It's two fold. They're learning and understanding more things, but at a very surface level and without the nuance and ability to actually use the knowledge because they have none of the muscle memory and hard work associated with learning it. You can use AI or the internet to learn the basics of how a gas engine works in a couple of minutes. But you'd be incapable of actually working on a gas engine or designing one. Surface level knowledge gets you surface level functionality. You don't become good at something from surface level knowledge, but you might think you're good at it.

i use claude a lot and i find that it is best applied in domains in which i am already a master. I tried applying it to domain's im unfamiliar with and i found that i produced stuff but as time went on i understood what i produced less and i almost felt like i do after binge watching a netflix show, 2 weeks later i barely remember any of the details. I wonder how much you need to "do" to learn and remember. LLM's give you a shortcut to doing and so you probably aren't learning either. It's like when you watch a professor write a proof and it makes sense while listening to the professor but at home you have difficulty deriving it. LLM's give me the same sort of feeling. I think the way forward is still going to be doing things manually to learn and using LLM's once you've mastered an area and people who don't understand this fact are going to slowly descend down a hill and forget how to depend on their own thinking.

You're right. But I like to add artwork to my presentations. My artistic skills have not advanced beyond 2nd grade. So I'll make a line sketch, and give to AI to "fix" it. The results are nice and I use them. I have no interest in learning how to do art well myself, so using AI for it is appropriate. But I still write my code myself.

You can ask LLMs about high-level techniques, and their answers will usually be good enough. What you can't get from LLMs is the taste and judgment, which you can only obtain by having a strong CS base and coding manually for years. High-level techniques were never a problem. You could Google tens of articles on this topic. They are useless too, it's like learning how to drive a racing bicycle from reading a book. Sure, you will know a lot about nuances, but you will fail miserably when it comes to a real race.

The other day I just wanted to loop through characters in a std::string to copy data to a new string with a few escape characters (sending to peripheral device). Simple enough task for AI. I got a coroutine monstrocity back, with copies to std::array and a range based iterator, since I specified C++23. If I specified C++11, I would have received a: char p = src.data(); while ( p) { … p++; } I had the experience to keep calling out AI to simplify and downgrade the solution to something primitive, which ended up smaller, faster, easier to maintain. Juniors with real world experience would not bother, they’ll take the first working AI result.

Understanding in what ways it can be useful and in what ways it can be counterproductive in long run requires a certain degree of experience itself.

Data cleansing is a terrible use for LLMs if you want reliable data.

I think it varies tremendously from one role to the next. I'm a senior software engineer and LLMs, the way I'm using them, improve almost everything I do. I use them to write most of my code now, but first I spent twenty years writing code before LLMs came into existence and second writing code is like 5% of my job. Most of my job is research, investigation, and architecture. I treat LLMs just like a junior engineer. I give them clearly defined jobs that I could do on my own just fine, that I already spent years doing. The problem here is that students are using LLMs to automate everything BEFORE they become proficient at it themselves. Letting college students use LLMs for homework is like letting kindergarteners use calculators instead of counting on their fingers.

Are you saying you leave it up to the LLM to judge whether your idea is good or not? Are you even human anymore? (I am not saying LLMs can't be a good tool in evaluating ideas. To me, it sounds like you're firing off ideas all over, letting the LLMs judge what's good and what's not. Insane.)

Pity. I recently started a fun activity to rebrush my math my where I tries to solve problems while asking Gemini Live mode for confirmation and suggestions, sometimes step by step. It kinda was fun, like a very patient professor stand right besides you. It was the one of the best math learning experience I've ever had, and you don't even need to send bribe/gift to Gemini to keep you in it's favor. On the other hand, if you ask a LLM to completely finish the work without thinking it through by yourself, then it sounded like cheating, to yourself.

My experience (n=1) is that while I'm definitely lazier on certain tasks, AI has opened up some much more complex tasks. There are many tasks which I still carry out which I don't trust AI with. Maybe it's a result of the codebase I work with being fairly complicated and math heavy, but I'd say the overall outcome for me has been: lazier application on the easy tasks, mind opening on the harder tasks.

I dont think ai is good enough for it coding or any other work once i told ai a problem and he generated an entire solution which i used and it was broken. You should never use ai like it treat is like a helper write a function for code and then ask if everything is correct and if something can improve read documentations understand how its working under the code if everything is correct then only deploy or build.

Respectfully, I disagree. I think there's absolutely a case for AI being encouraged in younger people, and there's room for these tools. I've been leaning on LLMs for side learning in side projects, and it has concretely helped me with conceptual questions about math and Vulkan as I've been trying to learn some graphics basics with side projects. I would grant: I was not the most studious kid, I could definitely stand to learn how to read code a lot more effectively than I do; but I have found being able to ask a computer, "what portions of the Vulkan Programming Guide are less relevant with Vulkan's design changes since the release" pointing me to the dynamic rendering extensions and placing it into context, with inline code and links out to useful blog posts for additional reading, that sort of thing is very helpful. Working on a prototype before I was trying to learn Vulkan, I was using it to explore SDL_GPU's API which definitely had some gaps in its documentation. Granted again, I could have referenced the sample code - I am sure you'll prefer I'd have done that - but it helped to get information about what each piece of the API was doing, and gave reasonable results that made sense and did inform me enough to understand what I was doing, turning much of that into an interactive learning of basic GPU programming for graphics. Where the AI hallucinated, it was often on things like method names, which I was able to read through and find the methods it was intending to name. (This only occurred once or twice when I was learning). Unrelated, but adding the C macro syntax and nesting macros, which I could have an LLM explain inline and link the GNU manual. Never got that taught to me in a C course. Man, computers are complicated! These have not replaced textbooks; I have been using them alongside textbooks and handwriting code for practice, and they work as a very good complement. I also sometimes use them to unblock me - I don't know CMake very well and lean on AI to do CMake, so I can focus on learning C++ and graphics, which is my primary objective right now. I would add too, I have for fun given it prompts about various topics I learned in university, and I often will get answers that are bang-on what I learned in university undergraduate courses - the topics I tried were welfare state taxonomies, distributed systems, disk storage performance, filesystem layouts and internals. Boy, this would've been cool for me as a kid. There's just so much information right there, and pointing you to topics and textbooks a couple questions away, I wish I had these tools. I was a curious kid in a terrible MAGA-esque family that was deeply uncurious about the world, had no knowledge of any advanced subject and basically mocked me for trying to learn more about stuff. And you go to the school library and it's all kids shit, not even an option to try and reach out for more. Now smart kids might be able to go just learn shit very freely and be pointed to textbooks, and go pirate them off some Russian site, and start learning and go tutor themselves, as I'm doing today as an adult. At least knowing myself and knowing if there's another kid like me, I think they would deeply enjoy having a natural language encyclopedia, if we can get it as close to that as possible. I think even with some error inherent, if the tools can be often and directionally correct, that would be a plus. I went to university, and the professors there hallucinated some things so embarrassing it should bar them from teaching, for the standards people hold LLMs to! i.e., sanitizing conspiracy theories that Android records all language through the microphone therefore iOS is better, Apple Silicon is more battery efficient because it is RISC and not CISC. Got a terrible history of computer graphics technology you'd know was slanted if you watch the 8 Bit Guy on YouTube. Rubbish. The thing that worries me, and what this article really talks about, are the kids that just don't give a shit. They are not new - when I went to high school, before AI, stupid kids would copy code off the internet. I think AI probably makes it worse because it makes it harder to call out and enforce against it, and agreed, that should be stopped. But to me, that is mainly a cultural problem. Too many Americans are completely uncurious and just spout garbage; there are a lot of kids who grow up in that cesspool and are going to grow up uncurious, and then AI acts as a shortcut rather than a vehicle of curiosity. And granted, maybe AI is less useful when you are in a structured environment - but the structured environment has its downsides. Even in that environment many of the TAs were clueless and unhelpful, or just too damn busy or already too knowledgeable to meet students where they were at. Again, talk about hallucinations with TAs! Many times in my experience. And that's all to say nothing about getting people to not just do homework but actually go get curious about things and try stuff that isn't required of them. I think there will be some culture that remains curious, and has these tools, will come to grips with where they can help, where they go wrong, how to balance it with other learning methods; and I think they are going to have kids that absorb a lot more knowledge and get to play with topics and learn things, faster, to each kids' interest, perhaps even individualized tutoring at better scale - I hope that is possible. I hope the United States as well, but maybe not, because holy cow our culture and attitudes are plainly terrible these days. Your comment is pretty representative of how most people react if I suggest this or talk about my own experiences I'm describing here. But I hope at least I'm arguing something comprehensive here. There is too little conversation beyond hyperbolic nonsense on the internet; I consider "FUTURE LUDDITE" etc. to be in that realm.

In my experience, AI seems like it’s helping debug problems, but it’s very hard to tell when fictional information starts being added. I’ve wasted a lot of time trying AI suggested solutions that I only realized were pointless when I started asking questions like ”I think this distro is missing a package, could that be the problem?” It would agree and tell me a specific package to download. I’d then ask “could iptables be the problem?” It would agree and give me a specific configuration to change. LLMs can be useful, but I haven’t found a way to use them where I’d be confident in using it to solve technical problems I didn’t already deeply understand.