There's been a decent amount of work showing that they should be much more energy efficient. There is some empirical work showing other potential advantages (like robustness) but most of that work is still too nascent to be definitive.

SNN - ANN conversion and surrogate gradient methods can both get good results these days, so training has become a lot more comparable to ANNs than it was in the past. I would agree though that there is a disconnect between the hardware and software still which is preventing SNNs from reaching the dream of super low power models.

There has been a lot of progress in the last 2 - 3 years. They're still not quite at the level of ANNs in general but have been gaining ground quickly and do outperform ANNs on some specific tasks -- usually things with a temporal component but low data dimensionality per time-step. Another area with comparable results to ANNs would be object detection.

Look into spiking neural networks if you're not aware of them already