Sciency Words: The Gartner Hype Cycle

Hello, friends!  Welcome back to Sciency Words, a special series here on Planet Pailly where we talk about the definitions and etymologies of science or science related terms.  Today, we’re talking about:

THE GARTNER HYPE CYCLE

In my last blog post, I shared my thoughts about A.I. generated art.  It’s a new technology.  There’s a lot of hype about this new technology right now, and my suspicion is that A.I. art is getting a little more hype than it really deserves.  I feel that way, in part, because of something called the Gartner hype cycle.

Definition of the Gartner hype cycle: The Gartner hype cycle is a curvy line on a graph that purportedly models how the hype for a newly introduced technology changes over time.  First, the hype will go up—way up.  Then the hype will plummet down.  In the final phases of the cycle, hype will go slightly up again, before leveling off.

Etymology of the Gartner hype cycle: The idea that new technologies experience a “hype cycle” was first introduced in 1995 by tech analyst Jackie Fenn.  She worked for a tech consulting firm called Gartner Inc., which continues to use hype cycle charts in presentations about new and emerging technologies.

As Gartner Inc. describes it on their website, the Gartner hype cycle has five distinct phases:

Innovation Trigger: A new technology is introduced.  Hype starts to grow (and grow and grow).

Peak of Inflated Expectations: The hype surrounding this new technology gets blown way out of proportion.  Media reports make it sound like almost all the world’s problems could be solved by this new technology.  Investors on Wall Street start screaming “Buy! Buy! Buy!”

Trough of Disillusionment: The hype bubble bursts.  It becomes clear that this new technology cannot solve all the world’s problems, and those Wall Street people start screaming “Sell! Sell! Sell!” 

Slope of Enlightenment: While the new technology can’t solve all of the world’s problems, it turns out that it can solve some problems.  Interest and investment in the new technology starts to build again, based on more realistic expectations.

Plateau of Productivity: The new technology becomes normalized after finding its proper niche in society.

There are at least three major criticisms of this concept.  First, the word “cycle” is misleading.  It implies that this process is cyclical when it clearly isn’t.  Second, this concept is not good science.  How do you measure something like hype, scientifically speaking?  And third, the Gartner cycle would have you believe that every new technology will eventually find its niche.  There’s no guarantee of that.  Sometimes a new technology simply fails.  It falls into that “trough of disillusionment” and never comes back.

Despite those valid criticisms, I do think the Gartner cycle can be a helpful first approximation of what might (might!) happen with a newly introduced technology.  The cycle may not be good science.  It may not make exact predictions, and it can’t guarantee anything.  But the general idea that the hype for a new technology will go way up, then go way down, and then settle somewhere in the middle… that does seem to happen, more often than not.  There’s enough truthiness to the Gartner cycle that it’s influenced my own thinking about A.I. art, as well as my thinking on topics like cryptocurrency, commercial space flight, self-driving cars, and a bunch of other things.

And the Gartner cycle is something I’m starting to think about in my Sci-Fi writing as well.  What might happen when we invent antigravity technology?  Faster-than-light travel?  Time machines?  Would those technologies experience something like the Gartner hype cycle?  Maybe.  Or maybe not.

Again, there are no guarantees with this one. In my mind, the Gartner cycle is a useful first approximation of what might happen. Nothing more.

WANT TO LEARN MORE?

I first heard about the Gartner cycle in a video by Wendover Productions, which uses drone delivery services as an example of the Gartner hype cycle in action.  Click here to watch.