Deconstructing Gartner's "hype cycle" myth

Using this sunday afternoon to work on a book chapter, I was brought back to this peculiar tool created by Gartner called the "Hype Cycle"... defined in Julian's comments the other day on the near future laboratory blog as:

"The Gartner Hype Curve, where whatever the future is, it is sure to be oversold and overpromised, leading to the *trough of disillusionment and despair, after which the future sort of becomes more reasonable than the hype and slowly productizes itself. ((I’m still waiting for the Jet Pack future.))"

The underlying point of this cycle is that products/technologies have a peak of inflated expectations and it’s only after a period of disappointment that "they are adopted by people". Although the idea is promising a first, there are various problems with the cycle itself. The first one is that it doesn't look like a cycle at all, it's as if products/technologies only go through one disillusionment phase before becoming a success... which is utterly wrong. Some products fail several times, some never succeed... and what's a success anyway? We see it's about "visibility" but what does it mean more seriously? A second general problem is of course the idea that progress can't be stopped and that every single piece of tech will find its way to what other people call "the market". I've collected other problems below:

In addition, Richard Veryard has interesting points:

"Clue Number One: All technologies appear to have the same eventual outcome.

Clue Number Two: All the points are perfectly on the line. To a scientific mind, this indicates that the coordinates are not based on any real objective measurement, and that the curve itself is not subject to scientific investigation or calibration. The curve itself is based on a standard engineering pattern.

Clue Number Three: The shape of the line has not altered (or accelerated) in ten years. But all the evidence points to a shifting (shrinking) curve. For one thing, technology studies suggest that the half-life of new technologies is getting shorter. (This is sometimes known as the Red Queen Effect.) Furthermore, we might expect the quantity of attention received by each technology to be affected by the number of technologies competing for attention - and since this is increasing, the quantity and/or duration of hype might be reduced - in other words the hype curve getting steeper."

Finally, Jorge Aranda add important elements to the discussion:

" found the curve fallacious and untrustworthy for two reasons:

Irrational optimism: The curve tells you that, no matter how wacky your technology is, and how unachievable its goals, after it fails to live up to its hype things are gonna get better, always! You’ll see the light at the end of the bad-press tunnel. I find this happy ending scenario very implausible, partly because some proposed technologies do simply crash without recovering, and partly because forecasters have mistaken their job for that of cheerleaders in the past.

Disappearing acts: If you compare the curve from 2005 (below, click for better view) with the most recent one from 2006, you’ll see a number of technologies that have simply fallen out of the radar. SOA is gone. Videoconferencing is gone. Podcasting is gone. Are they past the plateau? Are they not worth a mention?"

Why do I blog this? Deconstructing other's thinking tools is always curious. That said, it might be that the Hype Cycle should not be taken too seriously and that it's just an alibi to start a discussion about the maturation of certain technological products.

There are of course other kinds of diagrams (with their own problems), see my previous post about s-curves.