AI changes the economics of software rewrites

What do your tests look like. Because rewriting by hand and rewriting via AI have the same load bearing on whether or not your tests cover your scenarios and your integrations well.

The LLM might change the economy of this, but I doubt it.

I tend to believe that the engineering culture you describe will end up producing similar or, as Joel postulates, an even worse result, just dressed up in a modern stack.

If the technical leadership remains the very same one that enabled such a culture, I don't see them being able to suddenly produce a genuinely better software product only because an LLM is in a picture - especially considering how easy it is to convince an LLM that your idea is the best one.

I think the important lesson is to use clear eyes to evaluate what the rewrite buys you. I was on a team that rewrote a native code app in C#. We also had access to early cloud tech in the Azure stack, what is called queue now and then was called service bus.

These two technologies combined greatly simplified this specific product making it far easier to maintain. Performance on these services was not important so native code was carrying a lot of penalties without the benefits.

Having a well documented messenger like service bus with great SLAs removed several tools we had needed in the old implementation.

We were able to leverage the tests form the original product to define success and tmthus were able to solve a lot of the edge cases in the new code w before we even shipped.

However, the old code was perfectly fine code. If new technologies had not provided significant simplification of the service architecture, a rewrite would've been foolish. And without the very good previously existing tests, we would've run into a lot of issues as we released.

I'm not sure there is any value in knowing shit about AI. I know quite a lot about enterprise organisation level AI, but really, you could just ask an AI and it'd guide you through the processes. Knowledge in general is going to become real cheap in the age of AI. I've been a data archtiect in the past, so I used Opus 4.8 as I would've used a consultant agency on how to do our data architecture for multiple standard systems which can't directly share data with eachother. After a couple of hours with it as a sparring partner, I had some pretty awesome powerpoint decision making slides, one for c-levels and one for it-management.

Since our owners also own an IT consultant agency, I ran the same process through with one of our regular consultants who is an actual awesome data architect. The output was strikingly similar, well except that I/we didn't need to make the slides. I then had him run over the actual slides, and all we changed was adding a { between some arrows to make the source of the arrows more clear.

We're still going to use real human consultants in the loop because they are readily and freely available, and because this is still new. I doubt we'd want to spend 100 consultant hours on something like this in 5 years though. I mean, we'd still do it for decisions where we'd want someone to blame.

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.