If you spend time on places that attract newbie programmers (some subreddits focused on game dev or game engines, for example) you’ll see the outcome of “I no longer think you should learn to code.” And it’s not pretty.<p>Many, many posts of people looking for help fixing AI-generated code because the AI got it wrong and they have no idea what the code even does. Much of the time the problem is simply an invented method name that doesn’t exist, a problem that is trivially solved by the error message and documentation. But they say they’ve spent several days or whatever going back and forth with the AI trying to fix it.<p>It’s almost a little sad. If they just take the time to actually learn what they’re doing they’ll be able to accomplish so much more.<p>Now of course people learning the traditional way have these same problems, but they’re debugging <i>code they wrote</i>, not gobbledygook from an AI. It’s also easier to explain the solution to them because they wrote the code, so it tends to be simpler. Several times I’ve pitied someone asking for help with AI code and even when I explained the solution they still didn’t understand it, and I had to just give up on them - I’m not getting paid to help them.
I'm a professor at a small college. I teach intro programming most semesters and we're now moving to using tools like Cursor with no restrictions in upper-level courses.<p>"How do students learn to code nowadays?" - I think about this pretty much all the time.<p>In my intro class, the two main goals are to learn about structured programming (using loops, functions, etc.) and build a mental model of how programs execute. Students should be able to look at a piece of code and reason through what it does. I've moved most of the traditional homework problems into class and lab time, so I can observe the students coding without using AI. The out-of-class projects are now bigger and more creative and include specific steps to teach students how to use AI collaboratively.<p>My upper-level students are now doing more ambitious and challenging projects. What we've seen is that AI moves the difficulty of programming away from remembering details of languages or frameworks, but rewards having a careful, structured development process:<p>- Thinking hard and chatting about the problem and the changes you need to implement before doing anything<p>- Keeping components encapsulated and thinking about interfaces<p>- Controlling the scope of your changes; current AIs work best at the function or class level<p>- Testing and validation<p>- Good manual debugging skills; you can't rely on AI to fix everything for you<p>- General system knowledge: networking, OS, data formats, databases<p>One of my key theories is that AI might lower the value of "computer science" as a standalone major, but will lead to a lot more coding across fields that currently don't use it. The intersection of "not a traditional engineer" and "can work with AI to solve problems with code" is going to be an emerging skill set that will change a lot of disciplines.
The rise of tools like Cursor reminds me of the Industrial Revolution in France. When machines first appeared in factories, unskilled workers who didn’t understand how they operated often got injured - sometimes quite literally losing fingers. But for skilled craftsmen, these machines became force multipliers, dramatically increasing productivity and improving overall living standards.<p>The same applies to software development. If you lack the fundamentals - how memory, I/O, networking, and databases work - you’re at risk of building something fragile that will break under real-world conditions. But for those who understand the moving parts, tools like Cursor supercharge efficiency, allowing them to focus on high-level problem-solving rather than boilerplate coding.<p>Technology evolves, but the need for deep knowledge remains.
Those who invest in learning the craft will always have the advantage.
<i>> Because if you can vibe code… so can everyone else.</i><p>That's really the money shot, right there.<p>CEOs have this dream of firing all their "obnoxious" engineers, and "vibe-coding" their own products. That's not something new. People have been selling this dream to gullible C-suiters since I first started coding in Machine Code (1980s).<p>The future will belong to the <i>engineers</i> that can leverage AI. Engineering is a lot more involved than "HAL, write me a Facebook," which is the C-suite dream.<p>It's just that engineering will move another level up, as it has, for hundreds of years.
I've been programming for a few decades. I love LLMs. They make tedious things quick. Help me resolve gnarly issues. Make short work of writing unit tests. Generate oodles of boilerplate at will. Etc. It makes me more productive and less reluctant to take on risky things. By risky I mean things that formerly would have likely derailed my busy schedule because I'd get side tracked for to long and would have to de-prioritize more important stuff.<p>Anyway, resistance is futile. You will be assimilated ... or retired. The reality of our job is that new generations are going to come in and they'll be using all the latest tools and gadgets. That's nothing new. And I'm part of a generation that in a decade or two will be mostly on the sidelines enjoying retirement. So, I'm well aware that progress isn't going to stop over my whining and grumbling. It annoys me when I catch myself doing that. I want to be better than that.<p>LLMs are part of the job now. They are tools. And tools are only as good as the people wielding them. So, skill up and learn. It's not like it's very hard. If you are getting poor results, you might be doing it wrong. Figure it out; part of the job. Your mileage may vary. But there are a lot of tools and chances are you just haven't found the right one yet. Also, if some tool/llm limitation is blocking getting good results for something, wait 3 months and try again. The pace of progress is ridiculous currently.<p>Or better yet: become part of the solution and make your own tools. This stuff is stupidly easy. It's prompt engineering with some trivial plumbing around them mostly. And you can generate the plumbing (what, you were going to do that manually?). That's why there are so many AI tools popping up right now. Most of them won't survive very long. But there are some good ideas lurking there.
Probably the opposite is true: the more you know how to code, the less <i>productive</i> you'll be with AI. This has been my observation watching a non-technical friend build a SaaS as a one-man team in 4 months and is generating $#,000 in revenue within 2 months.<p>The way he uses AI is just completely different from how the technical folks I know use AI because he doesn't think about the code at all. The way he instructs the AI is different from how engineers prompt the AI.<p>I actually think that his success with AI is in particular <i>because</i> he doesn't know how to code but was previously managing projects and offshore teams (so lots of writing down exactly what he wants, but with no specifics on how it gets implemented).
This resonates a lot with what I’ve seen outside of code too. I’ve been building an AI chess coach and noticed the same pattern: people plug their games into Stockfish, see a list of best moves, and walk away thinking they’ve “analyzed” the game. But real understanding — like in programming — only comes from engaging with why things went wrong.<p>That’s what I’m trying to fix. Instead of just showing lines, my AI coach gives voice-guided feedback, visual highlights, and practical insights. More like working with a real coach than sifting through raw engine output.<p>The goal is to make analysis as engaging as playing—and shift the mindset from “just tell me the best move” to “help me think better.”<p>Demo if curious: <a href="https://www.loom.com/share/9e1578f1348841c1992c5d902e371312?sid=56108d1f-e81a-4538-84e9-c4792e96aea1" rel="nofollow">https://www.loom.com/share/9e1578f1348841c1992c5d902e371312?...</a>
To each, their own. To deeply understand an area you have to learn it from bottom up.<p>I learned BASIC as a small kid, using a clone of ZX Spectrum. I was aware that the memory was limited and that I can poke and peek a memory address to set or retrieve the info.<p>I learned Pascal and then rapidly went to C and C++. I learned about pointers and how the memory is layed out and what are system calls.<p>I learned about CPUs and I learned some X86 assembly.<p>Ar the university I learned about digital circuits and how to assemble one using logical gates. Of course I learned much more, data structures, algorithms, operating systems, distributed systems, parallel and concurrent programming, formal languages and automata theory, cryptography, web, lots of stuff.<p>I learned other lots of stuff by myself.<p>I've built desktop apps, websites, software for microcontrollers, games, web applications and now I am working on microservices based apps running in cloud.<p>I was a junior developer, graduated somehow to senior. I worked as a software architect and now I am a team leader.<p>These days I work almost exclusively with C#, but I am also interested in other languages if I have some spare time to evaluate them.<p>What I want to say is this: is not enough to learn the highest level of technology of today. Today that is AI, few years ago it was JS frameworks, more years ago it was Java, .NET, Python.<p>To be good at what you do you always have to learn all layers under the current top layer. Learn from bottom up. You don't have to be good at every technical detail, but you have to understand at least how the things work.
Ignoring AI would be foolish, since AI is the best programming tutor you can wish for and will speed up your learning noticeably, since you can always ask for clarification or examples when something isn't clear. It's also a great way to get random data for testing. And it will help you unearth lesser known corners of a programming language that you might have overlooked otherwise.<p>The downside is that at the current speed of improvement, AI might very well already be at escape velocity, where it improves faster than you, and you'll never be able to catch up and contribute anything useful. For a lot of small hobby projects, that's already the case.<p>I don't think there are any easy answers here. Nothing wrong with learning to code because it's fun. But as a career choice, it might not have a lot of future, but then, neither might most other white-collar jobs. Weird times are ahead of us.
I'm getting back into programming after a couple of years other roles. I have to learn a framework I haven't worked before and get to know new paradigms I never used.<p>In a way, I'm feeling lucky that my company currently explicitly bans the use of AI tools on our code bases (for good reasons). This forces me to write all my own code and understand what it's doing. The only thing I use AI tools for is to explain some new concepts or paradigms in ways I can understand.<p>What's also great is that I can throw some code from Stack or Github in there and get it explained. I'm glad these tools exist, since they make learning much easier, but they're also a trap if you depend on them in stead of your own knowledge.
> Because if you can vibe code… so can everyone else.
> And if everyone can do it, what makes you think Devin won’t replace you?<p>Devin won't replace you if you can create valuable products through "vibe coding" or whatever else you call it.<p>when coding itself becomes a commodity, value creation becomes more concentrated up the stack: what you choose to build, how well you market / sell it, how you connect with your customers, product design. Devin won't outcompete humans at these skills anytime soon.<p>instead of sticking to a skill that's quickly becoming a commodity (as the author recommends), moving up the stack is the way to go (outside of very niche, specialized engineering domains - e.g: training base models).
AI does not and will not solve the most difficult part of programming:
Expressing how you wish to solve a problem in simple terms.<p>It doesn't matter if you communicate with a compiler or an LLM - you still need to express your thoughts and ideas with no ambiguity for it to produce the wanted behavior. What makes "vibe coding" with an LLM both easier and more challenging at the same time is that is will guess what you mean and give you results that "kind of " work even when you express yourself unclearly. For someone who can code, the "kind of work" results can be used as a starting point to evolve into something useful. For someone who can't code, it's an inevitable dead end.<p>I find that those who struggle with programming have the exact same type of struggles when trying to do it with LLMs - no structured plan on how to approach a problem and difficulties to understand the context in which they are working.
To me "learn to code" has ALWAYS been a synonym to "learn to think". Coding is nothing else than thinking. You learn a handful of structures and then you only think about how to combine them. It doesn't matter if you do it by AI or "by hand".
Using LLMs to write code is a waste. If its rote code or boilerplate it sucks at writing it consistently.<p>If its novel code, it's not in its training set.<p>What LLMs don't suck as much is writing semi readable docs and commit messages.
I guess experience is widely varying with tools. I had no idea what WSS was. Cursor searched the web and docs and necessary and built a working program. Then we went over each conceptual blocks and it explained stuff to me. When I was confused about some parts, it decided I need to strip away the RealTime API component and just understand asynco in context of WSS so it created a new file with a toy example.<p>Could it have written some really, really bad and unmaintainable code and it was just justifying things? Very possible. I asked to strip some 60% of the code after I thought I understood, and it was still working. The original code was too abstract for me and hard to follow. Did I learn in this whole process? I think yes. Would I have learned more if I had worked without AI? I think yes.<p>In life, some people play games and figure out the best builds after a lot of sweat and tears while others prefer googling the meta and only doing those builds, it seems. Both approach seems fine.
For a cynical take, 10, 15 years ago this headline would have been "Learn to code, ignore Stack Overflow, then use SO to code even better".<p>In this context, AI is just one of many resources you can use - books, websites, SO, etc - to improve your coding. The big differentiator is that you can have a realtime conversation with AI, whereas when talking to other people - be it on SO, IRC, Github, forums etc - you may or may not get a response and it may or may not be helpful. AI tools are a bit more predictable that way.<p>I'm of the opinion that AI will not replace developers but will be another tool in their arsenal. And part of the skillset of (some) developers will be working with an AI tool effectively. I don't have that yet, but then, I don't know much about Docker / K8S, cloud providers, low level memory management, pure functional coding, Rust, graphics programming etc etc either.
That is what I did. I programmed since I was 10 on and off until around age 27 where I started basically full time after studying chemistry but not wanting to work in the lab. I was 100% FT on a project since 2018/2019 and began using AI when ChatGPT came out, but couldn't use it on this project for ages because it was all giant legacy code with lots of context (both explicit and implicit). I used it for new projects and side things. I really enjoyed it. I learned SQL and Bash and PowerShell from the collaborations. I also made my first macOS app. I have even learned a few new things in JavaScript which is my main and began to use it on my main project too. I feel so lucky to be alive at this time. It's amazing to have this way to work!
I wonder how much the success of AI coding tools hinges on having a decent learning set?<p>Because if a lot, I predict that as more published code is generated, we will find ourselves locked in a certain point in history in terms of versions of dependencies.
That advice was true already a decade ago, but replace AI with StackOverflow. I encountered many junior devs (mostly money-driven, not out of passion for software development) that were completely fine just copy-pasting code out of Stackoverflow without getting a deeper understanding or at least trying to understand what their copied code does. Same is true for AI. Both are great tools, but to become truly a master of your craft you should always thrive to become the person that would have answered the SO question - or wrote the article that the AI learned from.
Re what Replit CEO said: i mean sure, learn how to think, learn how to breakdown problems and communicate - good skills to have, regardless of AI or not. and learning how to code is one of the great ways to improve these skills. people generally aren't going to learn how to think in a vacuum, right? Math, science, and programming, among others, that's how most people learn how to think, they are not going to be a waste of time.
I've been following some kid on TikTok who started last year on this journey - and it's been truly frustrating to watch. I tried to discourage them from leaning so heavily on AI, but they insisted it was a different way of learning.
A year later, and they still don't comprehend for loops or conditional blocks, and believe that comprehending this basic material is a deep understanding of code.
In a way, "vibe coding" is like what infants do when they give "error messages" of "hungry" to query mom for food.<p>Newcomers may be tempted to maintain the uttermost ignorance of what they are creating, as I imagine "vibe coding" implies, where the height of problem-solving is creating quicker pipes between errors and re-submission to AI to fix those errors.<p>I'm really glad I learned programming a long time ago, before AI. I use AI more like a tool for getting information (much like browsing manuals, forums, Stack Overflow), but it is, of course, magnitudes better. I still do the conceptual outlining and problem solving, and that is what programming is to me. If there's something I don't understand from the AI, I always query to know how it works. It's even easier now to understand how things work thanks to AI.
Imo if you learn to code today and ask the LLM to explain things to you it could very efficient. People that fall into the trap of using LLMs to write code they don't understand would just fall into another trap when LLMs wouldn't exist.
The only people saying this kind of thing are coders desperate to stay relevant. There is no future in coding. Its gone in a few years. Instead begin learning the skills required to work with AI to get it create what you want.
I think of it as a magic wand that makes me more efficient when it comes to smaller projects. I now do things I would earlier not even pick up because I simply type less now. But I feel like you need to have intuition/experience to question AI all the time. Why this code was added? Is it safe? Is it the right way? Is it the right method, tool, library, maybe we can try another approach. I feel like I learn faster too, because I can so quickly ask a question in a context with which AI is familiar. I would neither down play its role or follow "learning how to code is useless" hypers.
There are a lot of reasons I dislike the use of LLMs in software, one of which is that I think it's absurd to claim that using such fuzzy and imprecise tools and methods should be considered engineering...<p>But to be honest, I think the main thing is that they just absolutely suck the joy and <i>fun</i> out of an activity. I like writing, I like coding, these are things that I do at least in part because I get some kind of direct enjoyment out of doing them. Using a chatbot simply is <i>not interesting or fun to me</i>. It's like being a spectator and thinking that means you "participated" in the game you watched.<p>If you code or whatever just as a means to make ends meet sure, it makes total sense that you'll want to use these tools, but I will absolutely never understand the people that claim to like a craft and reach for LLMs to do it—to me these people have just entirely missed the point of what it means to have an activity you enjoy. It's not about the output, it's all about the process and self-enrichment that happens along the way.
The best part of AI in my opnion is not having to search through documentation. You have any questions about a specific funcion in this lib? Just ask, and will do the search for you, and provide an quick example of use.<p>This shit is magical.
I've stopped reading HN posts about AI because comments on them are the same old same old: 2 camps, one of which says get over it 'cause it's great, the other of which says it's a complete FAIL and always will be.