I think it's also worth mentioning that for certain specific use cases coroutines are much more efficient than a full goroutine, because switching to/from a coroutine doesn't require context switching or rescheduling anything. If you have two cooperating tasks that are logically synchronous anyway (e.g. an iterator) it's much more efficient to just run everything on the same CPU because the kernel doesn't have to reschedule anything or power down/wake up CPU cores, and the data is in the right CPU caches, so you'll get better cache latency and hit rates. With goroutines this
may happen anyway, but it's not guaranteed and at the minimum you have the overhead of going through the Go runtime goroutine scheduler which is fast, but not as fast as just executing a different code context in the same goroutine. Coroutines offer more predictable scheduling behavior because you know that task A is switching directly to task B, whereas otherwise the goroutine scheduler could switch to another available task that wants to run. The last section of the blog post goes into this, where Russ shows that an optimized runtime implementation of coroutines is 10x faster than emulating them with goroutines.
Google has internal kernel patches that implement cooperating multithreading this way (internally they're called fibers), and the patches exist for precisely this reason: better latency and more predictable scheduling behavior. Paul Turner gave a presentation about this at LPC ~10 years ago that explains more about the motivation for the patches and why they improve efficiency: https://www.youtube.com/watch?v=KXuZi9aeGTw