I'm sure this is gated by where you work (especially by how technically savvy your manager is), but the most effective contributors at my job tend to be the ones with near-zero (or sub-zero!) net LoC.

LLMs are prolific and they love to add shit. Truly capable engineers are able to achieve more business outcomes with less code / fewer moving parts.

> It is now significantly harder to figure out who understands the systems and is using AI effectively and who doesn't know shit and is just slinging LLM copypasta around.

It's okay, I'm sure the algorithm questions during the interview phase totally weeded out the fakers of systems knowledge right?

There are also people who think AI is magic. I have often heard - "we want to use AI to automate processes but we don't have full documentation about the processes, we hope AI can help". Despite being told that no one can create outputs from thin air - every AI topic turns into the same discussion.

Often the solution to create those document to feed into these AI automations? Use AI. Its like ouroboros. Create docs using AI, then summarize and ingest using AI, explained by AI.

Same thing is going to happen with code. Create 1000s of line of code using AI. Then explain it using AI etc.

Maybe the solution is to look out for the most silent engineers. Those that output less despite having the ability to create near infinite output.

I think it depends a little on how and where you work. In the energy industry of Europe where we are extremely regulated AI has been writing some excellent and maintainable code. Of course we can't do any of that CLEAN SOLID DRY stuff, or any abstraction and implicity really, and I imagine that AI would struggle with that. Though you have to wonder if any of those religions ever really worked when you consider that they've still failed to replace most COBOL systems 30 years later. Anyway, that's a different discussion and even Uncle Bob has moved on to functional programming.

I've yet to have Opus 4.8 fail me with defensive explict code. Often it'll write code that is better than what I might have done. I imagine it would be a nightmare to go through one of the OOP debug chains with implict error handling, but when every function has a runtime assertion which is basically the contract for how it is supposed to work and exactly what to do if it encounters a corrupt state, then things are just so much easier with AI.

I do agree with you on documentation. The amount we have has exploded in the post AI world. Which is a little ironic since the assertion is frankly what you'll need to know and not the 10 pages of prose the AI autogenerated in the shared loop (microsoft's terrible confluence). It is what it is though, and at least it's easier to meet EU compliance rules now, since those are more about the bureaucracy than actual security.

>This is mostly due to incredible pressure from the C-level for every engineer to be using as much AI as possible

I think this is an important point. Software engineers always had the right instincts on how to approach AI for coding -- cautiously. Execs got too coked up on LinkedIn puff pieces from nobodies and adver-prophesizing CEOs selling their tokens and chips that they forced something unnatural upon their orgs.

Now what we see in the software dev space is incredible levels of malicious compliance ("you want slop, I'll give you slop").

Eh, are you using the loop engineering effectively? Remember, Boris can merge more than 200 PRs a week mostly with merely a phone and despite his busy schedule. Anthropic engineers do not write code manually any more, and they don't need to review most of the code either.

In all seriousness, though, I'm indeed curious about Anthropic's engineering practice, particular how they can achieve such level of autonomy.

AI usage perhaps will have to be monitored by AI.

This game-theory phase seems to go hand-in-hand with totally myopic grift/gig/hustle thinking too. There is no product but the confidence act needed to win each moment.

Chalk up yet another echo of the 1920s Gilded Age? Between all these economic spasms and the simultaneous tilting towards fascism, I think there is way too much historical rhyming going on right now...

So, in other words, all the "awesome engineers" can't really tell good code from bad unless it's really obvious? Why should we listen to you about AI code being crap, then? Maybe, in the end, you don't really know. Maybe AI is better at it than you?