[Dr_Birdbrain]

My feeling is that AI-generated code is disposable code.

It’s great if you can quickly stand up a tool that scratches an itch for you, but there is minimal value in it for other people, and it probably doesn’t make sense to share it in a repo.

Other people could just quickly vibe-code something of equal quality.

[thewebguyd]

That's how I've been using and treating it, though I'm not primarily a developer. I work in ops, and LLMs write all sorts of disposable code for me. Primarily one-off scripts or little personal utilities. These don't get shared with anyone else, or put on github, etc. but have been incredibly helpful. SQL queries, some python to clean up or dig through some data sets, log files, etc. to spit out a quick result when something more robust or permanent isn't needed.

Plus, so far, LLMs seem better at writing code to do a thing over directly doing the thing, where it's more likely to hallucinate, especially when it comes to working with large CSV or Json files. "Re-order this CSV file to be in Alphabetical order by the Name field" will make up fake data, but "Write a python script to order the Name filed in this CSV to be alphabetical" will succeed.

[QuercusMax]

That's exactly my experience as well. AI will read only the first 100 lines of a file, decide that's good enough, and spit out a garbage result. But ask it to write a bash one-liner and it will work perfectly.

[knollimar]

I've had large successes in using it to draft electrical drawings faster (more a symptom of the tools I have now being mediocre)

[WorldMaker]

My growing (cynical) feeling is that AI-generated code is legacy-code-as-a-service. It is by nature trained on other people and company's legacy code. (There's the training set window which is always in the past. There's the economics question of which companies would ever volunteer to opt-in their best proprietary production code into training sets. Sure there are a few entirely open source companies, but those are still the exception and not the rule.) "Vibe code" is essentially delivered as Day Zero "Legacy Code" in the sense that the person who wrote that code is essentially no longer at the company (even if context windows get extended to incredibly huge sizes and you have great prompt preservation tools, eventually you no longer have the original context and not to mention that the Models themselves retrain and get upgraded every so many months are essentially "different people" each time. But most importantly the Models themselves can't tell you the motivating "how" or "why" of anything, at best maybe good specs documents and prompts do, but even that can be a gamble).

The article starts with a lot of words about how the meaning and nature of "tech debt" are going to change a lot as AI adoption increases and more vibe coding happens, but I think I disagree on what that change means. I don't AI reduces "tech debt". I don't think it is "deflationary" in any way. I think AI are going to gift us a world of tech debt "hyperinflation". When every application in a company is "legacy code" all you have is tech debt.

Having worked in companies with lots of legacy code, the thing you learn is that those apps are never as disposable as you want to believe. The sunk cost fallacy kicks in. (Generative AI Tokens are currently cheap, but cheap isn't free. Budgets still exist.) Various status quo fallacies kick in: "that's how the system has always worked", "we have to ensure every new version is backwards compatible with the old version", "we can't break anyone's existing process/workflow", "we can't require retraining", "we need 1:1 all the same features", and so forth.

You can't just "vibe code" something of equal quality if you can't even figure out what "equal quality" means. That's many the death of a legacy code "rewrite project". By the time you've figured out how every user uses it (including how many bugs are load-bearing features in someone's process) you have too many requirements to consider, not enough time or budget left, and eventually a mandate to quit and "not fix what isn't broken". (Except it was broken enough to start up a discovery process at least once, and may do so again when the next team thinks they can dream up a budget for it.)

Tech debt isn't going away and tech debt isn't getting eliminated. Tech Debt is getting baked into Day Zero of production operations. (Projects may be starting already "in hock to creditors". The article says "Dark Software Factory" but I read "Dark Software Pawn Shop".) Tech debt is potentially increasing at a faster than human scale of understanding it. I feel like Legacy Code skills are going to be in higher demand than ever. It is maybe going to be "deflationary" in cost for those jobs but only because the supply of Legacy Code projects will be so high and software developers will have a buffet to choose from.

[wordpad]

I don't see why AI would be able to help you solve all your legacy code problems.

It still struggles making changes to large code bases, but it doesn't have any problems explaining those code bases to you helping you research or troubleshoot functionality 10x faster, especially if you're knowledgable enough not to take it at its responses as gospel but willing to have the conversation. A simple layman prompt of "are you sure X does Y for Z reason? Then what about Q?" will quickly get to them bottom of any functionality. 1 million token context window is very capable if you manage that context window properly with high level information and not just your raw code base.

And once you understand the problem and required solution, AI won't have any problems producing high quality working code for you, be it in RUST or COBOL.