Hype Cycle
The hype cycle describes a recurring pattern: a new technology is first grossly overestimated, then it disappoints, and only later does it find its realistic place. The model comes from the market research company Gartner and is often used to put investments and headlines into perspective.
New technologies are rarely taken seriously in a steady, even way. At the start, everyone talks about them, and expectations rise very quickly. Then it turns out that the technology is far from capable of everything that was promised. Enthusiasm tips over into disappointment, often just as exaggerated as the earlier excitement. Only after that does the technology establish itself in smaller, realistic applications. This up and down is called the hype cycle. It is an observation about the behavior of people and markets, not a law of nature.
Why investors and editorial teams keep the curve in mind
The hype cycle is above all a tool against two typical mistakes. The first mistake is to consider a technology finished at the peak of enthusiasm. The second mistake is to declare it a failure during the phase of disappointment. Both happen regularly, and both cost money.
A classic example is the internet around the year 2000. At the time, the stock prices of internet companies rose enormously, then the market crashed. Many companies disappeared, and the technology was briefly considered overrated. Nevertheless, the internet has since transformed almost every area of life. The collapse concerned the expectations, not the technology itself.
This is especially useful for news about artificial intelligence. When a company claims its product will replace an entire industry, it is worth asking about its current customers. The hype cycle provides a simple rule of thumb for this: the louder the promise, the more closely one should look at concrete results.
The five phases of the curve
The model divides the course into five sections. At the beginning there is a technology trigger, such as an invention or a first public demonstration. This is followed by the peak of inflated expectations. In this phase there are many reports, many company foundings, and few solid results.
Afterwards, the curve falls into what is called the trough of disillusionment. Projects fail, investors withdraw, the topic disappears from the headlines. Those who keep developing the technology now usually work without a large audience. After that, the curve slowly rises again, in the so-called slope of enlightenment. Here, companies for the first time understand exactly what the technology is good for and what it is not.
At the end lies the plateau of productivity. The technology has then become normal and is simply used. One caveat is important: the curve shows attention, not capability. It also does not predict how long a phase will last. Some technologies take twenty years to reach the plateau, others never reach it and vanish in the trough.
The hype cycle in current AI debates
Today, the model appears above all in discussions about artificial intelligence. Language models like ChatGPT triggered a wave of enormous expectations starting in 2022. At the same time, critics argue that many companies have so far made hardly any profit from AI. Both camps use the same curve, just at different points.
Similar trajectories are known from cryptocurrencies, the metaverse, or the self-driving car. For autonomous vehicles, series production was announced around 2018 for just a few years later. Today, robotaxis operate in a handful of cities, but not everywhere. This is typical of the later part of the curve: smaller than promised, but real.
A common mistake is to treat the hype cycle as a forecasting tool. It does not say when a particular technology will leave the trough. Placing individual technologies on the curve is also an assessment, not a measurement. It is useful as an aid to thinking: it separates the question of how much is being talked about from the question of what actually works.