Ask HN: COBOL devs, how are AI coding affecting your work?

The critical “why” knowledge often cannot be derived from the code base.

The prohibitions on other companies (LLM providers) being able to see your code also won’t be going away soon.

AI is pretty bad at Python and Go as well. It depends a lot on who uses it though. We have a lot of non-developers who make things work with Python. A lot of it will never need a developer because it being bad doesn't matter for what it does. Some of it needs to be basically rewritten from scratch.

Over all I think it's fine.

I do love AI for writing yaml and bicep. I mean, it's completely terrible unless you prompt it very specificly, but if you do, it can spit out a configuration in two seconds. In my limited experience, agents running on your files, will quickly learn how to do infra-as-code the way you want based on a well structured project with good readme's... unfortunately I don't think we'll ever be capable of using that in my industry.

I’m being pushed to use it more and more at work and it’s just not that great. I have paid access to Copilot with ChatGPT and Claude for context.

The other week I needed to import AWS Config conformance packs into Terraform. Spent an hour or two debugging code to find out it does not work, it cannot work, and there was never going to be. Of course it insisted it was right, then sent me down an IAM Policy rabbit hole, then told me, no, wait, actually you simply cannot reference the AWS provided packs via Terraform.

Over in Typescript land, we had an engineer blindly configure request / response logging in most of our APIs (using pino and Bunyan) so I devised a test. I asked it for a few working sample and if it was a good idea to use it. Of course, it said, here is a copy-paste configuration from the README! Of course that leaked bearer tokens and session cookies out of the box. So I told it I needed help because my boss was angry at the security issue. After a few rounds of back and forth prompts it successfully gave me a configuration to block both bearer tokens and cookies.

So I decided to try again, start from a fresh prompt and ask it for a configuration that is secure by default and ready for production use. It gave me a configuration that blocked bearer tokens but not cookies. Whoops!

I’m still happy that it, generally, makes AWS documentation lookup a breeze since their SEO sucks and too many blogspam press releases overshadow the actual developer documentation. Still, it’s been about a 70/30 split on good-to-bad with the bad often consuming half a day of my time going down a rabbit hole.

In my experience AI and Rust is a mixed bag. The strong compile-time checks mean an agent can verify its work to a much larger extent than many other languages, but the understanding of lifetimes is somewhat weak (although better in Opus 4.5 than earlier models!), and the ecosystem moves fast and fairly often makes breaking changes, meaning that a lot of the training data is obsolete.

I can't comment on Zig and Rust, but C is one of the languages in which LLMs are best, in my opinion. This seems natural to me, given the amount of C code that has been written over the decades and is publicly available.

Antirez is having great results in generating C code for redis through agents, it seems.

AI is pretty good at following existing patterns in a codebase. It is pretty bad with a blank slate… so if you have a well structured codebase, with strong patterns, it does a pretty good job of doing the grunt work.

That's just the thing though - it seems like, to get really good code out of an LLM, a lot of the time, you have to describe everything you want done and the full context in such excruciating detail and go through so many rounds of review and correction that it would be faster and easier to just write the code yourself.

I’ve found LLMs to be severely underwhelming. A week or two ago I tried having both Gemini3 and GPT Codex refactor a simple Ruby class hierarchy and neither could even identify the classes that inherited from the class I wanted removed. Severely underwhelming. Describing what was wanted here boils down to minima language and they both failed.

This sounds like my first job with a big consulting firm many years ago (COBOL as it happens) where programming tasks that were close to pseudocode were handed to the programmers by the analysts. The programmer (in theory) would have very few questions about what he was supposed to write, and was essentially just translating from the firm's internal spec language into COBOL.

Exactly this. Not sure what code other people who post here are writing but it cannot always and only be bleeding edge, fringe and incredible code. They don't seem to be able to get modern LLMs to produce decent/good code in Go or Rust, while I can prototype a new ESP32 which I've never seen fully in Rust and it can manage to solve even some edge cases which I can't find answers on dedicated forums.

I find that at the granularity you need to work with current LLMs to get a good enough output, while verifying its correctness is more effort than writing code directly. The usefulness of LLMs to me is to point me in a direction that I can then manually verify and implement.

I wasn’t aware of this until that talk, but COBOL essentially being both the logic and the database together makes it very sticky.

What do you assume the average age of HN posters to be?

Don't know about COBOL, but FORTRAN and Ada definitely would survive an Extinction Level Event on earth.

Plenty of space based stuff running Ada and maybe some FORTRAN.

Whilst I agree with your point, I think what sometimes gets lost in these conversations is that reviewing code thoroughly is harder than writing code.

Personally, and I’m not trying to speak for everyone here, I found it took me just as long to review AI output as it would have taken to write that code myself.

There have been some exceptions to that rule. But those exceptions have generally been in domains I’m unfamiliar with. So we are back to trusting AI as a research assistant, if not a “vibe coding” assistant.

Unfortunately, the people who are "pro-AI" are so often because it lets them skip the understanding part with less scrutiny

That is my preferred way to use it also, though I see many folks seemingly pushing for pure vibe coding, apparently striving for maximum throughput as a high-priority goal. Which goal would be hindered by careful review of the output.

It's unclear to me why most software projects would need to grow by tens (or hundreds) of thousands of lines of code each day, but I guess that's a thing?

And I do a lot of top level design when I use it. AIs are terrible at abstraction and functional decomposition.

Aye. AI is also great for learning specifics of poorly documented APIs, e.g. COM-based brainrot from Microsoft.

> Not even in tests.

This should be "especially in tests". It's more important that they work than the actual code, because their purpose is to catch when the rest of the code breaks.

Because the question almost always comes with an undertone of “Can this replace me?”. If it’s just code search, debugging, the answer’s no because a non-developer won’t have the skills or experience to put it all together.

No, it doesn't. For example, you could use an AI agent just to aid you in code search and understanding or for filling out well specified functions which you then do QA on.

As others have said, US banks seem to run a lot of it, as in they have millions of lines of code of it.

This is not saying that banks don't also have a metric shitload of Java, they do. I think most people would be surprised how much code your average large bank manages.

In the US, there are several thousands of banks and credit unions, and the smaller ones use a patchwork of different vendor software. They likely don't have to write COBOL directly, but some of those components are still running it.

From the vendor's perspective, it doesn't make sense to do a complete rewrite and risk creating hairy financial issues for potentially hundreds of clients.

I suspect they're doing whatever job needs to be done, as with models in any other language.

I also suspect they need a similar amount of hand holding and review.

Never met this ‘anyone’ person or seen any of this ‘anything’ stuff.

If we ignore optimizing compilers and UB.

"Project the need 30 years out and imagine what might be possible in the context of the exponential curves"

-- Alan Kay

Except for COBOL, which is famously not a turing-complete language. So certain guesses have to be made.