[cdata]

AI has pushed me to arrive at an epiphany: new technology is good if it helps me spend more time doing things that I enjoy doing; it's bad if it doesn't; it's worse if I end up spending more time doing things that I don't enjoy.

AI has increased the sheer volume of code we are producing per hour (and probably also the amount of energy spent per unit of code). But, it hasn't spared me or anyone I know the cost of testing, reviewing or refining that code.

Speaking for myself, writing code was always the most fun part of the job. I get a dopamine hit when CI is green, sure, but my heart sinks a bit every time I'm assigned to review a 5K+ loc mountain of AI slop (and it has been happening a lot lately).

[Yodel0914]

I agree. I’m using copilot more and more as it gets better and better, but it is getting better at the fun stuff and leaves me to do the less fun stuff. I’m in a role where I need to review code across multiple teams, and as their output is increasing, so is my review load. The biggest issue is that the people who lean on copilot the most are the least skilled at writing/reviewing code in the first place, so not only do I have more to review, it’s worse(1).

My medium term concern is that the tasks where we want a human in the loop (esp review) are predicated on skills that come from actually writing code. If LLMs stagnate, in a generation we’re not going to have anyone who grew up writing code.

1: not that LLMs write objectively bad code, but it doesn’t follow our standards and patterns. Like, we have an internal library of common UI components and CSS, but the LLM will pump out custom stuff.

There is some stuff that we can pick up with analysers and fail the build, but a lot of things just come down to taste and corporate knowledge.

[wvenable]

I've been using it to do big refactors are large changes that I would simply avoid because, before, the benefits don't outweigh the costs of the doing it. I think half the problem people have is just using AI for the wrong stuff.

I don't see why it doesn't help with reviewing, testing, or refining code either. One of the advantages I find is that an LLM "thinks" differently from me so it'll find issues that I don't notice or maybe even know about. I've certainly had it develop entire test harnesses to ensure pre/post refactoring results are the same.

That said, I have "held it wrong" and had it done the fun stuff instead and that felt bad. So I just changed how I used it.

[cdata]

I read a lot of AI generated code these days. It makes really bad mistakes (even when the nature of the change is a refactor). I've tried out a few different tools and methodologies, but I haven't escaped the need to babysit the "agent." If I stepped aside, it would create more work for me and others on the backend of our workflow.

I read anecdotes of teams that push through AI-driven changes as fast as possible with awe. Surely their AIs are no more capable than the ones I'm familiar with.

[wvenable]

I read all the code and it sometimes make mistakes -- but I wouldn't call it really bad. And often merely pointing it out will get a correction. Sometimes it is funny. It's not perfect but nothing is perfect. I have noticed that the quality seems to be improving.

I still think whether you see sustained value or not depends a lot on your workflow -- in what you choose to do or decide and what you let it choose to do or decide.

I agree with you that this idea of just pushing out AI code -- especially code written from scratch -- by an AI sounds like a disaster waiting to happen. But honestly a lot of organizations let a lot of crappy code into their code-base long before AI came long. Those organizations are just doing the same now at scale. AI didn't change the quality, it just changed the quantity.