166 points by zkid18 2 days ago
Curious to hear from anyone actively working with COBOL/mainframes. Do you see LLMs as a threat to your job security, or the opposite?
I feel that the mass of code that actually runs the economy is remarkably untouched by AI coding agents.
To be fair, I would not expect a model to output perfectly formatted C++. I’d let it output whatever it wants and then run it through clang-format, similar to a human. Even the best humans that have the formatting rules in their head will miss a few things here or there.
If there are 40 years of undocumented business quirks, document them and then re-evaluate. A human new to the codebase would fail under the same conditions.
Formatting isn't just visual, in pre-79 COBOL or Fortran. It's syntax. Its a compile failure, or worse, it cuts the line and can sometimes successfully compile into something else.
Thats not just an undocumented quirk, but a fundamental part of being a punch-card ready language.
With C++ formatting is optional. A better test case for LLMs is Python where indention specifies code blocks. Even ChatGPT 3.5 got the formatting for Python and YAML correct - now the actual code back then was often hilariously wrong.
A quick search finds many COBOL checkers. I’d be very surprised if a modern model was not able to fix its own mistakes if connected to a checker tool. Yes, it may not be able to one shot it perfectly, but if it can quickly call a tool once and it “works”, does it really matter much in the end? (Maybe it matters from a cost perspective, but I’m just referring to it solving the problem you asked it to solve.)
Clearly it isn’t just “broken” for everyone, “Claude Code modernizes a legacy COBOL codebase”, from Anthropic:
Taking Anthropic reporting on Anthropic, at face value, is not something you should really do.
In this case, a five stage pipeline, built on demo environments and code that were already in the training data, was successful. I see more red flags there, than green.
Nuances of a codebase are the key. But I guess we are accelerating towards solving that. Let's see how much time will this take.
I've not found it that great at programming in cobol, at least in comparison to its ability with other languages it seems to be noticeably worse, though we aren't using any models that were specifically trained on cobol. It is still useful for doing simple and tedious tasks, for example constructing a file layout based on info I fed it can be a time saver, otherwise I feel it's pretty limited by the necessary system specifics and really large context window needed to understand what is actually going on in these systems. I do really like being able to feed it a whole manual and let it act as a sort of advanced find. Working in a mainframe environment often requires looking for some obscure info, typically in a large PDF that's not always easy to find what you need, so this is pretty nice.
AI isn’t particularly great with C, Zig, or Rust either in my experience. It can certainly help with snippets of code and elucidate complex bitwise mathematics, and I’ll use it for those tedious tasks. And it’s a great research assistant, helping with referencing documentation. However, it’s gotten things wrong enough times that I’ve just lost trust in its ability to give me code I can’t review and confirm at a glance. Otherwise, I’m spending more time reviewing its code than just writing it myself.
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.
If it's bad at python the most popular language what language it's good at? If you see the other comments they're basically mentioning most programming languages
> AI is pretty bad at Python and Go as well.
It great in Golang IF its one shot tasks. LLMs seem to degrade a lot when they are forced to work on existing code bases (even their own). What seems to be more a issue with context sizes growing out of control way too fast (and this is what degrades LLMs the most).
So far Opus 4.5 has been the one LLM that keeps mostly coding in a, how to say, predictable way even with a existing code base. It requires scaffolding and being very clear with your coding requests. But not like the older models where they go off script way too much or rewrite code in their own style.
For me Opus 4.5 has reached that sweet spot of productivity and not just playing around with LLMs and undoing mistakes.
The problem with LLMs is a lot of times a mix of LLM issues, people giving different requests, context overload, different models doing better with different languages, the amount of data it needs to alter etc... This makes the results very mixed from one person to another, and harder to quantify.
Even the different in a task makes the difference between a person one day glorifying a LLM and a few weeks later complaining it was nerfed, when it was not. Just people doing different work / different prompts and ...
I’ve found claide code to be amazing at go. This is all nuts because experiences it’s so different from person to another.
> AI is pretty bad at Python and Go as well
I disagree with this. At least for Go.
with all those languages listed in this thread,it explains why I don't trust or use AI when I code.
That's basically all the languages that I am using...
For the AI fans in here, what languages are you using? Typescript only would be my guess?
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.
Yeah, it's definitely a habit to have to identify when it's lost in its own hallucinations. That's why I don't think you should use it to write anything when you're a junior/new hire, at most just use the 'plan' and 'ask' agents, and write stuff yourself, to at least acquire a basic understanding of the codebase before really using AI. Basically if you're a .5x dev (which honestly, most of us are on a new environment), it'll make you a .25x, and make you stay there longer.
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.
The weakness goes beyond lifetimes. In Rust programs with non-trivial type schemas, it can really struggle to get the types right. You see something similar with Haskell. Basically, proving non-trivial correctness properties globally is more difficult than just making a program work.
Definitely disagree. It can regurgitate solved problems from open source codebases, sure. Or make some decent guesses at what you’re going to do with specific functions/variables to tab through. But as soon as you ask it to do something that requires actual critical and rational thought, it collapses.
Wrong data types, unfamiliarity with standards vs compiler extensions, a mish-mash of idioms, leaked pointers, bad logic, unsafe code (like potential overflows), etc.
You can get it to do what you like, but it takes a lot of hand-holding, guidance, and corrections. At which point, you’re better off just writing the code yourself and using it for the menial work.
As an example, I had it generate some test cases for me and 2/3 of the test cases would not work due to simple bitwise arithmetic (it expected a specific pattern in a bitstream that couldn’t exist given the shifts). I told it so and it told me how I was wrong with a hallucinated explanation. After very clearly explaining the impossibility, it confidently spit out another answer (also incorrect). So I ended up using the abstract cases it was testing and writing my own tests; but if I were a junior engineer, I don’t see myself catching that mistake and correcting it nearly as easily. Instead wasting time wondering what is wrong with my code.
Sounds like rubocop though. I used that years ago to update an old legacy ruby codebase. Is that still AI?
Had the opposite experience using LLMs with C. Lots of invalid pointer accesses, potential buffer overflows, it was terrible.
Sounds like regular C programming, lol. On a serious note, give Opus 4.5 a try, maybe it would feel better. I’ve experimented with C the other week and it was quite fun. Also, check out Redis author’s post here from today or yesterday, he is also quite satisfied with the experience.
AI copilots and prompts give me massive lines of imperative OCaml and the interface for that code always requires changing to properly describe the data it will receive when I can write it myself in a few minutes. I can however write a simulation of some hardware quickly with Java or C using claude code and then run my hand written programs in there for testing. An example is mimicking the runtime environment of some controller
Antirez is having great results in generating C code for redis through agents, it seems.
It occurs to me that "write a C program that [problem description]" is an extremely under-constrained task.
People are highly aware that C++ programmers are always using some particular subset of C++; but it's not as obvious that any actual C programmer is actually going to use a particular dialect on top of C.
Since the C standard library is so anemic for algorithms and data structures, any given "C programmer" is going to have a hash map of choice, a b-tree of choice, a streams abstraction of choice, an async abstraction of choice, etc.
And, in any project they create, they're going to depend on (or vendor in) those low-level libraries.
Meanwhile, any big framework-ish library (GTK, OpenMP, OpenSSL) is also going to have its own set of built-in data structures that you have to use to interact with it (because it needs to take and return such data-structures in its API, and it has to define them in order to do that.) Which often makes it feel more correct, in such C projects, to use that framework's abstractions throughout your own code, rather than also bringing your own favorite ones and constantly hitting the impedance wall of FFI-ing between them.
It's actually shocking that, in both FOSS and hiring, we expect "experienced C programmers" who've worked for 99% of their careers with a dialect of C consisting of abstractions from libraries E+F+G, to also be able to jump onto C codebases that instead use abstractions from libraries W+X+Y+Z (that may depend on entirely different usage patterns for their safety guarantees!), look around a bit, and immediately be productively contributing.
It's no wonder an AI can't do that. Humans can barely do it!
My guess is that the performance of an AI coding agent on a greenfield C project would massively improve if you initially prompt it (or instruct it in an AGENTS.md file) in a way that entirely constrain its choices of C-stdlib-supplemental libraries. Either by explicitly listing them; or by just saying e.g. "Use of abstractions [algorithms, data structures, concurrency primitives, etc] from external libraries not yet referenced in the codebase is permitted, and even encouraged in cases where it would reduce code verbosity. Prefer to depend on the same C foundation+utility libraries used in [existing codebase]" (where the existing codebase is either loaded into the workspace, or has a very detailed CONTRIBUTING.md you can point the agent at.)
Heard an excellent COBOL talk this summer that really helped me to understand it. The speaker was fairly confident that COBOL wasn't going away anytime soon.
https://www.youtube.com/watch?v=RM7Q7u0pZyQ&list=PLxeenGqMmm...
First of all, that conference is right down the road from me, and I never knew about it. So, thanks for sharing!
My first job was working at a credit union software company. I designed and built the front-end (windows applications, a telephone banking system, and a home-banking web thing) and middle-tier systems (VB.NET-based services). The real back-end, though, was an old COBOL system.
I remember helping the COBOL programmers debug some stuff, and it was just so wildly foreign. My degree is in theoretical comp sci, and I'd seen a lot of different languages, including Prolog, various lisps and schemes, SQL, ADA, C++, C, Pascal, various assembly variants, but COBOL was simply unique. I've often wondered what ideas COBOL got right that we could learn from and leverage today in a new language.
I do remember our COBOL mainframes were really fast compared to the SQL Server layers my middle-tier services used, but I also remember looking at it and thinking it would be a giant pain to write (the numbers at the front of every line seemed like tedium that I would probably often get wrong).
Nice! Call for Speakers will be opening this week if you know anybody who may be interested. https://carolina.codes
You can check with the GnuCOBOL project, It works on Windows,MAC/Linux. Open Source and free. The discussion on the sourceforge page would be a good place to ask! https://sourceforge.net/p/gnucobol/discussion/
The COBOL layer had a TCP server somewhere in it (don’t know the details of that). We simply made TCP calls directly, sending and receiving fixed-sized records.
Both Fortran and COBOL will be here long after many of the current languages have disappeared. They are unique to their domains viz. Fortran for Scientific Computing and COBOL for Business Data Processing with a huge amount of installed code-base much of it for critical systems.
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.
The key to understanding their longevity lies in the fact that they were the earliest high-level languages invented at a time when all software was built for serious long-lived stuff viz. Banking, Insurance, Finance, Simulations, Numerical Analysis, Embedded etc. Computing was strictly Science/Mathematics/Business and so a lot of very smart domain experts and programmers built systems to last from the ground up.
There are many in-house tools (say at banks) where Java code generates... COBOL. It's wild: in the video you linked it's explained COBOL was meant for machines that don't exist anymore so COBOL is running inside emulators.
So you have Java code, generating COBOL code, that's then run on an emulator emulating an old IBM system that was meant to run COBOL. It's just wild.
Some of the tools are even front-facing users (bank employees): at times you can still see at some banks an employee running an app in a monochrome green-on-black text terminal emulator that is basically COBOL.
It's weird, just weird. But legacy code is legacy code. And if you think COBOL's legacy is bad, Java is going to dwarf COBOL's legacy big times (for Java is typically used at the kind of places that still use COBOL and it's used way more than COBOL).
So in the future, heck, we may have a new language, generating, inside an emulator emulating current machines/OSes, Java code that is going to be code generating COBOL code (!), that's then going to be run in an emulator.
cobol runs Everywhere. Windows Mac Linux free and open source. https://sourceforge.net/p/gnucobol/discussion/
In my experience working with large financial institutions and banks, there is plenty of running COBOL code that is around the average age of HN posters. Where as a lot of different languages code is replaced over time with something better/faster COBOL seems to have a staying power in financial that will ensure it's around a very very long time.
I wasn’t aware of this until that talk, but COBOL essentially being both the logic and the database together makes it very sticky.
Not COBOL but I sometimes have to maintain a large ColdFusion app. The early LLMs were pretty bad at it but these days, I can let AI write code and I "just" review it.
I've also used AI to convert a really old legacy app to something more modern. It works surprisingly well.
I feel like people who can't get AI to write production ready code are really bad at describing what they want done. The problem is that people want an LLM to one shot GTA6. When the average software developer prompts an LLM they expect 1) absolutely safe code 2) optimized/performant code 3) production ready code without even putting the requirements on credential/session handling.
You need to prompt it like it's an idiot, you need to be the architect and the person to lead the LLM into writing performant and safe code. You can't expect it to turn key one shot everything. LLMs are not at the point yet.
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.
I tried getting AI to update some JUnit 4 to Junit 5 - it replaced the JUnit 4 assertions with Java's built-in assert keyword. Very underwhelming.
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 have a sneaking suspicion that AI use isn't as easy as it's made out to be. There certainly seem to be a lot of people who fail to use it effectively, while others have great success. That indicates either a luck or a skill factor. The latter seems more likely.
What are your secrets? Teach me the dark arts!
There are wide gaps in:
1) the models people are using (default model in copilot vs. Opus 4.5 or Codex xhigh)
2) the tools people are using (ChatGPT vs. copilot vs. codex vs. Claude code)
3) when people tried these tools (e.g., December saw a substantial capability increase but some people only tried AI this one time last March)
4) how much effort people put into writing prompts (e.g., one vague sentence vs. a couple paragraphs of specific constraints and instructions)
Especially with all the hype, it makes sense to me why people have such different estimates for how useful AI actually is.
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.
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 really wouldn't want any vibe-coded COBOL in my bank db/app logic...
vibecoding != AI.
For example: I'm a senior dev, I use AI extensively but I fully understand and vet every single line of code I push. No exceptions. Not even in tests.
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.
> as long to review AI output as it would have taken to write that code myself
That is often the case.
What immensely helps though is that AI gets me past writer's block. Then I have to rewrite all the slop, but hey, it's rewrite and that's much easier to get in that zone and streamline the work. Sometimes I produce more code per day rewriting AI slop than writing it from scratch myself.
I think the point is in a banking context, every line of code gets reviewed thoroughly anyway.
Unfortunately, the people who are "pro-AI" are so often because it lets them skip the understanding part with less scrutiny
The good news here is that their code is of such a poor quality it doesn't properly work anyway.
I have recently tried to blindly create a small .dylib consolidation tool in JS using Claude Code, Opus 4.5 and AskUserTool to create a detailed spec. My god how awful and broken the code was. Unusable. But it faked* working just good enough to pass someone who's got no clue.
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.
> 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.
Does the use AI always implies slope and vibe coding? I’m really not sure
To do quality QA/code review, one of course needs to understand the design decisions/motivations/intentions (why those exact code lines were added, and why they are correct), meaning it is the same job as one would originally code those lines and building the understanding==quality on the way.
For the terminology, I consider "vibe-coding" as Claude etc. coding agents that sculpts entire blocks of code based on prompts. My use-tactic for LLM/AI-coding is to just get the signature/example of some functions that I need (because documents usually suck), and then coding it myself. That way the control/understanding is more (and very egoistically) in my hands/head, than in LLMs. I don't know what kind of projects you do, but many times the magic of LLMs ends, and the discussion just starts to go same incorrect circle when reflected on reality. At that point I need to return to use classic human intelligence.
And for COBOL + AI, in my experience mentioning "COBOL" means that there is usually DB + UI/APP/API/BATCHJOB for interacting with it. And the DB schema + semantics is propably the most critical to understand here, because it totally defines the operations/bizlogic/interpretations for it. So any "AI" would also need to understand your DB (semantically) fully to not make any mistakes.
But in any case, someone needs to be responsible for the committed code, because only personified human blame and guilt can eventually avert/minimize sloppiness.
You 100% can use it this way. But it takes a lot of discipline to keep the slop out of the code base. The same way it took discipline to keep human slop out.
There has always been a class of devs who throw things at the wall and see what sticks. They copy paste from other parts of the application, or from stack overflow. They write half assed tests or no tests at all and they try their best to push it thought the review process with pleas about how urgent it is (there are developers on the opposite side of this spectrum who are also bad).
The new problem is that this class of developer is the exact kind of developer who AI speeds up the most, and they are the most experienced at getting shit code through review.
That undertone is overt in the statements of CEOs and managers who salivate at “reducing headcount.”
The people who should fear AI the most right now are the offshore shops. They’re the most replaceable because the only reason they exist is the desire to carve off low skill work and do it cheaply.
But all of this overblown anyway because I don’t see appetite for new software getting satiated anytime soon, even if we made everyone 2x productive.
How many banks really use COBOL? Here in central Europe it seems to be Java, Java, Java for the most part. Since many years actually.
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.
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.
I'm in Australia and a friend of a friend had a COBOL job working at a mid-sized bank (the COBOL had lots of Java on top). Australia's big banks are older than this bank so if they're not using COBOL at the bottom layer, they'll be using something similarly old for sure.
I am working as a Software engineer in a European bank. There is a huge multi year program to remove COBOL as much as possible with cloud based Java Spring application.
The main reason is maintainability. There is no more cobol developers coming. Existing ones close to retirement or already retired.
You’re describing the pattern we’re seeing across most companies who are still on COBOL.
The shortage of COBOL engineers is real but the harder problem is enterprise scale system understanding. Most modernization efforts stall not because COBOL is inherently a difficult language, but because of the sheer scale and volume of these enterprise codebases. It's tens of thousands of files, if not millions, spanning 40+ years with a handful of engineers left or no one at all.
We're exploring some of this work at Hypercubic (https://www.hypercubic.ai/, YC-backed) if you're curious to learn more.
With the current reasoning models, we now have the capability to build large scale agentic AI for mainframe system understanding. This is going beyond line-by-line code understanding to reason across end-to-end system behavior and capturing institutional knowledge that’s otherwise lost as SMEs retire.
I’m not a full-time COBOL dev, but I’ve worked adjacent to mainframe systems (bank integrations, legacy batch jobs, and data pipelines).
From what I’ve seen, LLMs aren’t really a threat to COBOL roles right now. They can help explain unfamiliar code, summarize programs, or assist with documentation, but they struggle with the things that actually matter most: institution-specific conventions, decades of undocumented business logic, and the operational context around jobs, datasets, and JCL.
In practice, the hardest part isn’t writing COBOL syntax, it’s understanding why a program exists, what assumptions it encodes, and what will break if you change it. That knowledge tends to live in people, not in code comments.
So AI feels more like a force multiplier for experienced engineers rather than a replacement. If anything, it might reduce the barrier for newer engineers to approach these systems, which could be a net positive given how thin the talent pool already is.
There was a COBOL LLM eval benchmark published a few years ago, looks like it hasn’t been maintained: https://github.com/zorse-project/COBOLEval
At least I think that’s the repo, there was an HN discussion at the time but the link is broken now: https://news.ycombinator.com/item?id=39873793
I wonder if the OP's question is motivated by there being less public examples of COBOL code to train LLM's on compared to newer languages (so a different experience is expected), or something else. If the prior, it'd be interesting to see if having a language spec and a few examples leads to even better results from an LLM, since less examples could also mean less bad examples that deviate from the spec :) if there are any dev's that use AI with COBOL and other more common languages, please share your comparative experience
Most COBOL I know of won't ever see the light of day.
Also COBOL seems to have a lot of flavors that are used by a few financial institutions. Since these are highly proprietary it seems very unlikely LLMs would be trained on them, and therefore the LLM would not be any use to the bank.
COBOL migration is one of Devin's advertised capabilities:
https://docs.devin.ai/use-cases/examples/cobol-modernization https://cognition.ai/blog/infosys-cognition
I'm not in the COBOL world at all, but when I saw IBM putting out models for a while, I had to wonder if it was a byproduct of internal efforts to see if LLMs could help with the supposedly dwindling number of legacy mainframe developers. I don't know COBOL enough to be able to see if their Granite models are particularly strong in this area, though.
Not a COBOL dev, but I work on migrating projects from COBOL mainframes to Java.
Generally speaking any kind of AI is relatively hit or miss. We have a statically generated knowledge base of the migrated sourcecode that can be used as context for LLMs to work with, but even that is often not enough to do anything meaningful.
At times Opus 4.5 is able to debug small errors in COBOL modules given a stacktrace and enough hand-holding. Other models are decent at explaining semi-obscure COBOL patterns or at guessing what a module could be doing just given the name and location -- but more often than not they end up just being confidently wrong.
I think the best use-case we have so far is business rule extraction - aka understanding what a module is trying to achieve without getting too much into details.
The TLDR, at least in our case, is that without any supporting RAGs/finetuning/etc all kind of AI works "just ok" and isn't such a big deal (yet)
I am in banking and it's fine we have some finetuned models to work with our code base. I think COBOL is a good language for LLM use. It's verbose and English like syntax aligns naturally with the way language models process text. Can't complain.
Can you elaborate? See questions about what kind of use in sibling thread.
And in addition to the type of development you are doing in COBOL, I'm wondering if you also have used LLMs to port existing code to (say) Java, C# or whatever is current in (presumably) banking?
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.
If I were using something like Claude Code to build a COBOL project, I'd structure the scaffolding to break problems into two phases: first, reason through the design from a purely theoretical perspective, weighing implementation tradeoffs; second, reference COBOL documentation and discuss how to make the solution as idiomatic as possible.
Disclaimer: I've never written a single line of COBOL. That said, I'm a programming language enthusiast who has shipped production code in FORTRAN, C, C++, Java, Scala, Clojure, JavaScript, TypeScript, Python, and probably others I'm forgetting.
You may want to give free opensource GnuCOBOL a try. Works on Mac/Linux/Windows. As far as AI and Cobol, I do think Claude Opus 4.5 is getting pretty good. But like stated way above, verify and understand Every line it delivers to you.
Funny enough, I found ChatGPT to be pretty good at AppleSoft BASIC
Given the mass of code out there, it strikes me it's only a matter of time before someone fine tunes one of the larger more competent coding models on COBOL. If they haven't already.
Personally I've had a lot of luck Opus etc with "odd" languages just making sure that the prompt is heavily tuned to describe best practices and reinforce descriptions of differences with "similar" languages. A few months ago with Sonnet 4, etc. this was dicey. Now I can run Opus 4.5 on my own rather bespoke language and get mostly excellent output. Especially when it has good tooling for verification, and reference documentation available.
The downside is you use quite a bit of tokens doing this. Which is where I think fine tuning could help.
I bet one of the larger airlines or banks could dump some cash over to Anthropic etc to produce a custom trained model using a corpus of banking etc software, along with tools around the backend systems and so on. Worthwhile investment.
In any case I can't see how this would be a threat to people who work in those domains. They'd be absolutely invaluable to understand and apply and review and improve the output. I can imagine it making their jobs 10x more pleasant though.
> competent coding models on COBOL
Which COBOL... This is a particular issue in COBOL is it's a much more fragmented language than most people outside the industry would expect. While a model would be useful for the company that supplied the data, the amount of transference may be more limited than one would expect.
I see it as a complete opposite for sure, I will tell you why.
it could have been a threat if it was something you cannot control, but you can control it, you can learn to control it, and controlling it in the right direction would enable anyone to actually secure your position or even advance it.
And, about the COBOL, well i dont know what the heck this is.
Compiling high level languages to assembly is a deterministic procedure. You write a program using a small well defined language (relative to natural language every programming language is tiny and extremely well defined). The same input to the same compiler will get you the same output every time. LLMs are nothing like a compiler.
Is there any compiler that "rolls the dice" when it comes to optimizations? Like, if you compile the exact same code with the exact same compiler multiple times you'll get different assembly?
And th Alan Kay quote is great but does not apply here at all? I'm pointing out how silly it is to compare LLMs to compilers. That's all.
Except for COBOL, which is famously not a turing-complete language. So certain guesses have to be made.
The point about the mass of code running the economy being untouched by AI agents is so real. During my years as a developer, I've often faced the skepticism surrounding automation technologies, especially when it comes to legacy languages like COBOL. There’s a perception that as AI becomes more capable, it might threaten specialized roles. However, I believe that the intricacies and context of legacy systems often require human insight that AI has yet to master fully.
I logged my fix for this here: https://thethinkdrop.blogspot.com/2026/01/agentic-automation...
Compliance is usually the hard stop before we even get to capability. We can’t send code out, and local models are too heavy to run on the restricted VDI instances we’re usually stuck with. Even when I’ve tried it on isolated sandbox code, it struggles with the strict formatting. It tends to drift past column 72 or mess up period termination in nested IFs. You end up spending more time linting the output than it takes to just type it. It’s decent for generating test data, but it doesn't know the forty years of undocumented business logic quirks that actually make the job difficult.