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Ask HN: Has AI stolen the satisfaction from programming?

97 points marxism | 2 comments | 13 Oct 25 19:07 UTC | HN request time: 0s | source

I've been trying to articulate why coding feels less pleasant now.

The problem: You can't win anymore.

The old way: You'd think about the problem. Draw some diagrams. Understand what you're actually trying to do. Then write the code. Understanding was mandatory. You solved it.

The new way: The entire premise of AI coding tools is to automate the thinking, not just the typing. You're supposed to describe a problem and get a solution without understanding the details. That's the labor-saving promise.

So I feel pressure to always, always, start by info dumping the problem description to AI and gamble for a one-shot. Voice transcription for 10 minutes, hit send, hope I get something first try, if not hope I can iterate until something works. And when even something does work = zero satisfaction because I don't have the same depth of understanding of the solution. Its no longer my code, my idea. It's just some code I found online. `import solution from chatgpt`

If I think about the problem, I feel inefficient. "Why did you waste 2 hours on that? AI would've done it in 10 minutes."

If I use AI to help, the work doesn't feel like mine. When I show it to anyone, the implicit response is: "Yeah, I could've prompted for that too."

The steering and judgment I apply to AI outputs is invisible. Nobody sees which suggestions I rejected, how I refined the prompts, or what decisions I made. So all credit flows to the AI by default.

The result: Nothing feels satisfying anymore. Every problem I solve by hand feels too slow. Every problem I solve with AI feels like it doesn't count. There's this constant background feeling that whatever I just did, someone else would've done it better and faster.

I was thinking of all the classic exploratory learning blog posts. Things that sounded fun. Writing a toy database to understand how they work, implementing a small Redis clone. Now that feels stupid. Like I'd be wasting time on details the AI is supposed to handle. It bothers me that my reaction to these blog posts has changed so much. 3 years ago I would be bookmarking a blog post to try it out for myself that weekend. Now those 200 lines of simple code feels only one sentence prompt away and thus waste of time.

Am I alone in this?

Does anyone else feel this pressure to skip understanding? Where thinking feels like you're not using the tool correctly? In the old days, I understood every problem I worked on. Now I feel pressure to skip understanding and just ship. I hate it.

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tpoacher ◴[13 Oct 25 20:14 UTC] No.45572859[source]▶
Not necessarily an identical thought to OP, but, anecdotally (n=1), my experience teaching the exact same course on Advanced Java Programming for the last 4 years has been that the students seem to be getting more and more cynical, and seem to think of programming as an art or as a noteworthy endeavour in itself less and less. Very few people have actually vocalised the "why do I even need to learn this if I can write a prompt" sentiment out loud, but it has been voiced, and even from those who don't say it there's a very definite 'vibe' that is all but screaming it.

Whereas the vibe in the lecture theatre 4 years ago was far more nerdy and enthusiastic. It makes me feel very sorry for this new generation that they will never get to enjoy the same feeling of satisfaction from solving a hard problem with code you thought and wrote from scratch.

Ironically, I've had to incorporate some AI stuff in my course as a result of needing to remain "current", which almost feels like it validates that cynical sentiment that this soulless way is the way to be doing things now.

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raw_anon_1111 ◴[14 Oct 25 02:28 UTC] No.45575626[source]▶
I have no idea where this romanticism of software development comes from. I’ve been in the field professionally for 30 years across 10 jobs - everything from startups, to boring old enterprise companies, to small to midsize “lifestyle companies” and BigTech. Most people see the job as a vocation to make money and not a passion.

Scott Hanselman talked about “Dark Matter Developers” in 2012.

https://www.hanselman.com/blog/dark-matter-developers-the-un...

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1. baq ◴[14 Oct 25 10:05 UTC] No.45578182[source]▶
the art is in academics and in the hobby space, it was a cool feeling of accomplishment when you finally got something working. LLMs are very helpful here if you use them right - e.g. as a tutor if starting out.

in the business, it was always best to be value and result oriented, which very often has nothing to do with art and composed thought - shipping something that kinda works is priority 1. LLMs can significantly accelerate the already breakneck pace, but you need expertise to keep them in check, and this is where the gap between students and senior swes lies - and it's only getting bigger with every model release.

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2. tpoacher ◴[16 Oct 25 09:15 UTC] No.45603135[source]▶
I would disagree that the art is in the "academics and hobby space", though I suppose I may be using a different definition for 'art' here.

I read something some time a go and noted it down because I liked the concept:

> The japanese have two words for quality

> The first is "Atarimae hinshitsu", which can be roughly translated as "taken-for-granted quality." What do the Japanese take for granted when it comes to quality? They take for granted that things should work as they are supposed to, and they even see an elegance to things working properly -- whether it's cars, subway schedules, traditional flower arranging, or the famous tea ceremony.

> The second is "Miryoku teki hinshitsu", which means "bewitching" or "enchanting quality." This kind of quality appeals not to customer expectations and reliability (that things should do what they're supposed to), but rather to a person's aesthetic sense of beauty and elegance.

> Great products have both.

There's also the whole "Kaizen vs Cha bu duo" philosophy.

I think it was the above sentiments I was echoing, though I suppose more in a learning context.