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In my experience the facilities to throw tasks into a scheduler that will run them in parallel was never the hard thing to accomplish (regardless of the language: some may have built-in capabilities, others may have syntactic sugar provided by a lib, but at the end of the day, most systems provide some kind of runTask(f) method).

What's really hard is to break down a problem into parallelizable chunks, figure out as much independent work as possible to reduce the touchpoints, and coordinate all those tasks such that they keep the CPU as busy as possible and as a whole finish as early as possible.

Beyond this "parallel breakdown design", it's the little touchpoints with shared data structures and synchronization that creates the difficulty of implementation, and I haven't seen any language or system that does magic there.

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throw tasks into a scheduler that will run them in parallel was never the hard thing to accomplish

Common mistake number 1. Goroutines are running in parallel AND concurrently - they are coroutines (common mistake number 2 is to think they are only coroutines). I suggest not to underestimate that, it's the big deal. Additionnaly, it's important to note that goroutines yields on sleeps (any kind of sleeps / waits, like disk reads, network requests, channel writes/reads, etc), and while it may sound like a detail, it's a wonder of cpu control. Due to that, there is also a rare elegance to the way Go solve sharing data via channels.

That's exactly what Go is a wonder for due to the combination of parallelism, concurrency, yield-on-sleep and channels.