This entire article is about the fact that the codebase deteriorates over time when you work this way. If they are correct, it will take longer and longer to develop PRs, and more and more cycles to QA them. Eventually the code will be unsalvageable and it might get there before anyone notices a problem. I don’t know if they are right but I know codebases can get there with humans at the helm - and the humans can adapt and learn.
This is not a certain fate but it sure looks likely and is a serious risk. Some software is so low stakes that you can get away with it I’m sure - there are always trade offs.
The bet is that models get better faster than code gets worse. Looks like so far it's correct, as Fable can already write better code than me and most others on average.
the thesis of the post is that this is not true. fable can solve more problems but for complex systems it is not sufficiently diligent that you can turn the lights off.
They turned the lights off in 2025. The post is simply not accurate to today, they even say so, stating that apparently no one has proof of better models in 2026 improving codebases over time. I've seen it happen but the author hasn't apparently, so it's a game of "you said I said", not anything concrete.
(op here btw) - the question isn't "can models make code better" - its "left fully unattended, will they turn your codebase to slop over time"
from the footnotes (sorry if this got a little buried)
> yes of course you can get gpt-5.5 xhigh to do BRILLIANT refactors. But you had to tell it to do that. And to tell it to do that you had to understand your codebase well enough to know it needed doing. We're here talking about why lights-off wont work
i suppose this would also be a good place to ask if you fall in the "high stakes production systems" camp
> If you love vibe coding, please, go on vibing. I still vibe code lots of things, I just also maintain lots of production software (and through HumanLayer, help 1000s of other engineers do the same), so the rest of this is aimed at folks solving hard problems in complex codebases.
GPT 5.6 Sol and Fable are similar step changes, not 5.5, but that's beside the point. If in the Markdown instruction files you ask them to periodically clean up the code or refactor where necessary then that's what they'll do. They don't explicitly do it because otherwise people, as they had for previous models, complain where models are asked to change one thing then they rewrite whole systems, so now the latest models are explicitly trained on instruction following.
In other words you can make the AI act like a software engineer complete with fixing and improving code autonomously (Cursor gave a glimpse of this [0]). It is not a failure of the model to do this automatically but a failure of the human having the wrong instructions.
[0] https://news.ycombinator.com/item?id=48982535