Comment by arka2147483647
2 days ago
Often the biggest blocker on moving to a new programming language, is the cost of re-writing everything.
Cue some story here on a bank or airline somewhere still relying on cobol backend servers.
These LLM conversions really seem to make modernization of large parts software layers possible!
I have some familiarity with the bank situation, and while a lot of them are on some very old systems (maybe COBOL, maybe something else, either way they want off it) the cost of actually re-writing the code is far from the most significant issue.
Consider: You have a big mainframe running your tier 1 bank. Assume that you can see all the code on it, and you can feed all that to an LLM if you like. Getting it to spit out a Rust version is not what you actually want - you now have a modern language but it's still a singleton instance, so where do you run it? Most hardware doesn't give you enough uptime for what you need here, because what you actually needed was a re-architecture for distribution / failover / whatever, and while you could ask your LLM to do that you aren't going to run your bank on the result.
> while you could ask your LLM to do that you aren't going to run your bank on the result.
Why not?
I feel like we're entering a new era of prejudice against not a category of humans, but against non-human intelligences.
The design patterns for distributed and fault-tolerant systems are well-known and established in the industry. Both humans and AIs are familiar with them!
So if you sketch a design for the AI to follow, establish the rules in AGENTS.md, have a robust test suite, use a frontier model dialed up to eleven, etc... why not rely on the LLM output?
At the end of the day, humans are not without fault either.
I've been wading through some legacy "pre-AI" code recently and it has more bugs than a rainforest! Static fields used incorrectly, causing data races. Floating point types used for money amounts. JavaScript and SQL injection up the wazoo. Wildly unsafe password handling. So on, and so forth. This is the norm for most human-written software, not the exception.
As a proof-of-concept, I tried an AI rewrite of one such legacy app[1], and it is not bug free, but it notably has fewer bugs than the original. Different bugs, sure, and I'll have to iron them out after a round or two of UAT, but I'm honestly more confident with what I got from the chatbot than the code inherited from humans.
[1] Deals with money, but admittedly at a much lower level of risk and consequence than a banking app running on a mainframe.
Because you know that the current one works. If you have a bank running on COBOL (or whatever), you've had that for 30+ years now, so while it might have bugs, you know what they are. You don't know what the LLM output is. Hence back to my original point: writing the code is not the hard bit. Making yourself (and your CEO etc) comfortable to put that into production is one of the hard bits.
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How did you conduct this rewrite? Did you hand the AI some specs, some tests, the existing code?
I feel like AI has dramatically changed how complete rewrites can be considered, especially for long-lived, legacy projects.
> but it's still a singleton instance, so where do you run it? Most hardware doesn't give you enough uptime for what you need here, because what you actually needed was a re-architecture for distribution / failover / whatever, and while you could ask your LLM to do that you aren't going to run your bank on the result.
If only we had a way to solve these issues with tools capable of running Rust programs in that way. I guess every company that needs distribution / failover has a mainframe sitting in their office nowadays huh?
https://k3s.io/
https://kubernetes.io/
https://aws.amazon.com/
https://www.erlang.org/
etc.
You misunderstand.
You could run one of these things on a mainframe, because it's a zero-downtime machine - you can swap out parts of them as they run. But fundamentally it's a singleton. It is deeply naive to believe you can trivially translate that to something running on Kubernetes just "because Rust".
Of course most companies that need distribution do manage to do that, and eventually the banks will get there too. But it isn't feasible to do that by translating their existing non-distributed COBOL code, they need a fundamental re-architecture, and that is much harder.
It's not enough to do a rewrite. Someone has to maintain it. Such a huge codebase with literally zero experts is unmaintainable. There is no one who knows how the internals work.
Sure you could keep vibe coding it but I wouldn't bet my data on that. A database needs to be rock solid.
This seems to be the issue with using LLMs for any code generation. Even with my own code bases that I've written entirely by hand over years, if I use AI to implement anything, I don't go through the mental model of architecting it, so I don't know how it works. I can only imagine this to be far, far worse for large code bases maintained by a team of people who are all using AI.
That depends on the language you are using. Some language communities had already rejected "architecture astronauts" before LLMs were born, so the training data was highly consistent, which has lead to LLMs being highly consistent in their output. You know how it works because the LLM spits out the same as what you would have written yourself. It's almost eerie that they can do that.
Unfortunately that doesn't apply to all languages. LLMs are especially bad at producing code for the languages that were historically known as beginner-friendly as the training data was full of code by beginners doing what beginners do. All bets are off if you get stuck there. (Although maybe you could use an LLM to translate your code to a language that LLMs are good at!)
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> Cue some story here on a bank or airline somewhere still relying on cobol backend servers.
There's existing money and expertise in those environments to rewrite the whole thing, yet they don't. You may loan them free engineers/experts and they might still not rewrite anything.
It's a clean-cut financial decision.
The existing system works. Yes, it costs a lot to maintain, and you could definitely reduce that if you moved to a more modern system. So now you're talking payback periods. Cost of development / maintenance cost savings per year = number of years before you pay back the project.
Problem is, that the cost of the development is often unclear, and the maintenance cost savings, while definitely above zero, and often unclear, and approximated the numbers usually come to a payback period in decades.
And that's without the usual tech caveats; We can't promise there won't be bugs. We can't promise deadlines will be met. We can't promise the project will succeed at all. We can't promise existing functionality will be faithfully reproduced in the new system. The normal risks around any software dev project.
All in all, it looks really expensive and really risky compared to just doing nothing and running the same old system for another five years.
Source: I helped do some of the maths on this for a Y2K project.
OK but, Postgres is not one of those clunky "we have to replace this" systems.
> the biggest blocker on moving to a new programming language, is the cost of re-writing everything
In 2026, not sure if it was satire. Do some people truly believe that all their software stack has to be single tech, from device drivers to end user apps? Does that extend to remotely accessed services?
At the same time that was ever the only reason for moving to a new programming language: abandoning all the bad ideas and craft that had accumulated in the previous language ecosystem. Needing to rewrite everything meant starting from a clean slate, allowing the new systems to be designed for the new age, making everything in that new language feel sleek and modern and thus appealing. Of course, as time progresses even the new language starts to accumulate bad ideas and cruft, historically necessitating yet another language to offer the clean slate again.
If the code is going to be translated forward instead of abandoned and then rewritten, as is now completely viable via LLM, there is no reason to move to a new language at all.