Pure concurrency has the advantage that you can do away with a lot of complex and nuanced synchronism mechanisms, by virtue of the fact you’re not actually sharing memory between parallel lines of computation. Something that makes writing correct concurrent code quite a bit easier and friendlier. In that world have clear and explicit markers of when a function call might result in you yielding to the event loop, and thus memory values you read previously in your function might change, is very handy. Especially if there’s a nice mechanism to delay that yield until after you’ve completed all your important memory operations, and have confidence that your computed values are consistent.

Coroutines are certainly a different approach to the same problem, so hide the blocking nature of functions in a neat way, but at the cost of requiring you to start using those complex synchronisation primitives, because any function call or operation might result in an implicit yield, and thus you can’t predict when memory values might change.

My first introduction to async/await was the Twisted framework for Python. It wasn’t called async/await back then, the principles were identical. Twisted made it possible to write pretty high performance concurrent network code in python, in a way that was very understandable, and _safe_, without resorting to multi-threading or multi-processing. As a result I think the async/await in Python is actually a really good idea. When used correctly, it makes it possible to write really nice, performant, code in python, without resorting to parallelism, and all the pitfalls that come with that (I.e. synchronisation). Async/await provides a nice middle ground between full on parallelism, and single threaded blocking code with no ability to interleave IO operations.