Yes, and to add, in case it's not obvious: in my experience the maintenance, mental (and emotional costs, call me sensitive) cost of bad code compounds exponentially the more hacks you throw at it
Now with AI, you're not only dealing with maintenance and mental overhead, but also the overhead of the Anthropic subscription (or whatever AI company) to deal with this spaghetti. Some may decide that's an okay tradeoff, but personally it seems insane to delegate a majority of development work to a blackbox, cloud-hosted LLM that can be rug pulled from underneath of you at any moment (and you're unable to hold it accountable if it screws up)
Call me naive, but I don't believe that I'm going to wake up tomorrow and ChatGPT.com and Claude.ai are going to be hard down and never come back. Same as Gmail, which is an entirely different corporation. I mean, they could, but it doesn't seem insane to use Gmail for my email, and that's way more important to my life functioning than this new AI thing.
I'm pretty sure that will be true with AI as well.
No accounting for taste, but part of makes code hard for me to reason about is when it has lots of combinatorial complexity, where the amount of states that can happen makes it difficult to know all the possible good and bad states that your program can be in. Combinatorial complexity is something that objectively can be expensive for any form of computer, be it a human brain or silicon. If the code is written in such a way that the number of correct and incorrect states are impossible to know, then the problem becomes undecidable.
I do think there is code that is "objectively" difficult to work with.
There are a number of things that make code hard to reason about for humans, and combinatorial complexity is just one of them. Another one is, say, size of working memory, or having to navigate across a large number of files to understand a piece of logic. These two examples are not necessarily expensive for computers.
I don't entirely disagree that there is code that's objectively difficult to work with, but I suspect that the Venn diagram of "code that's hard for humans" and "code that's hard for computers" has much less overlap than you're suggesting.
Certainly with current models I have found that the Venn diagram of "code that's hard for humans" and "code that's hard for computers" has actually been remarkably similar, I suspect because it's trained on a lot of terrible code on Github.
I'm sure that these models will get better, and I agree that the overlap will be lower at that point, but I still think what I said will be true.
I wouldn't expect so. These machines have been trained on natural language, after all. They see the world through an anthropomorphic lens. IME & from what I've heard, they struggle with inexpressive code in much the same way humans do.
What do you think about the argument that we are entering a world where code is so cheap to write, you can throw the old one away and build a new one after you've validated the business model, found a niche, whatever?
I mean, it seems like that has always been true to an extent, but now it may be even more true? Once you know you're sitting on a lode of gold, it's a lot easier to know how much to invest in the mine.
It hasn't always been true, it started with rapid development tools in the late 90's I believe.
And some people thought they were building "disposable" code, only to see their hacks being used for decades. I'm thinking about VB but also behemoth Excel files.
I guess the question is, are the issues not worth fixing because implementing a fix is extremely expensive, or because the improvements from fixing it were anticipated to be minor? I assume the answer is generally a mix of the two.
Someone has to figure out how to make the experiences of the two generations consistent in the ways it needs to be and differ only in the ways it doesn't still.
The tl;dr of this is that I don't think that the code itself is what needs to be preserved, the prompt and chat is the actual important and useful thing here. At some point I think it makes more sense to fine tune the prompts to get increasingly more specific and just regenerate the the code based on that spec, and store that in Git.
> At some point I think it makes more sense to fine tune the prompts to get increasingly more specific and just regenerate the the code based on that spec, and store that in Git.
Generating code using a non-deterministic code generator is a bold strategy. Just gotta hope that your next pull of the code slot machine doesn’t introduce a bug or ten.
Observability into how a foundation model generated product arrived to that state is significantly more important than the underlying codebase, as it's the prompt context that is the architecture.
Because LLMs are designed as emulators of actual human reasoning, it wouldn't surprise me if we discover that the things that make software easy for humans to reason about also make it easier for LLMs to reason about.
It’s also possible to sell chairs that are uncomfortable and food that tastes terrible. Yet somehow we still have carpenters and chefs; Herman Miller and The French Laundry.
Some business models will require “good” code, and some won’t. That’s how it is right now as well. But pretending that all business models will no longer require “good” code is like pretending that Michelin should’ve retired its list after the microwave was invented.
Those high end restaurants are more like art and exploration of food then something practical like code. The only similarity is maybe research in academia. There's not real industry uses of code that's like art.
I used the extreme of the spectrum, I can’t imagine you’re arguing that food is binary good / bad? There’s a litany of food options and quality, matching different business models of convenience and experience.
Research in academia seems less appropriate because that’s famously not really a business model, except maybe in the extractive sense
There's no equivalent of experience and art in code. Writing code is not expressing your self, and you don't pay for pushing the limits and experimenting with it. That's what high end restaurants are along with service they provide.
As far as good or bad, how food is made is irreverent to the outcome if it's enjoyable.
Still, talk about "good" code exist for a reason. When the code is really bad, you end up paying the price by having to spend too more and more time and develop new features, with greater risk to introduce bugs. I've seen that in companies in the past, where bad code meant less stability and more time to ship features that we needed to retain customers or get new ones.
Now whether this is still true with AI, or if vibe coding means bad code no longer have this long term stability and velocity cost because AI are better than humans at working with this bad code... We don't know yet.
Not only true but I would guess it's the normal case. Most software is a huge pile of tech debt held together by zip-ties. Even greenfield projects quickly trend this way, as "just make it work" pressure overrides any posturing about a clean codebase.
A cornerstone of this community is "if you're not embarrassed by the first release you've waited too long", which is a recognition that imperfect code is not needed to create a successful business. That's why ShowHN exists.
It depends on the urgency. Not every product is urgent. CC arguable was very urgent; even a day of delay meant the competitors could come out with something slightly more appealing.
Most of their products are so large that you can easily find parts with very bad and parts with excellent code. I am not sure a whole ERP product could work with all very bad code, though.