We most definitely should, especially so if you're working in a team or organization bigger than a handful of people. Because it's almost certain that you may need to change or interact with that code very soon in the lifetime of the project. When that happens you want to make sure the code aligns with your own mental model of how things work.

The industry has institutionalized this by making code reviews a very standard best practice. People think of code reviews mainly as a mechanism to reduce bugs, but turns out the biggest benefits (born out by studies) actually are better context-sharing amongst the team, mentoring junior engineers, and onboarding of new team-mates. It ensures that everyone has the same mental model of the system despite working on different parts of it (c.f. the story of the blind men and the elephant.) This results in better ownership and fewer defects per line of code.

Note, this also doesn't mean everybody reviews each and every PR. But any non-trivial PR should be reviewed by team-mates with appropriate context.

AI is not my coworker, with different tasks and responsibilities.

The comparison is oniy reasonable if most of your job is spent trying to understand their code, and make sure it did what you wanted. And with them standing next to you, ready to answer questons, explain anything I don't understand and pull in any external, relevant parts of the codebase.

Of course not that's a bit disingenuous. I would hope my colleagues write code that is comprehensible so it's maintainable. I think that if the code is so complex and inscrutable that only the author can understand it then it's not good code. AI doesn't create or solve this problem.

I do think when AI writes comprehensible code you can spend as much time as necessary asking questions to better understand it. You can ask about tradeoffs and alternatives without offending anybody and actually get to a better place in your own understanding than would be possible alone.