An AI that reaches a certain point in its own development becomes able to improve itself. At this point, recursive cascades slam over many internal growth curves to near the limits of their current hardware, and the AI undergoes a vast increase in capability.

This seems like the first problem I detected. An intelligence being able to improve itself does not necessarily lead to a recursive cascade of self-improvement - since it may only be able to improve some parts of itself - and it's quite possible that after it has done those improvements, it can't do any more.

Say that machine intelligence learns how to optimise FOR loops, eliminatining unnecessary conditions, etc. Presto, it can optimise its entire codebase - and thus improve itself. However, that doesn't lead to a self-improving recursive cascade - because it only improved itself in one way, and that was a rather limited way. Of course this kind of improvement has been going on for decades - via lint tools and automatic refactoring.

As machines get smarter, they will gradually become able to improve more and more of themselves. Yes, eventually machines will be able to cut humans out of the loop - but before that there will have been much automated improvement of machines by machines - and after that there may still be human code reviews.

This is not the first time I have made this point here. It does not seem especially hard to understand to me - but yet the conversation sails gaily onwards, with no coherent criticism, and no sign of people updating their views: it feels like talking to a wall.