Unfortunately, room for growth does not guarantee ability for growth. The revolutions we see now have sprung largely from a handful of work that came to fruitio

Unfortunately, room for growth does not guarantee ability for growth. The revolutions we see now have sprung largely from a handful of work that came to fruition ten or so years ago: a new set of tools to approximate solutions to previously intractable problems. It took thirty years for those tools to be developed, with the fortunate confluence of the development of many other technologies alongside, and it does seem like they're already reaching the limit of new things they can "solve". So while there is probably still headroom in application and capitalization, success in 'solving' any given problem in AI does not have any clear correlation to any other problem in the set of things still trivial to humans but inaccessible to artificial machines. There's no convenient hierarchy of complexity like we have for more general computation, no proof by equivalence or operational measurement of difficulty. This space is still a huge mystery, and at any given moment, we have no idea if the path we're on is going to lead anywhere other than a dead end; research of this nature is not monotonic. This pattern is not infrequent in AI.