Generative AI is About to Reach the Trough of Disillusionment, Will It Survive?

It seems like generative AI is breaking new records each and every day, particularly when ChatGPT managed to gain 100 million users in just two months. In spite of the fact that this is the case, generative AI might be approaching one of the most treacherous sections of the Hype Cycle. This cycle consists of five stages, namely the Innovation Trigger, Peak of Inflated Expectations, Trough of Disillusionment, Slope of Enlightenment and the Plateau of Productivity.

With all of that having been said and now out of the way, it is important to note that generative AI is in the second of these stages. This is when the hype cycle is at its highest level, and countless people can’t stop talking about the technology in question. However, after the peak of inflated expectations has run its course, the trough of disillusionment comes shortly after, and this leads to a plummeting popularity for the technology.

Right now, people have seemingly unlimited expectations regarding what AI can accomplish. Once they start to learn about its limitations, they will start to feel disillusioned which will result in it becoming less widespread than might have been the case otherwise. The culling of people that are just hopping onboard the bandwagon could potentially help the tech get leaner and more efficient, after which people will start to adopt it again until it reaches the sustainable plateau of productivity.

The question that everyone is asking is whether or not AI will be able to survive the so called trough of disillusionment. It can be a death knell for tech because of the fact that this is the sort of thing that could potentially end up forcing investors to pull funding and leaving companies high and dry. Generative AI might reach the trough of disillusionment sooner than we might think, although it bears mentioning that this is not always the way things work out. Generative AI might be an exception to the rule, although it will still face challenges ahead as practical limitations get in their way and become ever more complicated in the future.


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