AI Changes the Economics of Software Rewrites
My view on software rewrites has shifted because AI thrives on clear, consistent code patterns found in popular stacks. Proprietary or inconsistent systems force models to spend tokens inferring rules, increasing costs and lowering quality. Rewriting code isn't just about modernizing technology; it is a strategic move to rebuild around patterns that maximize AI leverage, turning potential inefficiency into a competitive advantage.
You could either be using AI to solve the problem, or you could spend the time trying to get AI to learn your language first.
- ghost_pepper
This website has four articles, once daily, three of which being AI crap and doomsaying (the fourth arguably too, it just doesn't say so), all with lines like:
> A fast car doesn't win races — a driver does
> the gap is not just speed - it's output quality
> A rewrite isn't just an opportunity to modernise your technology stack - it's an opportunity [...]
Garbage.
- matsemann
What's the point of the rewrite if it doesn't fix the underlying issues, though?
A rewrite being a good idea often hinges on the ability to simplify. After a decade or more, it's now apparent what the application should and shouldn't do, so one can build it with those learnings and shed all tech debt from how it grew organically.
Aka preserving all behavior is not what I would want from a rewrite. The point would be to make decisions on what behavior should be kept and what complexity can be removed. An AI can't do that. It can help with execution if the decisions are made, but they're made by being very intimate with the codebase and floating all cases and then talking with stakeholders.
- nottorp
Does it really change the whys of rewriting?
https://www.joelonsoftware.com/2000/04/06/things-you-should-...
Maybe the LLM will catch and reproduce all corner cases... maybe not...
- devin
I don't think it does. (meaningfully change the economics of rewrites)
Burning a sea of tokens to arrive at the equivalent functionality and having a small team of people oversee that process is rarely going to be the fix to the organizational problems that surround typical failed/stagnant software projects.
Rewrites are rarely about the organization of the symbols and are more often about a change in the fundamental understanding of the organization about the problem they've solving. Remember: People change slowly.
People are often too tied to the idea of "rewrite" as a replay of all current capabilities, but should instead be thinking about fundamentally different primitive capabilities of the system. It's not a "redo" if you're changing some of your fundamental assumptions about the problem space.
- light_hue_1
This kind of data-free opining reminds me of the Mythical Man-Month. Yeah, in theory adding more people to a project will speed it up. And all people are replaceable so I can hire 100 bodies for cheap and we'll be done with this project ASAP.
Sounds great! Have you tried this? Did you see what went wrong? Otherwise this is just the same nonsense as always.
- akssri
Au contraire - LLMs are quite bad at large scale pattern fidelity. They'll even forget key details and constraints unless told over and over again. That's why AI-written code has the quality of a patch-on-patch-on-patch.
- jdw64
The point where I truly feel that AI is a game changer is that these kinds of posts keep appearing. Tautological outcries keep going on both sides, pro and con, endlessly repeating circular logic. There's no real substance or evidence, and rather than discussing how things were actually applied, it's just an echo chamber for whatever group you belong to.
In that sense, my homepage (https://www.makonea.com/en-US) doesn't even make it to the HN front page—it's mostly in SHOWDEAD. Does that mean it has less value than this post? I'm feeling a sense of doubt about myself.
- SunlightEdge
In my experience, LLM's can be both impressive and also totally wrong in their reasoning when doing a code re-write. I was involved in an api migration a while back and while at times the llms were able to re-write the code - they also had instances where their totally misunderstood the platform and their recommendations for solving the issue was almost dangerously wrong. an over reliance on them can also make people lazy at what are quite simple programming issues (but they can code things up a hell of a lot faster) - its a tool and the outputs need to be carefully reviewed (with a dose of critique when its an uncertain area).