Do we need be worried about the nature of cyclic phenomenon even in the face of eventual progress?

.                       Gartner Hype cycle

Yes, we need to be concerned about the cyclical nature of phenomena like AI winters, even with eventual progress. While each "AI spring" brings genuine advancements, the cyclical pattern itself is indicative of underlying problems that can have significant consequences.
The AI winter cycle isn't just a harmless lull but a phenomenon that has real-world impacts. From financial and career instability along with wasted efforts of millions of professionals to eventual loss of public trust. 
For researchers, a downturn means funding dries up, projects get canceled, and careers are often derailed. This instability discourages young talent from entering the field, slowing down long-term progress. Periods of inflated hype lead to a lot of wasted effort. Companies and individuals invest heavily in technologies that aren't ready, only to abandon them when they fail to deliver on unrealistic promises. This is inefficient and can lead to a loss of public trust. When AI systems are repeatedly overhyped and then fail, the public becomes skeptical. This makes it harder to garner support for beneficial AI projects, even when the technology is mature. The "crying wolf" effect of successive AI springs makes it more difficult for people to believe in the true potential of the technology.

The Philosophical Problem.
From a philosophical perspective, the cycle reveals a fundamental disconnect between technological capability and human expectation. Each winter is a humbling reminder that technology, no matter how powerful, is a tool. We must address the deeper questions about what we want AI to do and how we can do it responsibly. The cycle will likely continue until we, as a society, move past the binary thinking of "AI as a savior" or "AI as a threat" and instead focus on integrating it ethically and practically into our lives.
The real goal isn't to just keep progressing; it's to break the cycle by building a more stable, honest, and responsible approach to AI development.

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