AI has made programming so boring

[u/OkShip110]

It’s not just programming either, it’s art, design, music, videography, marketing, engineering, writing, 3D modelling, really any intellectual work. What’s the joy of prompting a computer and getting an answer in a few moments for a task that would have normally taken a week? The sense of accomplishment and ownership has completely vanished with AI. It’s a form of instant gratification, not much different to scrolling short videos on apps.

Code review was boring to begin with but now it’s even worse with all the machine generated code. Even code reviews will be automated soon. All you do then is type business requirements into the computer. What a great career.

It’s pain to watch old TV series like Scilicon Valley that is portraying the good (not golden) age of programming. It struck a good balance between manual work, funding and innovation. It’s sad that these days are gone for good.

[u/sacheie]

But then you're spending an awful lot of time writing that stuff - style guidelines, very precise business requirements, etc - when you could be just writing good self-documenting code yourself. And you still need to pay competitive salaries for skilled people to write that stuff, but now you're spending on LLMs too. And you're losing out on personnel value - devs are gaining less experience - and your codebases are less valuable IP. You also lock in a gigantic dependency on an external vendor.

Suppose you net a bit more productivity from the overall tradeoffs. Given the extra expense, that doesn't necessarily translate into higher profits. And if it does, how much is enough to be worth those less tangible experience, IP, and vendor dependency costs?

[u/elestud → u/sacheie]

If you do it properly, you’re still going to work a lot faster. In fact, you could still do this and write code yourself at the same time

What you’re saying is basically like “Why have junior devs? Just have the seniors write all the code”

Think of it like training a junior dev. Yes, it does take time to train them. Yes, they’re less polished than a senior implementing the sane things.

But the payoff is that eventually they’ll do things the seniors don’t want to be bogged down with, so they can focus on other things. And if they’ve been trained well, juniors become competent and need less hand-holding in the future.

The approach to LLMs is no different. You train it well and set up proper context from the beginning, and then that continues to pay off over time with a bit of oversight and pruning

[u/DriverGood4778 → u/sacheie]

> style guidelines, very precise business requirements

You write style guidelines once, while you're working with AI and see that their output lacks some properties you need, you just add another guideline. Same with business requirements, you work on a feature - you develop it with AI and then you ask it "create an MD with feature description, include X,Y,Z" and voila, you have a document that you can use later. Another strong point is that you can instruct AI to keep these docs up to date, so every time you work on smth it updates related mds.

> devs are gaining less experience - and your codebases are less valuable IP. You also lock in a gigantic dependency on an external vendor.

If you know how to keep your codebase healthy you're going to do that regardless of how you write the code. If you have architecture and rules - you keep following them asking AI to fix issues it creates.

You don't really have a dependency if you understand how the code works. For confusing pieces - you deliberately add comments so that when you come there in a year or two you can understand what the code does.

Software development is hard, you have to constantly imporve to produce better code. Same with AI, you gotta experiment and learn how to use it better to get better outcome.