The AI summer/winter cycle feels to me like something similar to a search algorithm, we have a phase of exploration, which seem to have slower, if any, progress

The AI summer/winter cycle feels to me like something similar to a search algorithm, we have a phase of exploration, which seem to have slower, if any, progress and no one is very sure what's the next big thing so they perhaps start trying many things (the winter "skepticism") and eventually someone finds some breakthrough and gets everyone to an exploitation phase in which everyone knows where to invest and comparatively small effort is required to create progress (the summer "hype"). And eventually all low-hanging fruits are over and the search seems to converge to a local maximum and again larger exploration is required. So maybe the winter is just as important as the summer. Each winter lead to a summer with different focus points (specialist systems and logic followed by neural networks, bayesian models and SVMs and finally deep learning). And after each cycle we have more and more tools, each more useful than the last. And also maybe the key to avoid this strict cycle would be to encourage more exploration during the exploitation phase, giving full support to both incremental ideals that improve on the state of the art and (potentially) revolutionary ideas that give poor immediate results but create new venues to investigate. Of course that's a simplification and there are many aspects to it, including data availability, hardware and tooling that can easily prevent brilliant ideas that were had too soon.