hype-ciklus

in English: hype cycle

basicwidespread

The hype cycle is a curve showing how a new technology moves from exaggerated enthusiasm through disillusionment to sensible, everyday use.

In plain terms

The hype cycle describes how expectations for a new technology change. First, everyone expects miracles, then comes disappointment, and finally, it becomes clear what it is actually good for.

It is like a New Year's resolution and the gym. In January, the gym is full, and everyone hopes for transformation. By February, most drop off in disappointment. Those who remain find a realistic, sustainable rhythm by spring and truly achieve results.

The model is useful when a much-hyped novelty appears: it lets you ask where we are on the curve right now. When reading news about AI and agents, it helps separate promise from proven benefit.

The hype cycle does not measure the technology's actual development but the expectations surrounding it. The technology may meanwhile improve quietly and steadily, even as public sentiment fluctuates.

How it works

Innovation trigger (formerly technology trigger): a breakthrough or impressive demonstration appears, the media picks it up, but usable products are still scarce.
The peak of inflated expectations: success stories, large investments, and bold predictions follow one another, with little said about failures.
The valley of disillusionment: experimental projects fail to deliver promised results, some attention and money evaporate, and weaker players disappear.
The enlightenment slope: persistent companies learn where the technology works and where it does not, and more mature second- and third-generation solutions emerge.
The productivity plateau: the technology becomes a familiar tool, its benefits are measurable, and no one speaks of it as a sensation anymore.

An everyday example

At your workplace, management announces after a conference that AI agents will take over customer service within six months. A year later, the project quietly shrinks to the two or three tasks where the agent actually works reliably. This is the path from the peak, through the valley, to sensible use in miniature.

Who is affected

entrepreneur, executiveIt helps decide when to invest in a new technology and when you are paying only for the trend.

investor, saverAt the peak of expectations, company valuations often detach from real performance, making the risk of a bubble greatest there.

employeeYou can more realistically assess how seriously to take predictions like "AI will take everything tomorrow". → Work & HR

everyoneIt provides a basis to distinguish promise from proven results in technology news.

A common misconception

MythIf a technology falls into the valley of disillusionment, it has failed and is not worth pursuing.

In realityThe valley mostly signifies the bursting of exaggerated expectations, not the failure of the technology. Many solutions now considered fundamental, such as e-commerce, became truly useful only after the disappointment.

Limits and risks

The hype cycle is not a natural law but a vivid rule of thumb: it is based on expert estimates, not precise measurements.
Not all technologies complete the journey: some disappear permanently in the valley, while others spread with almost no disappointment. Retrospective studies show that only a portion of technologies follow the curve.
The timing is not specified: a phase can last a few months or even a decade.
It is easy to abuse: any floundering project can be said to be "only in the valley," even though it might simply be a bad idea.

Similar, but not the same

tech bubble – A bubble refers to overpricing in financial markets; the hype cycle covers the full arc of expectations, of which the bubble represents at most the peak area.
AI winter – An AI winter refers to a multi-year shortage of funding and interest affecting the entire field of artificial intelligence research, whereas a hype cycle describes the trajectory of a specific technology.
technology adoption curve – The adoption curve shows how many people actually use an innovation, while the hype cycle shows the magnitude of expectations surrounding it.

Related concepts

Broader

technological forecast, innovation

Narrower

peak of inflated expectations, trough of disillusionment, productivity plateau

On the edge

AI bubble, AI winter, Amara's Law, technology adoption curve, AI-washing

A brief history

The model was introduced by the technology research firm Gartner in 1995; its development is attributed to analyst Jackie Fenn. Since then, the company has published hype cycle charts annually for various technology areas, including artificial intelligence.

Where things stand

The hype cycle is a standard reference point in business and technology journalism. It is still often cited in AI debates in late 2026: many speak of disillusionment with generative AI, while AI agents are surrounded by heightened expectations.

Status: 2026 November

In the news

Appears in 1 Agent Age stories; the curve shows weekly occurrence in the news magazine.

Gartner: AI spend in digital marketing is a governance issue, not a tooling raceAug 23, 2026 · Marketing

Also known as

hype-ciklus · hype ciklus · hype cycle · Gartner hype-ciklus · Gartner Hype Cycle · hype curve · hype cycle · hype curve

Sources

Wikipedia: Gartner hype cycle

This entry was machine-edited and has undergone machine expert review; the “In the news” section updates automatically from Agent Age stories.