, including writing my own code.
I suspect that a lot of people who are going full hog on LLMs aren't actually making up the difference, and are simply offloading their thinking. I choose to not be one of those. I'll use them at work because they make me faster and most of the code is already not mine. It's a means to an end. My personal projects are for the fun and love of programming, not just to have some software in the end.
A more appropriate analogy for what you just described might be writing vs ghost writing.
But it's certainly not handwriting vs typewriter.
Note: there's no implied value judgement here.
My point is that the analogy that LLMs are equivalent to typewriters is fundamentally broken. Using an LLM to create text, and writing using writing tools, are simply completely different activities that just happen to lead to (at least superficially) the same outcomes.
Similarly, downloading a vase and 3D printing it isn't the same as making pottery. Programming a knitting machine to make socks for you isn't knitting. Putting text on paper using a printer isn't calligraphy.
And I'd argue we're going to collectively conclude that assembling software systems using AI isn't coding.
Again: no value judgement. But life isn't always just about outcomes. Process does, in fact, matter, and I think it's important we maintain these distinctions.
It's genuinely amazing and baffling to me that a non-trivial number of people around here don't seem to grok that.
Now think about what a typewriter does in that context.
Now think about what an LLM does in that context.
Can you reason about how those are vastly different tools within the context of writing or communication, so much so that the comparison doesn't actually make any sense?
Hint: it has nothing to do with determinism. It has to do with the nature of the work itself and the role of the person doing it.
As an aside, this whole exchange really is just a perfect encapsulation of the outcome-focused versus process-focused individual.
The outcome-focused person sees words in a document and whether an LLM produced them or a human typed them is a distinction without a difference.
The process-focused person is utterly baffled that anyone could think those are in any way equivalent.
HEH. I just read your comment three times and I definitely don't get it. I think its entire point is to express outrage about not getting it, rather than actually explain it.
When I was in high school, graphing calculators were pretty new. New enough that most teachers had not yet encountered them.
I was a pretty nerdy kid even then, and I saw graphing calculator as 'portable computer' in 1993! So, of course I wanted one.
When our teachers found about them, they banned the use of graphing calculators, some teachers going so far as to only allowing 'four-bangers' (a basic one) for classes lower than Trig (I never got to Calc in high school).
Funny, but your statement reminded me of what one of math teachers used to say: "If you use a graphing calculator, you aren't doing math." It seems really quaint today.
The smart reason graphing calculators are banned is because students are meant to be learning a concept. You don't learn how to graph to pass a test you take a test so you're pressured to learn how to graph.
Same with AI. Having AI generate a for loop doesn't mean you understand the concept of a for loop. You can argue that it's a waste of time to learn about for loops because an AI can write one for you but that's a separate matter.
That's why we use different tools when teaching/learning something vs. doing real work.
Another example is computer algebra systems like Mathematica. It would be self-sabotage to use them on your calculus homework, but they're indispensable to the working mathematician, physicist, or engineer, whose time is wasted doing routine calculations by hand.
"If I hand you a equation to solve, and you type in the numbers, hit enter on your programmatic calculator, and write down Texas Instruments' answer, you are definitely not dong math." <-- would have been my teachers' attitude too.
Throw in "and when the batteries in your calculator die, you'll need to know how to do it by hand!" for good measure. :-)
I think the difference with the calculator analogies is a calculator just speeds up calculations you could do yourself (granted some very slowly). I can’t type in “sally has 35 apples, and wants to give an equal number to each of her 7 friends. how many apples does each get?” I have to know to do 35/7. But an ai will do that problem solving for me, with zero understanding on my part.
I took calc at around the same time. The teacher had us learn to sketch function graphs by hand the old fashioned way, by finding the derivative and looking for critical points, etc.
Then he brought the graphing calculators in and had us plot graphs on those, compare them to our hand sketches, and set domain and range on the graph based on the critical points we found. He emphasized that doing the math beforehand let us use the graphing calculator effectively, and without knowing the math the calculator was useless.
Which sounds like the most sensible voices w.r.t. AI. At least for now. Six months ago I would not trust AI for any but rather menial tasks, but frontier models are able to do more and more of what I do every time I turn around. The TI-83 has been a fixed quantity in terms of capability since the 90s, and it never computed derivatives itself.
I remember a homework where I had to graph some function. I decided use the plotting function of Lotus 1-2-3 for that. The plot worked fine, but didn’t make sense.
I then spent way more time than what I would have spent without the plotter to figure out that a pole and a zero were located right next to eachother.
I had no idea if the teacher had done that on purpose, but it made enough of an impression on me that I still remember it.
The point of math exercises is usually not to get to solution as quickly as possible.
What about writing in C. If you use a compiler its not your code that's running and not your work.
You are setting the bar way to high. We use LLM's for our coding and its most definitely our code and work. How would it be any different than a collogue writing code that you review and commit?
IMO it's fine if you use it to review hand-written code. Vibe-coding (and especially 100% slop projects) on the other hand, is indeed cognitive surrender as it's identical to cheating on homework back then.
Could you not say the same about using a compiler or higher level language or a library you don’t understand or an algorithm you don’t understand or a chip that you don’t understand?
You might be right. I'm a better programmer than most, but I'm nowhere near as good as I could be, and there is still so much I could learn and improve upon. But back when I was saying that "AI is better than most programmers", ~6 months ago, I figured people at my level were still somewhat safe, as the average programmer, even working professionally, is a shockingly low bar. Do not judge the entire profession by Hackernews posters; most of us are in the upper quartile or so.
These days, for small to medium-sized tasks, frontier LLMs are consistently well within that upper quartile. People at my level have a lot to be concerned about, as most of our day-to-day can be automated away. Sure, there's need for judgement and architectural discipline, but in most shops that's done by the people we used to make fun of in the 2000s, who sit in design review meetings and produce box-and-arrow diagrams, not code. Those are now the real bringers of value to a software development company or division.
At a bare minimum, it is irresponsible to ship code that has any sort of security requirement without using AI. Frontier models are able to discover zero days in minutes that would have taken months with human investigators, and if you're not auditing and red-teaming your own code with AI, someone else will use AI to pop it.
You are just not working to a professional standard in 2026 if you're not using AI.
I don't think so. If you were truly better than most, you'd be proud of your work, instead of trying to avoid doing it at all costs, while accusing others who don't do the same of "not working to a professional standard."
Can't write bad code if you don't write code at all, am I right?
I am proud of my work, and I hate working with AI.
But reality is what it is. The economics are what they are. And at the present time, writing code without LLM assistance is a mere handicraft, an idle pastime indulged in as a curiosity. It's like writing games in BASIC on the Commodore 64—no one is doing it anymore for serious stuff. Software engineering is the practice of formulating and implementing procedures to produce software at industrial scales to meet industrial needs. And right now, there is no future for industrial-scale software development without AI. People and companies who refuse to use it will be lapped, in terms of speed, quality, and security, by those who do use it.
> those who refuse to use it will be lapped, in terms of ... quality, and security, by those who do use it.
I uh... I might have burst out laughing. Sorry, but do you really, seriously, believe the machine so fantastic that it is widely called "slop", produces amazing quality outputs?
My observations suggest that frontier models consistently produce correct, high-quality code for small to medium-sized tasks provided the goals are well-specified and testable. Even when they appear to get distracted, they converge on a solution.
So what that means is that you the software engineer need to be doing what you should have been doing all along: start with systems analysis, stepwise refine that into a program specification, then break that into tasks to give to your programmers to implement. Per PRIDE, the sadly forgotten but comprehensive common-sense information systems methodology, the analysis and specification phases should take up the bulk of the time on a project, with actual programming taking up 15% or less. LLMs can drive that time asymptotically towards zero for human developers. The specification is the deliverable. As a former boss of mine put it, you should be able to delete the entire source code repository and get functionally the exact same software back by running the spec through the LLM.
ITIL is at version 5 now - the idea of a process, is not exactly forgotten in the industry.
But you're working with a stochastic system. Even GPU matrix calculations are nondeterministic. It isn't possible, to the delete the entire source code and get exactly the same software back.
And any spec, that is specific and non-ambiguous enough to carefully construct the exact needs of each is... Well, that's code. Not a PBI, not an Epic. It's code.
I said Epic and PBI, not bytes. User Stories are ambiguous. Acceptance Criteria can be argued. And which path in our languages ambiguities that the model will follow, cannot be deterministic by the nature of our hardware.
If you produce a spec that is entirely unambiguous in every way, again, that's code. That's the purpose of a programming language - distilling from the natural ambiguities of natural language, to specificity.
Well, considering I accidentally embarrassed the Anthropic rep at work yesterday by producing better code in an hour, then he did in a day, I'm... Pretty sure I'm going to be safe.
