it's an interesting talk, my take being:
concurrency _is_ parallelism, but for I/O. People often think of parallelism for the case of making something go faster - eg placing two computations in parallel (the definition posed in the video), OR placing two I/O operations in parallel - so this is the keyboard-vs-mouse in the OS, even when you're on one core only; this is multiple web requests in JavaScript, which does not support multi-threading, but 100% does support concurrency for I/O operations - that... badum-tiss! RUN IN PARALLEL.
I get the point of the talk, and it's well interesting, but I think it depends on how one views things.
No, and that's the point of the article. What you are calling parallel w/r/t IO should be called concurrency (conceptually happening at the same time by virtue of being able to interrupt and resume units of work). The reason IO APIs like you've described is concurrent but not necessqrily parallel is because there is no guarantee in the API that they both happen literally simultaneously; I could build a JS runtime that "works" for all the code written against XMLHTTPRequest (ignoring side-effects) but which under the hood only ever makes one HTTP request at a time. And because I can do that, that means JS code is living in a concurrency-only world, even though as an implementation detail most runtimes support parallel execution of those concurrent operations.
> concurrency _is_ parallelism, but for I/O.
Not really. They're just separate but related concepts.
E.g. coroutines are a form of concurrency that doesn't have to involve any sort of I/O, you're just taking two logical processes (e.g generating a sequence and consuming it) and abstracting away how they execute relative to each other.
Describing your tasks using the language of concurrency is a requirement for process-based parallelism (multiple CPUs/cores), but data-level parallelism (SIMD) is a form of parallelism that doesn't involve concurrency either.