Knowledge bases should work in principle. There are many issues with filling them manually: a) the schema/ontology/conceptual framework is not guaranteed to be useful especially when done with no specific application in mind b) high cost of adding each fact with little marginal benefit etc. But I don't think it outweighs the issues of "pure" machine learning that much: poor introspection, capricious predictability of what you will get, and if you want to have really structured and semi-reliable information you will probably have to rely, at some point, on something like Wikipedia meta-information (DBpedia). Which is really a knowledge base with its own issues.

I think what really stopped Cyc from getting a wider traction is its closed nature[0]. People do use Princeton WordNet, which you can get for free, even though it's a mess in many aspects. The issue and mentality here is similar to commercial Common Lisp implementations, and the underlying culture is similar (oldschool 80s AI). These projects were shaped with a mindset that major progress in computing will happen with huge government grants and plans[1]. However you interpret the last 30 years, it was not exactly true. It's possible that all these companies earn money for their owners, but they have no industry-wide impact.

I was half-tempted once or twice to use something like Cyc in some project, but it would probably be too much organizational hassle. Especially if it turned out to be something commercial I wouldn't want to be dependent on someone's licensing and financial whims, especially if it can be avoided.

[0] There was OpenCyc for a time, but it was scrapped.

[1] Compare https://news.ycombinator.com/item?id=20569098