The "Changes to my workflow" part is most relevant, and would be more accurately titled "How Cursor writes code differently to me [a senior developer]".
For example:
1) Cursor/AI more likely to reinvent the wheel and write code from scratch rather than use support libraries. Good to avoid dependencies I suppose, but widely used specialized libraries are likely to be debugged, and mature - able to handle corner cases gracefully, etc. AI "writes code" by regenerating stuff from it's training data - akin to cut and pasting from Stack Overflow, etc. If you're using this for a throwaway prototype or personal project then maybe you don't care as long as it works most of the time, but for corporate production use this is a liability.
2) AI more likely to generate repetitive code rather than write reusable functions (which he spins as avoiding abstractions) means code that is harder to read, debug and maintain. It's like avoiding global symbolic constants and defining them multiple times throughout your code instead. This wouldn't pass typical human code review. When future you, or a co-worker, maybe using a different editor/IDE, fixes a bug, they may not realize that the same bug has been repeated multiple times throughout the code, rather than fixing it once in a function.
We don't have human level AGI yet, far from it, and the code that today's AI generates reflects that. This isn't code that an experienced developer would write - this is LLM generated code, which means it's either regurgitated as-as from some unknown internet source, or worse yet (and probably more typical?) is a mashup of multiple sources, where the LLM may well have introduced it's own bugs in addition to those present in the original sources.
It will be as cobbled together as the thoughtfulness of the person in charge of the code. Same as if they wrote it themselves.