Measuring the sloppiness of code

Disagree. I have 200k LOC now plus 100k in tests, and it is still performing like it was four months ago when I started to seriously use AI.

If anything, it works more reliably today with the smarter models.

Coding is solved, perhaps, with unlimited token spend on a frontier model. It remains to be seen if it that is prohibitively expensive forever. At my company, we token maxed while the getting was good. But when we had to switch to Anthropic's enterprise plan, and start paying per token, the shit really hit the fan. Now we're retreating back to sane cost levels and finding that - guess what? - people power might just be more cost effective. AI of course is an immense tool to leverage, but still too expensive to create loops and let it run. This will change over time of course, but assuming it is a solved problem is nonsense. Maybe if we solve cold fusion, yes. Until then, evolution is winning the war on entropy.

The shadow of Chesterton's Fence is relevant. The LLM doesn't know why code exists, if it doesnt start traversing up the scope of a project. Even then, it can't be sure that the code isn't a dependency of something else outside the project (especially if there's a side effect). I'm not sure it's ever going to be easy to address this concern in a straightforward and portable way.

I do sometimes see duplicate functions, which is troubling.

Are you saying that pure vibe coding by a non-technical person produces better code than pre-LLM developers, or that experienced dev + AI produces better code?

Both of those things are very different, and AI shouldn't be the one taking the credit if it's the second case.

I have no idea where most people writing code have worked at but in all product and platform teams I worked at the code quality has been much higher than the latest slop SOTA llms can output.

TLDR: coding is not solved.

I have 2 projects, one it's a distributed platform, the other one is a general processing engine with an inner workflow engine; Since gpt 5.2 I've tried new models to work in these codebases where the code is of good quality and every time I gave the model a slice of work instead of a single step from that slice the code, the tests, the comments, the docs and everything else has been suboptimal, unmaintainable, complex, bloated and just slop, unless I micro-manage and do many passes.

As a dev when you make a change you consider the broad picture, you consider the user, the codebase, future requirements, maintainability, performance, your team's understanding and some of these you do unconsciously. We are slow but that's for multiple good reasons, you push the organization/understanding forward not just loc of that specific project. I can't count how many PR notes or comments I've added considering teammates or just for a specific team member.

I don't see any way forward for an LLM to reach that unless it reaches general problem solving, my definition of GAI that could tackle software development or "coding" would be a model that doesn't require additional pretraining to solve new tasks or improve how it solves tasks in the future, it would just learn as it's going.

Can everything I mentioned be solved with current generation of LLMs and lot's of markdown and gates? Maybe... but the amount of effort required would be similar to the effort an expert system (pre-llm AI) would require to embed the rules, evolve them, check them everytime... which would require billions or trillions of tokens.

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off: I really like the discussions around how to prevent slop and bloated code as it's something it would benefit coding even without LLMs and can fit as another piece of automated infra for checking and ensuring code quality, I hope something materializes.