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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.
Not your handwriting, not your work!
Not to mention, LLMs can be made deterministic if that were actually valuable.
Seems like determinism is just the latest excuse to hate.
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.
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.
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.
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.
I for example, let the AI generate inbetween reports for a hobby project. These reports describe me how the project looks, stages, progress, etc.
I do know plenty about this particular project.
Also i wrote tons and tons of CRUD apps, do I really loose anything if that one quiz i did for learning, i don't understand in detail?
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.
Think about the act of writing with a pen.
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.
Bafflement? Certainly.
Maybe a little sadness at the lack of understanding of, or appreciation for, the creative process? Sure.
Outrage? Nah.
I only see that construction used condescendingly.
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.
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.
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.
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 jest...
If the math teacher is unaware of the difference between "math" and "arithmetic", what are you even talking about?
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 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.
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?