There were (are?) people talking about Artificial General Intelligence being just around the corner, which is a sign of people buying way too much into the hype cycle.
Space flight is another thing like that. We were supposed to have manned flights to the outer solar system by 2000. We're getting reusable first stages that are actually economical to fly and commercial orbital spacecraft in 2020. That was supposed to be 1980s stuff according to 1950s and 1960s space hype.
Putting someone on the moon? 1969. But that was done with a monstrously expensive finicky one-off architecture that could never be anywhere close to economical, and it was dangerous as hell. Routine trips to the moon that could even approach economic sustainability are at least 10X as hard as Apollo.
I strongly suspect safe, sustainable, and affordable nuclear fission power is yet another one. Getting some atoms to smash and boil some water? Easy! Build some power plants? A little harder. Then you hit the edge of that bath tub curve and the problems (waste, safety, fuel cycle, cost management) multiply and it gets brutally hard really fast.
We should learn to recognize engineering problems that have this kind of "bath tub curve" for difficulty vs ones with gentler learning curves.
It's easy to make predictions when outside a field (and I'm guilty of this fallacy too), but easy to miss the true challenges. For example, a lot of people just assume that fusion reactors will be better than fission, because that's what they were told their entire life and because we still haven't figured it out. But looking at energy density or thermal transference and fusion just looks like a really crappy heat source, something that would be incredibly difficult to ever engineer into a product without a secondary invention that is just as difficult, if not more, than the initial idea of fusion.
In general, when there is a breakthrough in a field of research—like what deep learning went through recently—there is a gold rush of new projects that push the boundaries.
A certain category of people look at these new startups, and project that momentum out to some extremely distant problem (like AGI or fully autonomous vehicles), without understanding if this field of research necessarily leads to solutions to those problems, at least in linear fashion.
Then there is another category of person who looks at those predictions and dismisses the entire field as hype, ignoring the surge of valuable projects that kicked it all off.