With AI, you can hypothesise what can potentially break with each new addition (which your regression tests do not even capture at present). Then, you can write tests for each of those hypotheses, ask AI to deploy a canary, ask AI to divert 5% of traffic to the canary. Ask AI to analyse the logs for any signs of regression in performance, ask AI to roll it out to 100% if everything is good. Congrats! At this point, you've become a slave to AI and cannot do without it. Even logging into a remote server now causes mental pain; having to do anything by hand causes pain. You just wait for your limit to be reset to return to slavery again. A master of a slave is as much of a slave to his salve as the slave is to the master itself.

I feel like AI might let us model some of the things that we initially didn't scope that led to these problems (e.g. "Decided not to fix every bug, or upgrade every dependency") - being able to more easily ask a system that can dig into "how much time are we spending on stuff related to foo"

AI tooling can also be a place where we start building our view of what maintainable software practices look like so we don't make decisions that have these same tail effort profiles. That can be things like building out tooling to handle maintenance updates

I think the real thing that comes out of AI tooling is probably that the tooling needs to be trained (or steered) towards activities that enhance human attention management.

In my experience AI reduces maintenance costs. Though, context might matter here, I'm working on a multi decade set of projects, while there is a lot of greenfield feature development, the old code / older projects have suddenly become a lot easier to work with, modernize, and in a bunch of cases, eliminated. Dependency on old libraries, build tools, in some cases updated, in other cases just eliminated, builds are faster, easier for developers, etc. End to end testing has become a lot easier to setup and automate. DevOps have been improved a lot, diagnosing production issues drastically improved, we have a ton of logs and information, and while we have various consolidated dashboards / monitoring to capture critical things, now we can do a lot more analysis on our deployed system (~50 ish projects)

Yeah, but to be honest, I sometimes just tell Claude to cleanup / refactor stuff; it finds a lot of things, discusses it with me and I approve the plan, and it churns away my tokens for some time. I do this once in a while, and I've been doing this for over 6 months and I don't feel like my development has significantly slowed down. Yeah my token usage is more for sure, but my codebase also is, so I'm not worried about that. To me AI seems to make maintenance very easy, like the rest. You just need to do it.

Edit: I make it sound a bit simple maybe. I do more extensive redactors also, where I'm more involved and opinionated. But I don't feel the need to do that very often very deeply. But yeah sometimes it's definitely necessary to prevent the project from going off rails.

Great Article! I think ultimately we are heading towards a world where much better software will be created. This is the major roadblock we need to cross over before that can be true, but I think it is a very tractable problem!

I created a video that talks about this in more detail:

https://www.youtube.com/watch?v=G3Q7Y-nrUbk

Insightful. Agree with this take.

Unfortunately, maintainability is simply bucketed as a "non-functional" requirement.

Maintainability (and similar NFRs) should actually be considered what preserves and enables the delivery of future functional requirements -- in contrast to framing non-functional requirements as simply "how" the software must do what it does vs. the "what"/functional requirements that "actually matter".

From that standpoint, if a steady flow of features/improvements is important for a project, maintainability isn't really a non-functional requirement at all, and amounts to being a functional requirement, in practice, over anything except the shortest of time horizons.

Same with code reviews.

I wonder if AI could make code reviews more presentable.

for example, with human code reviews, developers learn quickly not to visually change code like reflowing code or comments, changing indent (where the tools can't suppress it), moving functions around or removing lines or other spurious changes.

And don't refactor code needlessly.

also, could break reviews up into two reviews - functional changes and cosmetic changes.

This is what I've been preaching to my team. With 5.5 and 4.7 the coding agents are good enough know to almost never take any tech debt. Any new feature or fixes should come with a cleanup or refactor, on the same PR.

My low value comment. This feels directionally correct to me. The problems I've been struggling with in my dev job for the past 6 months have been 80% maintenance/legacy code interfering with new feature development.

Some of our developers are overly aggressive about using AI and I've started going down that path because I need to keep up and actually enjoy the flow of working with AI in my IDE.

I put a lot of work into keeping my area of the codebase understandable and coherent but I do not see that from the others on our team. I'm not perfect but I and extremely sensitive to incoherent, or un-grok-able at a glance.

Anyway, I like the novel (to me at least) framing of this article!

For me, if I can make a kickass testing system that people love so much that they actually build features with it and it’s not an afterthought, then maintenance becomes much easier. It’s often called test driven development but I’ve rarely seen it done in such a way that the dev ex is good enough for it to work.

But say you have that. Then you have great profiling. At that point you can measure correctness and performance. Then implementation becomes less of a focal point. And that makes it a lot easier to concede coding to ai

The maintenance-cost framing is the useful constraint. I’d rather see agents default to smaller diffs, test scaffolding, and explicit assumptions than maximize lines changed per prompt.

https://www.laws-of-software.com/laws/kernighan/ relates here.

The incitives for remote LLMs are off with providing defaults which optimize for maintenable sound architecture though. Same way Claude is going to produce overview of the indexes of the summaries of comprehensive reports, no one is going to read. No doubt this feels like excellent KPI on how much output was generated.

I think AI is great for the soul destroying boring stuff that makes me want to quit my job like wrapping legacy code in test cases. Hey I’ll take on any idiot who’s willing to do that job, even if he’s artificial.

So what are all of these agentic based strategies going to do once the infinite money spigot of investment into AI ends and they need to start charging prices that actually make a profit?

I get that most of the cost is in training and not inference, but I don’t see how models stay useful once the worlds software updates in a few months post training since the models can’t learn without said training.

Are we just going to have shops do the equivalent of old COBOL shops where everything is built to one years standards and the main language/framework is mostly set in stone?