Yes, that's exactly what the article is about: how we can model "plain for-loop" levels of performance in the presence of complex (intricate state at multiple levels and high potential for nesting iterators) code that is supplying the loop(s)
Then you lose the separation of iteration from the looping construct.
You can use a function or a method, but you lose the nice continuation/generator ability to write a function that can do complicated yields without having to write the state machine yourself, plus you run a risk that the call won't inline in which case you're incurring non-trivial function call overhead.
The problem with iteration in Go isn't that you can't solve any given individual problem, the problem is that you can't solve all of them simultaneously the way you can in Rust or Python. (Though one of these days I want to get around to benchmarking Python's iteration versus Go channel-based iteration, I'm not actually sure which would win. What Go considers dangerously slow can still be baseline performance for other languages.) So you can get a defeat-in-detail sort of thing where a person cites a problem, and someone posts as solution to that, and then they cite another problem, and there's a solution for that, and then there's another problem, and a solution is posted for that, and all the solutions do indeed more-or-less solve the given problem... but you can't combine them into one.
Since all recursive programs can be converted into iterative programs then you can "simply" (not always simple) convert recursive solutions like McCarthy's Lisp solution to a loop: https://dl.acm.org/action/showFmPdf?doi=10.1145%2F1045283 (page 5)
