The fun is still there. I'm relearning Rust and generative AI is really useful to help with understanding concepts and improving code. But I'm still the one understanding and improving.
Still an infinite amount to learn and do. It's still not hard to have more skill than an AI. Of course AI can solve all the dumbbell problems you get in school. They're just there to build muscle. Robots can lift weights better than you, too, but that doesn't mean there's no value in you doing it.
> It's still not hard to have more skill than an AI.
Eh, today, maybe, and within specific domains. It's far from certain that this will remain true 5 or 10 years from now. The capability of these tools has improved greatly even compared to a year ago, so it's not far fetched to imagine that they will continue to gain ground.
> Of course AI can solve all the dumbbell problems you get in school. They're just there to build muscle. Robots can lift weights better than you, too, but that doesn't mean there's no value in you doing it.
That's a strange analogy. Technology, by definition, exists to facilitate human work. Relying on it has the opposite effect of "building muscle". "Muscles", in fact, atrophy the more we rely on technology.
Doing the work without technology can certainly be valuable. But it's a personal value appreciated at most by a niche community of people. The actual market value of the work collapses once the product becomes a commodity. This is the effect of "AI" tools on software. The quality of the fast and cheap version of the product is still inferior to the artisan product, but a) this can only improve, and b) most of the market can't tell the difference.
> It's far from certain that this will remain true 5 or 10 years from now.
I agree with this statement. But I also firmly believe that if AI gets good enough to replace software developers en masse, it will be good enough for basically everything and the global economy will collapse.
> Relying on it has the opposite effect of "building muscle". "Muscles", in fact, atrophy the more we rely on technology.
I also agree with that statement, but I'm not arguing to rely on it entirely, but to use it to become better at bigger things than it can possibly imagine.
Yes, there will be tons of boilerplate code and those jobs will go the way of the dodo. But half the businesses in the world are better than the other half, and they didn't get there by doing the exact same thing as everyone else.
Thought experiment: if there were an AI everyone had access to that was capable of designing and implementing a business that would crush all competition, how would you make your business succeed?
"I’m not sure if anyone else feels this way, but with the introduction of generative AI, I don’t find coding fun anymore. It’s hard to motivate myself to code knowing that a model can do it much quicker. The joy of coding for me was literally the process of coding."
I experimented with GPT-5 recently and found its capabilities to be significantly inferior to that of a human, at least when it came to coding.
I was trying to give it an optimal environment, so I set it to work on a small JavaScript/HTML web application, and I divided the task into small steps, as I'd heard it did best under those circumstances.
I was impressed overall by how far the technology has come, but it produced a number of elementary errors, such as putting JavaScript outside the script tags. As the code grew, there was also no sense that it had a good idea of how to structure the codebase, even when I suggested it analyze and refactor.
So unless there are far more capable models out there, we're not at the stage where generative AI can match a human.
In general I find current model to have broad but shallow thinking. They can draw on many sources, which is extremely useful, but seem to have problems reasoning things through in depth.
All this is to say that I don't find the joy of coding to have gone at all. In fact, there's been a number of really thorny problems I've had to deal with recently that I'd love to have side-stepped, but due to the currently limitations of LLMs I had to solve them the old-fashioned way.
You are probably doing something others have done before frequently.
I find the LLMs struggle constantly with languages there is little documentation or out of date. RAG, LoRA and multiple agents help, but they have their own issues as well.
> and found its capabilities to be significantly inferior to that of a human, at least when it came to coding.
I think we should step back and ask: do we really want that? What does that imply? Until recently nobody would use a tool and think, yuck, that was inferior of a human.
GPT-5 what? The GPT-5 models range from goofily stupid to brilliant. If you let it select the model automatically, which is the case by default, it will tend to lean towards the former.
Having gone through a bit of a crisis of meaning personally lately, this article resonates deeply. I would encourage the author to look inward and question the beliefs that got them here.
I'd argue you didn't lose the joy of coding, you lost the illusion that coding made you real, that it made you you.
I came to the same conclusion after 40+ years of programming: better if you come to that realisation earlier. Still love coding though, but I leave the paid work to my colleagues and llms: I just code for fun these days. I also write for fun and find it pretty similar, feeling and satisfaction wise.
But, what about the graduating senior who, yeah started because they love the craft, but also need a way to pay the bills for a few decades of their life?
There definitely are times that I lose the "joy of coding" and it has nothing to do with any illusions, it has everything to do with the kind of programming tasks I have to work on. Greenfield projects are the best, tech debt is the worst. Working on fun stuff is just fun.
I'm not suggesting that the joy of coding is tied to illusions for everyone, just appears to be more to the story in the case of the author based on his framing.
I agree wholeheartedly. I'm not suggesting there's no value in the act of creation.
I think the author has been telling himself that he derived joy from the act of creating, but his comments suggest otherwise, he was deriving joy from a false belief of what being a coder meant, about what it would provide him. There's a mismatch between what he believes he's getting out of coding, vs what he's actually getting.
Put another way, reality is reality, there is no right reality, or wrong reality. Perceiving it as right or wrong is just our ego trying to bend reality to match our beliefs.
>>> The joy of coding for me was literally the process of coding.
Maybe I was lucky. For me, the joy was the power of coding. Granted, I'm not employed as a coder. I'm a scientist, and I use coding as a problem solving tool. Nothing I write goes directly into production.
What's gone is the feeling that coding is a special elite skill.
With that said, I still admire and respect the real software developers, because good software is more than code.
This seems to romanticize the past. I've been doing this for 40 years and I don't see that much has changed. I would code even if I didn't get paid for it. That said I've always seen writing code as a means to an end. I use GenAI every day to write code, and it brings pure joy when there's boiler plate that I don't need to write so I can focus on the fun stuff. There is zero value in me writing yet another Python argparse routine. I've done it and I've learn everything I'm ever going to learn about it. Let me get on to the stuff that I don't know how to do.
Code generation tools of today are pretty good at writing the boring boilerplate. I think the author is aware of this.
But what happens when they get really good at generating the not-so-boring bits? They're much better at this than they were a year or two ago, so it's not unthinkable that they will continue to improve.
I'm a firm "AI" skeptic, and don't buy into the hype. It's clear that the brute force approach of throwing more data and compute at the problem has reached diminishing returns. And yet there is ample room for improvement by applying solid engineering alone. Most of what we've seen in the past year is based on this: MCP, "agents", "skills", etc.
> I would code even if I didn't get paid for it.
That's great, but once the market value of your work diminishes, it's no longer a career—it's a hobby. Which doesn't mean there won't be demand for artisanal programming, but it won't power the world anymore. It will be a niche skill we rely on for very specific tasks, while our jobs will be relegated to steer and assist the "AI" into producing reliable software. At least in the short-term. It's doubtful whether the current path will get us to a place where these tools are fully self-sufficient, and it's arguable whether that's something worth aiming for anyway.
This is the bleak present and future the article is talking about. Being an assistant to code generation tools is far removed from the practice of programming. I personally find it tedious, unengaging, and extremely boring. There's little joy in the experience beyond ending up with a working product. The road to get there is not a journey of discovery, serendipity, learning, and dopamine hits. It is a slog of writing software specs, juggling contextual information and prompts, and coaxing a human facsimile into producing working software by using natural language. I dislike every part of this process. This is not the type of work that inspired me to do this professionally. Sure, every job has tasks we sometimes don't enjoy. But once you remove programming from the equation, there's not much joy in it left for me.
I'm not particularly worried about being automated out of a job. I use the cutting edge tools for my work and they are getting incrementally better. It feels like we are plateauing. I can see a world where the LLM isn't writing code end-to-end. Instead it is writing chunks of code that I integrate. That may be more efficient than me writing out a 10,000 sentence English spec document. That allows me to express my value-add more effectively than I could otherwise. I think OP is projecting the bleak end of the possible outcomes. I don't see that happening.
Okay I get the desire of not wanting to do repetitive stuff. It appears doing this with an llm scratches your itch. Before the same - focusing on the intrinsic complexity instead of the accidental - could be achieved by using libraries, toolkits, frameworks, better compiler (plugins), or “better” languages.
What plagues me about LLMs is that all that generated code is still around in the project making reviews harder as well s understanding the whole program source. What is in there that makes you prefer this mechanism instead of the abstractions that have been increasingly available since forever?
Isn't this compiled languages vs writing pure machine code argument all over again?
The compiler produces a metric shit ton of code that I don't see when I'm writing C++ code. And don't get me started on TypeScript/Clojure - the amount of code that gets written underneath is mindbogglingly staggering, yet I don't see it, for me the code is "clean".
And I'm old enough to remember the tail end of the MachineCode -> CompiledCode transition, and have certainly lived through CompiledCode -> InterpretedCode -> TranspiledCode ones.
There were certainly people who knew the ins and outs of the underlying technology who produced some stunningly fast and beautiful code, but the march of progress was inevitable and they were gradually driven to obscurity.
This recent LLM step just feels like more of the same. *I* know how to write an optimized routine that the LLM will stumble to do cleanly, but back in the day lots of assembler wizards were doing some crazy stuff, stuff that I admired but didn't have the time to replicate.
I imagine in the next 10-20 years we will have Devs that _only_ know English, are trained in classical logic and have flame wars about what code exactly would their tools generate given various sentence invocations. And people would benchmark and investigate the way we currently do about JIT compilation and CPU caching - very few know how it actually works but the rest don't have to, as long as the machine produces the results we want.
Just one more step on the abstraction ladder.
The "Mars" trilogy by Kim Stanley Robinson had very cool extrapolations where this all could lead, technologically, politically, social and morally. LLMs didn't exists when he was writing it, but he predicted it anyway.
I've seen a lot change. I used to have a seemingly bottomless list of things we are doing wrong and about half of them have dropped off in the last twenty years. Did they all turn out as well as we hoped they would? No. I don't think a single one did. We are half-assing a lot of things that we used to laugh off entirely. In most of these cases some is better than none, but could be a lot better.
What I worry about is that my list has gotten shorter not because everything is as it should be but because I have slowed down.
Quite a lot of things on that list were of the "The future is here but it's not evenly distributed" sort. XP was about a bunch of relatively simple actions that were force multipliers with a small multiple on them. What was important was that they composed. So the benefit of doing eight of them was more than twice the benefit of doing four. Which means there's a lot of headroom still from adding a few more things.
That's certainly a more positive way to look at this. Working software has always relied on having people who grok the code, and this happens by spending a lot of time thinking about the code while writing it. And it's undocumented, because the nature of it is something you cannot really document.
If AI is writing all the code, how do we keep the quality good? It's so obvious with the current GenAI tools that they're getting great at producing code, but they don't really understand the code.
We don't really know how this story unfolds, so it's good to keep a positive mindset.
Just putting aside the bold assumption that LLMs do make coders obsolete or coding unnecessary, it is possible to find similar joy in the end result as one does (or did, given the article) for programming itself. Focusing on what kind of tools or products are being created, and what problems are being solved, and together with LLMs achieving that goal better and faster than without them and finding joy in solving problems this world has. That’s typically why anyone would have paid you to code anyway even before LLMs.
Of course in reality there’s weird economical mechanics where making the most money and building something that benefits the world don’t necessarily collide, but theres always demand for and joy in solving complex problems, even if its on a higher abstraction level than coding with your favorite language.
I genuinely feel like I got bait-and-switched by computer science. If I could go back and study something different I would do it in a heartbeat.
Sadly, there's very little I can do now. I don't have the financial means to meaningfully change careers now. Pretty much the only thing I could do now that pays somewhat well and doesn't require me to go to university again is teaching. I think I will ride this one out and end it when it ends.
I did like the (short) LLM-free part of my career. The bait-and-switch refers specifically to the changes due to the introduction of LLMs. Any career where LLMs don't play a big role would not have been a bait-and-switch.
That said, I don't understand the point of "what if nothing ever works out for you?"-type questions. What do you expect me to answer here? That I'm secretly a wizard and with the flick of my magic wand I'll make something work out?
For most jobs in any field, having a degree is more important than what the degree is in. University is not a jobs training program, it’s a way to build a foundation. Understanding how systems work together can be applied in many areas of business, not just coding.
I still enjoy coding. AI mostly doesn’t produce adequate quality or correctness for the type of code I enjoy writing. There are several domains where AI is worse than useless because training data doesn’t exist. Obviously my experience doesn’t generalize but writing software is a vast, unbounded domain.
If you find coding boring, explore the frontiers. You will find a lot of coding wilderness where no AI has trod.
"If you find coding boring, explore the frontiers. You will find a lot of coding wilderness where no AI has trod."
this, AI is nothing without data set
so if you working in bleeding edge technology where your tools is only have 3 contributor and a way to access them via IRC channel once a day, things get interesting
Readit News
Still an infinite amount to learn and do. It's still not hard to have more skill than an AI. Of course AI can solve all the dumbbell problems you get in school. They're just there to build muscle. Robots can lift weights better than you, too, but that doesn't mean there's no value in you doing it.
Eh, today, maybe, and within specific domains. It's far from certain that this will remain true 5 or 10 years from now. The capability of these tools has improved greatly even compared to a year ago, so it's not far fetched to imagine that they will continue to gain ground.
> Of course AI can solve all the dumbbell problems you get in school. They're just there to build muscle. Robots can lift weights better than you, too, but that doesn't mean there's no value in you doing it.
That's a strange analogy. Technology, by definition, exists to facilitate human work. Relying on it has the opposite effect of "building muscle". "Muscles", in fact, atrophy the more we rely on technology.
Doing the work without technology can certainly be valuable. But it's a personal value appreciated at most by a niche community of people. The actual market value of the work collapses once the product becomes a commodity. This is the effect of "AI" tools on software. The quality of the fast and cheap version of the product is still inferior to the artisan product, but a) this can only improve, and b) most of the market can't tell the difference.
I agree with this statement. But I also firmly believe that if AI gets good enough to replace software developers en masse, it will be good enough for basically everything and the global economy will collapse.
> Relying on it has the opposite effect of "building muscle". "Muscles", in fact, atrophy the more we rely on technology.
I also agree with that statement, but I'm not arguing to rely on it entirely, but to use it to become better at bigger things than it can possibly imagine.
Yes, there will be tons of boilerplate code and those jobs will go the way of the dodo. But half the businesses in the world are better than the other half, and they didn't get there by doing the exact same thing as everyone else.
Thought experiment: if there were an AI everyone had access to that was capable of designing and implementing a business that would crush all competition, how would you make your business succeed?
I experimented with GPT-5 recently and found its capabilities to be significantly inferior to that of a human, at least when it came to coding.
I was trying to give it an optimal environment, so I set it to work on a small JavaScript/HTML web application, and I divided the task into small steps, as I'd heard it did best under those circumstances.
I was impressed overall by how far the technology has come, but it produced a number of elementary errors, such as putting JavaScript outside the script tags. As the code grew, there was also no sense that it had a good idea of how to structure the codebase, even when I suggested it analyze and refactor.
So unless there are far more capable models out there, we're not at the stage where generative AI can match a human.
In general I find current model to have broad but shallow thinking. They can draw on many sources, which is extremely useful, but seem to have problems reasoning things through in depth.
All this is to say that I don't find the joy of coding to have gone at all. In fact, there's been a number of really thorny problems I've had to deal with recently that I'd love to have side-stepped, but due to the currently limitations of LLMs I had to solve them the old-fashioned way.
I find the LLMs struggle constantly with languages there is little documentation or out of date. RAG, LoRA and multiple agents help, but they have their own issues as well.
I think we should step back and ask: do we really want that? What does that imply? Until recently nobody would use a tool and think, yuck, that was inferior of a human.
GPT-5 what? The GPT-5 models range from goofily stupid to brilliant. If you let it select the model automatically, which is the case by default, it will tend to lean towards the former.
I also briefly tried out some of the other paid-for models, but mostly worked with GPT-5.
I'd argue you didn't lose the joy of coding, you lost the illusion that coding made you real, that it made you you.
I'm not suggesting that the joy of coding is tied to illusions for everyone, just appears to be more to the story in the case of the author based on his framing.
It is never everything, but it should also never be nothing.
I think the author has been telling himself that he derived joy from the act of creating, but his comments suggest otherwise, he was deriving joy from a false belief of what being a coder meant, about what it would provide him. There's a mismatch between what he believes he's getting out of coding, vs what he's actually getting.
Put another way, reality is reality, there is no right reality, or wrong reality. Perceiving it as right or wrong is just our ego trying to bend reality to match our beliefs.
Maybe I was lucky. For me, the joy was the power of coding. Granted, I'm not employed as a coder. I'm a scientist, and I use coding as a problem solving tool. Nothing I write goes directly into production.
What's gone is the feeling that coding is a special elite skill.
With that said, I still admire and respect the real software developers, because good software is more than code.
But what happens when they get really good at generating the not-so-boring bits? They're much better at this than they were a year or two ago, so it's not unthinkable that they will continue to improve.
I'm a firm "AI" skeptic, and don't buy into the hype. It's clear that the brute force approach of throwing more data and compute at the problem has reached diminishing returns. And yet there is ample room for improvement by applying solid engineering alone. Most of what we've seen in the past year is based on this: MCP, "agents", "skills", etc.
> I would code even if I didn't get paid for it.
That's great, but once the market value of your work diminishes, it's no longer a career—it's a hobby. Which doesn't mean there won't be demand for artisanal programming, but it won't power the world anymore. It will be a niche skill we rely on for very specific tasks, while our jobs will be relegated to steer and assist the "AI" into producing reliable software. At least in the short-term. It's doubtful whether the current path will get us to a place where these tools are fully self-sufficient, and it's arguable whether that's something worth aiming for anyway.
This is the bleak present and future the article is talking about. Being an assistant to code generation tools is far removed from the practice of programming. I personally find it tedious, unengaging, and extremely boring. There's little joy in the experience beyond ending up with a working product. The road to get there is not a journey of discovery, serendipity, learning, and dopamine hits. It is a slog of writing software specs, juggling contextual information and prompts, and coaxing a human facsimile into producing working software by using natural language. I dislike every part of this process. This is not the type of work that inspired me to do this professionally. Sure, every job has tasks we sometimes don't enjoy. But once you remove programming from the equation, there's not much joy in it left for me.
What plagues me about LLMs is that all that generated code is still around in the project making reviews harder as well s understanding the whole program source. What is in there that makes you prefer this mechanism instead of the abstractions that have been increasingly available since forever?
The compiler produces a metric shit ton of code that I don't see when I'm writing C++ code. And don't get me started on TypeScript/Clojure - the amount of code that gets written underneath is mindbogglingly staggering, yet I don't see it, for me the code is "clean".
And I'm old enough to remember the tail end of the MachineCode -> CompiledCode transition, and have certainly lived through CompiledCode -> InterpretedCode -> TranspiledCode ones.
There were certainly people who knew the ins and outs of the underlying technology who produced some stunningly fast and beautiful code, but the march of progress was inevitable and they were gradually driven to obscurity.
This recent LLM step just feels like more of the same. *I* know how to write an optimized routine that the LLM will stumble to do cleanly, but back in the day lots of assembler wizards were doing some crazy stuff, stuff that I admired but didn't have the time to replicate.
I imagine in the next 10-20 years we will have Devs that _only_ know English, are trained in classical logic and have flame wars about what code exactly would their tools generate given various sentence invocations. And people would benchmark and investigate the way we currently do about JIT compilation and CPU caching - very few know how it actually works but the rest don't have to, as long as the machine produces the results we want.
Just one more step on the abstraction ladder.
The "Mars" trilogy by Kim Stanley Robinson had very cool extrapolations where this all could lead, technologically, politically, social and morally. LLMs didn't exists when he was writing it, but he predicted it anyway.
What I worry about is that my list has gotten shorter not because everything is as it should be but because I have slowed down.
Quite a lot of things on that list were of the "The future is here but it's not evenly distributed" sort. XP was about a bunch of relatively simple actions that were force multipliers with a small multiple on them. What was important was that they composed. So the benefit of doing eight of them was more than twice the benefit of doing four. Which means there's a lot of headroom still from adding a few more things.
If AI is writing all the code, how do we keep the quality good? It's so obvious with the current GenAI tools that they're getting great at producing code, but they don't really understand the code.
We don't really know how this story unfolds, so it's good to keep a positive mindset.
Of course in reality there’s weird economical mechanics where making the most money and building something that benefits the world don’t necessarily collide, but theres always demand for and joy in solving complex problems, even if its on a higher abstraction level than coding with your favorite language.
Sadly, there's very little I can do now. I don't have the financial means to meaningfully change careers now. Pretty much the only thing I could do now that pays somewhat well and doesn't require me to go to university again is teaching. I think I will ride this one out and end it when it ends.
That said, I don't understand the point of "what if nothing ever works out for you?"-type questions. What do you expect me to answer here? That I'm secretly a wizard and with the flick of my magic wand I'll make something work out?
If you find coding boring, explore the frontiers. You will find a lot of coding wilderness where no AI has trod.
this, AI is nothing without data set
so if you working in bleeding edge technology where your tools is only have 3 contributor and a way to access them via IRC channel once a day, things get interesting
This assumes that companies care about "code quality" and customers care about bugs.
> If you find coding boring, explore the frontiers. You will find a lot of coding wilderness where no AI has trod.
There are a lot of software engineers and not a lot of frontier.