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What is the Gartner hype cycle in AI?

Is it worth getting excited about?

Reviewed By: Kevin Pocock

Last Updated on December 5, 2023
What is the Gartner hype cycle?
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The Gartner hype cycle is an often-touted concept by haters in the process of killing your vibe. That said, it’s something sensible to be wary of when investing your time (or money) into a new technology. What is the Gartner hype cycle, and why should you care about it?

What is the Gartner hype cycle?

The Gartner hype cycle is a visual representation of the excitement you can expect from the public, regarding any new technology as it matures. It it divided into 5 stages, namely:

  • The trigger of innovation
  • The peak of inflated expectations
  • The trough of disillusionment
  • The slope of enlightenment
  • The plateau of productivity

This graphic representation of the maturity of a technology is known as the Gartner hype cycle methodology, after American technological research and consulting firm Gartner, which created it.

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The trigger of innovation

In this initial stage, there’s a problem that needs solving. Either that, or a stubborn start-up founder with a solution that needs a problem, which never goes well.

This includes the research and development (R&D) of a company creating a solution. They secure funding on the promise of imminent public interest – the next stage – and produce their first product or service. This will typically be expensive per customer, as they do not yet have the economy of scale (that is, producing 100 of something is much cheaper per unit than producing one of something).

The peak of inflated expectations

This is when early adopters flood in. They see the potential – but only because they’re looking out for this kind of thing. Certain people have careers, or otherwise vested interest, in a field that allows them to hear about things before the majority of people. These people, personally excited about something, and about the social currency of being able to tell a friend a valuable secret, tell their friends their valuable secrets. Those friends work at mass media corporations, and suddenly the reporters are excited based on the excitement of the early adopter who told them.

Then the whole world finds out.

The trough of disillusionment

With public attention comes public scrutiny. If we could imagine the most perfect, simple, user-friendly software program possible, it would still encounter a human that requires tech support.

With negative experiences comes negative reviews, with negative reviews comes negative press. Suddenly the investors are uneasy, and founders lose their next round of funding. Now they can’t pay for what they thought they could, and if they were being propped up by loans which default, it only takes one mistake to go insolvent.

Some companies don’t make it. The industry turns sour.

The slope of enlightenment

In any system, there are outliers. Some companies do make it. These lucky, well-managed, few release their second-generation solution. It’s better than the last one for a list of impressive reasons long enough to host a keynote about. Now the founder is on TEDx roleplaying as Steve Jobs, but they have a few solid points and it wins people over.

This is because the survivors can learn from the mistakes of every company that didn’t make it past the trough of disillusionment. It can be near impossible to do so, because you don’t know when you’re in it – hindsight is 20/20, as they say.

As methodologies, manufacturing processes, and social verbiage matures, so to do the remaining companies with the human resources to continue supplying the demand. Only now, they have little to no competition…

The plateau of productivity

By this point, it’s estimated that between 20 – 30% of your target audience is on-board in one way or another. They use it, they’ve bought it, they tell their friends and family about it. Not as fervently as the early adopters, of course, because it’s not as exciting now – which also means the investment market is far less volatile, with public opinion changing less rapidly; It plateaus.

How to use the Gartner hype cycle

It is unwise to invest in an emerging technology purely based on hype. PC Guide has never offered investment advice, and never will, therefore what follows does not constitute investment advice – common wisdom is that you should only invest what you can afford to lose.

For an organisation, investing in new technologies includes more than capital allocation. Switching from one software to another as part of your core operational tech stack is a bold move, especially if that software is offered by a start-up that may disappear that same year, leaving you with no support and a broken pipeline.

You can use the Gartner hype cycle to draw insights into the future, bolstering your understanding about your operations and investments. We can predict mainstream adoption following positive reviews and enthusiasm from analysts. Today, that process is accelerated 100x by social media. A product launch or video demonstrations online can cause a breakthrough in the adoption of technologies, or vendors rushing to feed new demand. Technology maturity follows funding, which follows a product or service solving real business problems.

Is the Gartner hype cycle accurate?

Technology innovation is described by this visual diagram. In this curve, we can see generative AI. OpenAI’s ChatGPT has followed this curve, which inspired tech giants Google and Microsoft, in to some degree Meta, to follow suit with Bard, Bing Chat, and LLaMa 2. Large Language Models (LLM) are this years latest innovation trigger. The future potential of such a technology trigger, that in this case can accelerate itself towards AGI (artificial general intelligence), generated new opportunities in cloud computing, big data, communications, and media.

That said, the methodology has drawn criticism for its accuracy, or lack thereof in some cases. This is no strict science or statements of fact, and arguments to the contrary are difficult considering the use of subjective terminology in the guide itself. The research organisation make no claims that you should bet your house on the model, but the curves can be serviceable by technology users and investors with sensible evaluation.

Steve is an AI Content Writer for PC Guide, writing about all things artificial intelligence. He currently leads the AI reviews on the website.