More tokens -> more compute involved. Attention-based models work by attending every token with each other, so more tokens means not only having more time to "think" but also being able to think "better". That is also at least part of the reason why o1/o3/R1 can sometimes solve what other LLMs could not.

Anyway, I don't think any of the current LLMs are really good for coding. What it's good at is copy-pasting (with some minor changes) from the massive code corpus it has been pre-trained. For example, give it some Zig code and it's straight unable to solve even basic tasks. Same if you give it really unique task, or if you simply ask for potential improvements of your existing code. Very, very bad results, no signs of out-of-box thinking whatsoever.

BTW: I think what people are missing is that LLMs are really great at language modeling. I had great results, and boosts in productivity, just by being able to prepare the task specification, and do quick changes in that really easily. Once I have a good understanding of the problem, I can usually implement everything quickly, and do it in much much better way than any LLM can currently do.

It doesn't hurt that the model vendors get paid by the token, so there's zero incentive to correct this pattern at the model layer.

What’s worse, I get a lot of comments left saying what the AI did, not what the code does or why. Eg “moved this from file xy”, “code deleted because we have abc”, etc. Completely useless stuff that should be communicated in the chat window, not in the code.

LLMs are also good at commenting on existing code.

It’s trivial to ask Claude via Cursor to add comments to illustrate how some code works. I’ve found this helpful with uncommented code I’m trying to follow.

I haven’t seen it hallucinate an incorrect comment yet, but sometimes it will comment a TODO that a section should be made more more clear. (Rude… haha)

> If the code works, passes all relevant tests, is reasonably maintainable, and can be fitted into the system correctly with a well defined interface, does it really matter?

You're not wrong here, but there's a big difference in programming one-off tooling or prototype MVPs and programming things that need to be maintained for years and years.

We did this song and dance pretty recently with dynamic typing. Developers thought it was so much more productive to use dynamically typed languages, because it is in the initial phases. Then years went by, those small, quick-to-make dynamic codebases ended up becoming unmaintainable monstrosities, and those developers who hyped up dynamic typing invented Python/PHP type hinting and Flow for JavaScript, later moving to TypeScript entirely. Nowadays nobody seriously recommends building long-lived systems in untyped languages, but they are still very useful for one-off scripting and more interactive/exploratory work where correctness is less important, i.e. Jupyter notebooks.

I wouldn't be surprised to see the same pattern happen with low-supervision AI code; it's great for popping out the first MVP, but because it generates poor code, the gung-ho junior devs who think they're getting 10x productivity gains will wisen up and realize the value of spending an hour thinking about proper levels of abstraction instead of YOLO'ing the first thing the AI spits out when they want to build a system that's going to be worked on by multiple developers for multiple years.

what are you going to do when something suddenly doesn't work and cursor endlessly spins without progress no matter how many "please don't make mistakes" you add? delete the whole thing and try to one-shot it again?

Good insight, and indeed quite exactly my state of mind while creating this particular solution.

Iin this case, I did put in the guard rails to ensure that I reach my goal in hopefully a straight line and a quickly as possible, but to be honest, I did not give much thought to long-term maintainability or ease of extending it with more and more features, because I needed a very specific solution for a use case that doesn't change much.

I'm definitely working differently in my brown-field projects where I'm intimately familiar with the tech stack and architecture — I do very thorough code reviews afterwards.

I think this code is at least twice the size than it needs to be compared to nicer, manually produced Python code: a lot of it is really superfluous.

People have different definitions of "reasonably maintainable", but if code has extra stuff that provides no value, it always perplexes the reader (what is the point of this? what am I missing?), and increases cognitive load significantly.

But if AI coding tools were advertised as "get 10x the output of your least capable teammate", would they really go anywhere?

I love doing code reviews as an opportunity to teach people. Doing this one would suck.

Right, and the reason why professional developers are writing worse code out there is most likely because they simply don't have the time/aren't paid to care more about it. The LLM is then mildly improving the output in this brand of common real world scenario

I do believe it's amazing what we can build with AI tools today.

But whenever someone advertises how an expert will benefit from it yet they end up with crap, it's a different discussion.

As an expert, I want AI to help me produce code of similar quality faster. Anyone can find a cheaper engineer (maybe five of them?) that can produce 5-10x the code I need at much worse quality.

I will sometimes produce crappy code when I lack the time to produce higher quality code: can AI step in and make me always produce high quality code?

That's a marked improvement I would sign up for, and some seem to tout, yet I have never seen it play out.

In a sense, the world is already full of crappy code used to build crappy products: I never felt we were lacking in that department.

And I can't really rejoice if we end up with even more of it :)

I think there’s a difference in that this is about as good as LLM code is going to get in terms of code quality (as opposed to capability a la agentic functionality). LLM output can only be as good as its training data, and the proliferation of public LLM-generated code will only serve as a further anchor in future training. Humans on the other hand ideally will learn and improve with each code review and if they don’t want to you can replace them (to put it harshly).

I don't see the difference there. Whether I've written all the code myself or an AI wrote all of it, my name will be on the commit. I'll be the person people turn to when they question why code is the way it is. In a pull request for my commit, I'll be the one discussing it with my colleagues. I can't say "oh, the AI wrote it". I'm responsible for the code. Full stop.

If you're in a team where somebody can continuously commit trash without any repercussions, this isn't a problem caused by AI.