Sadly, there are some who don’t even know it, because they’re buying services from someone else that buys them from someone else that buys them from Amazon. So they’re currently wondering what the fuck is even going on, since they thought they weren’t using AWS.

I’m a software developer and my company is piloting the use of LLMs via Copilot right now. All of them suck to varying degrees, but everyone’s consensus is that GPT5 is the worst of them. (To be fair, no one has tested Grok, but that’s because no one in the company wants to.)

On top of that, there’s so much AI slop all over the internet now that the training for their models is going to get worse, not better.

They’ll ask their parents, or look up cooking instructions on actual websites.

Venture capital drying up.

Here’s the thing… No LLM provider’s business is making a profit. None of them. Not OpenAI. Not Anthropic. Not even Google (they’re profitable in other areas, obviously). OpenAI optimistically believes it might start being profitable in 2029.

What’s keeping them afloat? Venture capital. And what happens when those investors decide to stop throwing good money after bad?

BOOM.

There are tricks to getting better output from it, especially if you’re using Copilot in VS Code and your employer is paying for access to models, but it’s still asking for trouble if you’re not extremely careful, extremely detailed, and extremely precise with your prompts.

And even then it absolutely will fuck up. If it actually succeeds at building something that technically works, you’ll spend considerable time afterwards going through its output and removing unnecessary crap it added, fixing duplications, securing insecure garbage, removing mocks (God… So many fucking mocks), and so on.

I think about what my employer is spending on it a lot. It can’t possibly be worth it.

Yeah, code bloat with LLMs is fucking monstrous. If you use them, get used to immediately scouring your code for duplications.

It always is with these guys.

After working on a team that uses LLMs in agentic mode for almost a year, I’d say this is probably accurate.

Most of the work at this point for a big chunk of the team is trying to figure out prompts that will make it do what they want, without producing any user-facing results at all. The rest of us will use it to generate small bits of code, such as one-off scripts to accomplish a specific task - the only area where it’s actually useful.

The shine wears off quickly after the fourth or fifth time it “finishes” a feature by mocking data because so many publicly facing repos it trained on have mock data in them so it thinks that’s useful.

In our case, there are enough upper management folks who are opposed to it that I doubt it will last or ever be enforced. For people like me, it really doesn’t make any sense to enforce it in the first place, because all of my teammates are in other states and countries.

Making me go to the office just means you can’t schedule early meetings with me, because I’ll be commuting during that time.

My office just did the same thing. And the backlash is enormous. No one wants it. No one likes it.

The funny thing is that I’m actually an Arch user. I’m just not a dick about it.

Yeah, this sucks. Use the distro you like, people.

There’s a weird, cool anime that takes place in a bizarre version of it called Kowloon Generic Romance.

That’s how it’s done, yep.

Not the person you asked, but I do that sometimes. For instance, when I want to watch a specific video but I don’t want having watched it to affect other video recommendations.

You’re right, unit tests are another area where they can be helpful, as long as you’re very careful to check them over.

Actually, there’s growing evidence that beyond a certain point, more context drastically reduces their performance and accuracy.

I’m of the opinion that LLMs will need a drastic rethink before they can reach the point you describe.

So there are a few very specific tasks that LLMs are good at from the perspective of a software developer:

1. Certain kinds of analysis tasks can be done very quickly and efficiently with Copilot in agent mode. For instance, having it assess your existing code for adherence to stylistic standards where a violation isn’t going to trigger a linting error.
2. Quick script writing is something it excels at. There are all kinds of circumstances where you might need an independent script, such as a database seed file. It’s not part of the application itself, but it’s a useful utility to have, and Copilot is good at writing them.
3. Scaffolding a new application. If you’re creating something brand new and know which tools you want to use for it, but don’t want to go through the hassle of setting everything up yourself, having Copilot do it can be a real time saver.

And that’s… pretty much it. I’ve experimented with building applications with “prompt engineering,” and to be blunt, I think the concept is fundamentally flawed. The problem is that once the application exceeds the LLM’s context window size, which is necessarily small, you’re going to see it make a lot more mistakes than it already does, because - just as an example - by the time you’re having it write the frontend for a new API endpoint, it’s already forgotten how that endpoint works.

As the application approaches production size in features and functions, the number of lines of code becomes an insurmountable bottleneck for Copilot. It simply can’t maintain a comprehensive understanding of what’s already there.

Exactly. It’s not true. Any company that fires all of its developers and sets up some poor intern to prompt-engineer updates to their codebase is going to fail spectacularly.

Source: I’m a software developer and use LLMs regularly. There are certain tasks they are very good at, but anyone who commits unexamined code generated by an LLM gets exactly what they deserve.