We already knew in the late80s/early 90s that neural networks were universal function approximators, and there was an era in the late 80s/early 90s where neural nets were VERY successful (or at least: very influential among the ML circles of their day). Sure, they were dismissed once kernel machines came about, simply because those had more to offer at the time. But it would be a mistake to compare HTM with classical neural networks: neural nets were always known to do something sensible and to "work", even if they might not be the state-of-the-art method.
In stark contrast, HTM has been "out there" for over long a decade by now, with (as far as I know) not a single tangible result, neither theoretical nor practical. They never managed to hobble together even a single paper with credible results, even though they came out with it right at the time where connectionist approaches became popular again (yes, there were papers, but there's a reason they only got published in 2nd or 3rd tier venues). From where I stand, it's a "hot air" technology that somehow seems to stay afloat because the person behind it know how to write popular science books. Everyone researcher I know who tried to make HTM work came away with the same conclusion: it just doesn't.