

Highly recommend having some scripting/interpreted language in your stack – in fact you likely already do (consider how shell scripting makes up a significant part of Dockerfiles)
It’s an incredibly useful intermediate between freeform-but-non-executable text/docs/wikis and “industrial-grade”-but-inflexible tooling
In other words, a great fit for capturing this partial/incomplete/tribal knowledge space the post is talking about. I personally even go a bit further and actively advocate for converting “onboarding/operational docs” from wikis into scripts that print out the equivalent text that can be committed and incrementally automated.
I think there is a difference. Because software is so flexible and quick to build, it’s orders of magnitude easier to build something known and understood.
A promising startup with its systems in a knot, but their initial team is still on retainer? Brains can be picked, abstraction boundaries placed, surgical rewrites deployed. Despite the mess, they still understand it, and development can expand.
It remains to be seen if AI-generated code is recoverable, if any existing strategies can be applied so humans can contribute, or if the company is forever beholden to AI providers to release a better AI to manage/improve what they’ve already got.