🔗 Software acceleration and desynchronization

A bit more than a month ago, I posted the following on social media:

Seeing more reports and industry players blaming code reviews for slowing down the quick development done with AI. It's unclear whether anyone's asking if this is just moving the cognitive bottleneck of "understanding what's happening" around. "Add AI to the reviews" seems to be the end goal here.

And I received multiple responses, some that were going "This is a terrible thing" and some going "yeah that's actually not a bad idea." Back then I didn't necessarily have the concepts to clarify these thoughts, but I've since found ways to express the issue in a clearer, more system-centric way. While this post is clearly driven by the discourse around AI (particularly LLMs), it is more of a structural argument about the kind of changes their adoption triggers, and the broader acceleration patterns seen in the industry with other technologies and processes before, and as such, I won’t really mention them anymore here.

The model I’m proposing here is inspired by (or is a dangerously misapplied simplification of) the one presented by Hartmut Rosa’s Social Acceleration,1 bent out of shape to fit my own observations. A pattern I’ll start with is one of loops, or cycles.